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Biological target

A biological target is a specific biomolecule, such as a protein, nucleic acid, or cellular component, within a living organism to which a drug or endogenous ligand binds to modulate physiological function and produce a therapeutic effect.^[1]^[2] These targets are central to pharmacology, as their interaction with therapeutic agents determines efficacy, selectivity, and potential side effects in treating diseases.^[3] Common examples include enzymes like kinases inhibited in cancer therapies, receptors such as G-protein coupled receptors targeted for cardiovascular conditions, and ion channels modulated for neurological disorders.^[3]^[4] In drug discovery, biological targets are identified through methods combining phenotypic screening, genetic analysis, and computational modeling to pinpoint molecules causally linked to disease pathology, ensuring "druggability"—the capacity for safe and effective modulation by small molecules or biologics.^[4]^[5] Validation involves confirming the target's role via experimental perturbation, such as knockout studies or human genetic associations, to prioritize candidates with high translational potential from preclinical models to clinical outcomes.^[6]^[7] Challenges arise from off-target binding, which can lead to toxicity, and the complexity of polygenic diseases where single targets may yield limited efficacy, prompting shifts toward network pharmacology approaches.^[2] Despite these hurdles, successful targeting has driven breakthroughs, including protease inhibitors for HIV and monoclonal antibodies against cytokines in autoimmune diseases.^[8]

Definition and Historical Context

Definition and Scope

A biological target is a specific biomolecule or molecular complex within a living organism that interacts with a therapeutic agent, such as a drug or endogenous ligand, to modulate physiological or pathological processes.^[2] These targets are typically proteins, including enzymes, receptors, ion channels, and transporters, though nucleic acids and other biopolymers may also serve this role in certain contexts.^[3] The interaction occurs through binding or other mechanisms that alter the target's function, enabling selective intervention in disease mechanisms.^[5] The scope of biological targets extends primarily to pharmacology and therapeutics, where they form the foundation of target-based drug discovery by linking molecular modulation to clinical outcomes.^[9] Targets are evaluated for their relevance to disease etiology, expression patterns in affected tissues, and potential for druggability, encompassing both validated targets with proven clinical utility and emerging candidates identified via genomic, proteomic, or phenotypic screening.^[6] Common classes include G protein-coupled receptors, which account for approximately 35-50% of approved drugs; enzymes such as kinases and proteases; voltage-gated ion channels involved in neuronal signaling; and transporters regulating solute movement across membranes.^[10] This scope excludes non-specific or off-target interactions, focusing instead on entities with causal roles in pathology to minimize adverse effects.^[11] In broader biological research, the concept applies to any entity amenable to perturbation for studying causal relationships, but in therapeutics, emphasis lies on human or pathogen-derived targets with high specificity and low toxicity profiles, as evidenced by databases cataloging over 2,000 unique targets associated with FDA-approved drugs as of 2022.^[11] Advances in CRISPR-based validation and structural biology have expanded this scope to include previously undruggable targets like protein-protein interactions, though empirical confirmation of efficacy remains essential.^[6]

Historical Development

The concept of a biological target emerged in the late 19th and early 20th centuries through Paul Ehrlich's receptor theory, which posited that cells possess specific "side-chain" receptors capable of selective binding to ligands, enabling targeted therapeutic interventions akin to "magic bullets" that distinguish pathogens from host tissues.^[12] Ehrlich's synthesis of arsphenamine (Salvarsan) in 1909, approved for clinical use in 1910, marked the first deliberate chemotherapeutic agent designed to target the syphilis-causing bacterium Treponema pallidum via arsenic-based binding, achieving cure rates of up to 30% in early trials while minimizing host toxicity.^[13] This approach contrasted with prior empirical remedies, grounding drug action in specific molecular interactions rather than generalized toxicity. Throughout the mid-20th century, pharmacological research expanded target identification by elucidating biochemical pathways, with sulfonamide antibiotics introduced in 1935 selectively inhibiting bacterial dihydropteroate synthase enzymes in folate synthesis—a pathway absent in humans—reducing mortality from infections like puerperal sepsis by over 80% in clinical settings.^[14] The 1940s discovery of penicillin's mechanism, disrupting bacterial cell wall peptidoglycan via inhibition of transpeptidase enzymes, further exemplified target specificity, though initial drug screening remained largely phenotypic, relying on observable effects in infected models rather than predefined molecular entities.^[15] By the 1950s and 1960s, radioligand binding assays and X-ray crystallography began resolving receptor structures, such as the beta-adrenergic receptor in 1970, enabling quantitative assessment of drug affinity (e.g., dissociation constants in the nanomolar range) and affinity.^[16] The late 20th century saw a paradigm shift toward systematic target-based drug discovery (TBDD) in the 1980s, driven by recombinant DNA technologies introduced in 1973, which allowed heterologous expression of human proteins for screening.^[8] High-throughput screening platforms, commercialized in the early 1990s, facilitated testing of compound libraries exceeding 100,000 molecules against isolated targets, accelerating identification of hits like selective kinase inhibitors.^[1] The Human Genome Project, initiated in 1990 and completed in 2003, identified over 20,000 protein-coding genes, expanding potential targets from fewer than 500 known in 1990 to thousands, though only about 10% have proven druggable to date.^[17] This era prioritized causal validation of targets via genetic knockdown models, such as RNA interference developed in 1998, to confirm disease linkage before compound optimization.^[18] Despite successes, including statins targeting HMG-CoA reductase since 1987, TBDD faced scrutiny for lower clinical success rates—approximately 10% from hit to approval—compared to historical phenotypic methods, prompting hybrid approaches in the 2010s.^[19]

Fundamental Principles

Mechanisms of Interaction

Biological targets, such as proteins including enzymes, receptors, ion channels, and transporters, interact with ligands through molecular recognition processes that alter target function to elicit therapeutic effects. These interactions primarily involve binding at specific sites, governed by principles of affinity, specificity, and kinetics, where ligands form non-covalent bonds via hydrogen bonding, electrostatic forces, van der Waals interactions, and hydrophobic effects, enabling reversible association and dissociation.^[20] Covalent binding, in contrast, establishes irreversible linkages through reactive chemical groups on the ligand that form stable bonds with nucleophilic residues on the target, such as cysteine or serine, often prolonging duration of action but risking off-target reactivity.^[21] Interactions occur at orthosteric or allosteric sites. Orthosteric binding targets the endogenous ligand's active site, directly competing for occupancy and typically modulating function through steric blockade or mimicry, as seen in competitive enzyme inhibitors that elevate the Michaelis constant (Km) without affecting maximum velocity (Vmax).^[22] Allosteric binding engages topographically distinct sites, inducing conformational changes that propagate to the orthosteric site or functional domains, thereby modulating activity without direct competition; this can enhance selectivity and reduce dose-limiting side effects compared to orthosteric ligands.^[23] Conformational dynamics underpin these mechanisms: induced fit involves ligand-driven target reshaping post-binding to optimize interactions, while conformational selection entails the ligand preferentially binding pre-existing target conformations in equilibrium, shifting the population toward active or inactive states.^[24] Functional outcomes vary by target class. For receptors, agonists stabilize the active conformation to initiate downstream signaling, such as G-protein coupling or phosphorylation cascades; partial agonists elicit submaximal responses, while antagonists prevent agonist binding without intrinsic activity, and inverse agonists reduce basal activity in constitutively active receptors.^[25] Enzyme inhibition includes competitive (reversible orthosteric blockade), non-competitive (allosteric reduction of Vmax), and uncompetitive (binding only to enzyme-substrate complex) modes, each altering catalytic efficiency distinctly.^[22] Ion channels and transporters undergo gating modulation, where ligands open, close, or stabilize states to control ion or solute flux, as in voltage-gated sodium channel blockers that bind inactivated states to prevent depolarization.^[26] These mechanisms collectively determine pharmacological potency, with binding kinetics influencing residence time—the duration of target occupancy—which correlates with efficacy duration beyond simple affinity measures.^[27]

Classification of Targets

Biological targets in pharmacology and drug discovery are primarily classified by their molecular composition and functional characteristics, with proteins constituting the overwhelming majority of validated targets due to their accessibility and role in disease pathways.^[28] Approximately 90% of approved drugs interact with protein targets, reflecting their prevalence in therapeutic modulation.^[10] These protein targets are subdivided into functional categories such as enzymes, receptors, ion channels, and transporters, each offering distinct opportunities for selective drug binding and intervention.^[29] Enzymes represent a core class, functioning as catalysts in metabolic and signaling pathways; inhibition or activation of enzymes like kinases (e.g., BCR-ABL targeted by imatinib) or proteases disrupts aberrant processes in conditions such as cancer or inflammation.^[1] Receptors, another major category, transduce extracellular signals into intracellular responses and include G-protein-coupled receptors (GPCRs), which account for about 30% of marketed drugs (e.g., beta-adrenergic receptors for hypertension), nuclear receptors (e.g., steroid hormone receptors modulating gene expression), and kinase-linked receptors (e.g., EGFR in oncology).^[1] ^[10] Ion channels regulate ion flux across membranes, serving as targets for drugs like calcium channel blockers in cardiovascular therapy, while transporters facilitate solute movement and are exemplified by targets like the serotonin transporter inhibited by SSRIs.^[30] ^[29] Beyond enzymes, non-enzyme proteins such as scaffolding, regulatory, and structural elements expand the target space, often involving protein-protein interactions (PPIs) that lack deep pockets but can be modulated by small molecules or biologics in pathways like transcription or signaling cascades.^[28] Non-protein targets, though less common, include nucleic acids like DNA or RNA, which are engaged by agents such as antimetabolites or antisense oligonucleotides in antiviral or anticancer applications.^[5] Classifications may also consider subcellular location (e.g., extracellular, membrane-bound, intracellular) or druggability, but functional and molecular typing remains foundational for prioritizing candidates in discovery pipelines.^[1]

Identification and Validation Processes

Target Identification Methods

Target identification methods encompass a range of experimental, genetic, biochemical, and computational strategies aimed at pinpointing biomolecules, such as proteins or nucleic acids, that are causally linked to disease processes or responsive to therapeutic modulation.^[4] These approaches are broadly classified into disease-centered methods, which derive targets from pathological mechanisms via genomic or pathway analyses, and drug-centered methods, which deconvolute targets for compounds identified in phenotypic screens.^[2] Empirical validation remains essential, as initial candidates often require orthogonal confirmation to establish causality over correlation.^[6] Genetic and genomic methods leverage perturbations to infer target relevance. Genome-wide association studies (GWAS) identify disease-associated genetic variants, prioritizing targets like PCSK9 for hypercholesterolemia based on lipid pathway enrichment.^[31] Functional genomics employs RNA interference (RNAi), CRISPR-Cas9 knockouts, or overexpression to assess phenotype changes, as in identifying kinase targets via loss-of-function screens in cancer models.^[32] Transcriptomic profiling, including RNA sequencing, quantifies differential expression in diseased versus healthy tissues, with tools like DESeq2 for statistical rigor, though it risks conflating correlation with causation absent mechanistic follow-up.^[5] Biochemical and proteomic techniques directly probe protein-ligand interactions. Chemoproteomics uses affinity-based probes, such as immobilized small molecules or activity-based protein profiling (ABPP), to capture and identify binding partners via mass spectrometry; for instance, ABPP has mapped serine hydrolase targets for covalent inhibitors in inflammation pathways.^[33] Quantitative proteomics, including stable isotope labeling by amino acids in cell culture (SILAC) coupled with MS, measures proteome-wide changes post-perturbation, enabling target ranking by fold-change and significance.^[34] These methods excel in native cellular contexts but demand high probe specificity to minimize off-target artifacts.^[35] Computational and network-based approaches integrate multi-omics data for hypothesis generation. Homology modeling and structure prediction via tools like AlphaFold predict druggable pockets, accelerating virtual screening for targets like GPCRs.^[36] Network pharmacology constructs protein-protein interaction graphs from databases like STRING, applying centrality metrics to nominate hubs in disease modules, as in repurposing metformin for cancer via AMPK-mTOR edges.^[37] Machine learning models, trained on binding affinity datasets, score potential targets by features like physicochemical descriptors, with reported accuracies exceeding 80% in kinase classification tasks.^[38] Such in silico methods reduce experimental burden but require validation against empirical data to counter overfitting or bias in training sets from academic sources.^[31]

Validation and Druggability Evaluation

Target validation in drug discovery involves confirming that modulation of a biological target causally influences disease pathology and yields a therapeutic benefit, thereby reducing the risk of pursuing non-efficacious candidates. This process typically integrates multiple lines of evidence, including genetic perturbations such as gene knockouts or knockdowns via RNA interference (RNAi), which demonstrate phenotypic changes relevant to the disease state in model systems.^[39] Orthology-based validation assesses conservation across species, where human genetic variants or animal model disruptions (e.g., CRISPR-Cas9 knockouts) recapitulate disease traits, providing orthogonal support.^[6] Pharmacological validation employs tool compounds to mimic genetic effects, ensuring selectivity and confirming on-target activity, though challenges arise from off-target liabilities in early probes.^[40] High-confidence validation requires convergence of genetic, expression (e.g., tissue-specific mRNA/protein levels), and epidemiological data, such as Mendelian randomization studies linking germline variants to disease outcomes.^[41] Emerging techniques enhance validation rigor, including quantitative systems pharmacology (QSP) models that simulate target modulation effects and Bayesian network analysis for prioritizing candidates based on multi-omics integration.^[42] Phase I clinical trials increasingly serve as validation platforms for first-in-class drugs, measuring pharmacodynamic biomarkers to affirm target engagement and early efficacy signals.^[43] Despite these advances, validation remains iterative, as incomplete evidence (e.g., from cell lines lacking physiological context) can lead to attrition; thus, human-relevant models like organoids or patient-derived xenografts are prioritized to bridge preclinical gaps.^[6] Druggability evaluation assesses the feasibility of developing small-molecule or biologic modulators that bind with sufficient affinity and selectivity to achieve therapeutic modulation without excessive toxicity. Key criteria include the presence of ligand-binding pockets with suitable physicochemical properties, such as enclosure, hydrophobicity, and volume, often scored via computational tools like SiteMap, which classify sites as druggable if they resemble known ligandable pockets (e.g., scores >0.8).^[44] Sequence-based methods analyze homology to tractable protein families (e.g., kinases with established inhibitors), while structure-based approaches use X-ray crystallography or homology modeling to predict bindability.^[45] Experimental fragment-based screening confirms druggability by detecting weak binders, with hit rates correlating to success; pockets yielding >1-2 fragments per 1,000 screened are deemed highly druggable.^[46] Machine learning models integrate these features for predictive assessment, training on databases of known targets to forecast ligandability, though they underperform for novel folds or allosteric sites.^[47] For biologics, druggability extends to surface epitopes amenable to antibody binding, evaluated via alanine scanning mutagenesis. Overall, targets failing druggability thresholds—e.g., lacking pockets or exhibiting high plasticity—are deprioritized, as historical data show only ~10-15% of novel proteins are inherently small-molecule druggable without engineering.^[48] Integration of validation and druggability early in pipelines, as in the GOT-IT recommendations, minimizes late-stage failures by weighting evidence hierarchically.^[6]

Applications in Drug Discovery and Therapeutics

Target-Based Drug Design

Target-based drug design (TBDD) represents a rational strategy in drug discovery that focuses on modulating specific biological targets, such as enzymes, receptors, or ion channels, whose dysregulation contributes to disease. This approach begins with a validated target and employs computational and experimental methods to identify or optimize small molecules that bind selectively to the target's active site or allosteric regions, thereby altering its function to restore physiological balance or inhibit pathological activity. Unlike phenotypic screening, which observes cellular or organismal responses without predefined targets, TBDD leverages detailed structural and biochemical knowledge to prioritize compounds with high potency and specificity, reducing the risk of off-target effects early in development.^[49]^[50] Central to TBDD is structure-based drug design (SBDD), which utilizes three-dimensional target structures obtained via X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryo-electron microscopy to guide ligand optimization. Molecular docking simulations predict binding affinities by evaluating how candidate compounds fit into the target's binding pocket, often followed by virtual screening of large chemical libraries to rank potential hits. Fragment-based drug discovery (FBDD), a complementary technique, screens low-molecular-weight fragments that bind weakly but can be elaborated into high-affinity leads through iterative structure-activity relationship (SAR) studies. High-throughput screening (HTS) assays, such as fluorescence polarization or surface plasmon resonance, validate these computational predictions by measuring binding kinetics and inhibition constants in vitro. These integrated methods have accelerated lead optimization, with binding affinity improvements often achieving nanomolar potency within iterative cycles.^[50]^[51]^[52] In therapeutic applications, TBDD has yielded landmark successes, including kinase inhibitors like imatinib, which targets the BCR-ABL fusion protein in chronic myeloid leukemia, achieving response rates exceeding 90% in clinical trials through precise ATP-competitive binding. Similarly, HIV protease inhibitors such as saquinavir were designed using SBDD to mimic peptide substrates, disrupting viral maturation and forming the backbone of antiretroviral therapy. Despite these advances, TBDD's overall contribution to first-in-class drugs is estimated at around 56% of approved small molecules from 2000 onward, though it excels in polypharmacology challenges for complex diseases like cancer, where multi-target inhibitors address resistance mechanisms. Integration with phenotypic validation ensures translational efficacy, as pure target modulation may not correlate with in vivo outcomes due to factors like pharmacokinetics and tissue penetration.^[53]^[54]

Case Studies of Validated Targets

The BCR-ABL fusion protein serves as a paradigmatic validated biological target in chronic myeloid leukemia (CML), where the Philadelphia chromosome translocation t(9;22) generates an constitutively active tyrosine kinase driving leukemogenesis. Identified in 1982 through molecular cloning of the breakpoint cluster region (BCR) and ABL1 genes, BCR-ABL was validated as oncogenic via transformation assays in fibroblasts and hematopoietic cells, demonstrating its sufficiency to induce leukemia-like phenotypes in murine models.^[55] Imatinib, a small-molecule inhibitor selectively binding the ATP-binding site of BCR-ABL, was developed through high-throughput screening and structure-based optimization, achieving FDA approval in 2001 after phase I trials in 1998 showed 98% hematologic responses and 31% major cytogenetic responses in chronic-phase CML patients resistant to prior therapies.^[56] Long-term follow-up data indicate 5-year survival rates exceeding 90% with imatinib frontline therapy, confirming BCR-ABL inhibition as causally linked to disease control, though resistance via T315I mutations necessitates second-generation inhibitors like dasatinib (approved 2006).^[57] HIV-1 protease, an aspartyl protease essential for viral maturation, exemplifies target validation through structural biology and functional genomics in infectious disease. Sequenced in 1985 as part of the HIV genome, the protease was validated by mutagenesis studies showing cleavage-site mutations abolish viral infectivity and by crystallographic structures solved in 1989 revealing a homodimer active site amenable to inhibition.^[58] Rational drug design yielded saquinavir, the first protease inhibitor approved by the FDA in December 1995, which binds the active site with picomolar affinity and reduced HIV RNA levels by over 90% in early trials when combined with nucleoside analogs, paving the way for highly active antiretroviral therapy (HAART).^[59] Subsequent inhibitors like ritonavir (1996) and darunavir (2006) addressed resistance mutations such as V82A, with cohort studies demonstrating HAART regimens targeting protease achieving undetectable viral loads in 70-90% of adherent patients and restoring life expectancy near population norms.^[60] Epidermal growth factor receptor (EGFR), a receptor tyrosine kinase overexpressed or mutated in non-small cell lung cancer (NSCLC), highlights validation via oncogenic driver studies and precision oncology. EGFR mutations like L858R were identified in 2004 as predictors of response, with validation through xenograft models showing mutant EGFR dependence for tumor growth and RNA interference knockdown reducing proliferation.^[61] Erlotinib, approved by the FDA in 2004, inhibits EGFR kinase activity, yielding objective response rates of 70-80% in mutation-positive NSCLC patients in the EURTAC trial (2011), with median progression-free survival of 9.7 months versus 5.2 months for chemotherapy.^[61] Second-generation inhibitors like afatinib (2013) target exon 19 deletions and T790M resistance, with real-world data from 2020-2023 confirming improved overall survival (up to 38 months) in validated cohorts, underscoring EGFR's causal role despite acquired resistance via C797S mutations.^[61]

Tools and Resources

Databases and Bioinformatics Tools

ChEMBL, maintained by the European Bioinformatics Institute (EMBL-EBI), is a manually curated database aggregating chemical, bioactivity, and genomic data for bioactive molecules with drug-like properties, covering targets across drug discovery stages including preclinical and clinical phases; as of 2023, it includes data from patents and literature supporting target validation through measured activities like binding affinities and potencies.^[62]^[63] BindingDB functions as the first public molecular recognition database, compiling experimentally determined binding affinities between small molecules and protein targets to aid pharmacology and drug design research, with data sourced from scientific literature and patents.^[64] DrugBank serves as a comprehensive repository of detailed drug and target information, including sequences, pathways, and interactions, used globally by researchers for querying validated targets associated with approved therapeutics.^[65] The Therapeutic Target Database (TTD) compiles entries on therapeutic targets, associated drugs, and biomarkers, facilitating the exploration of target-disease relationships through curated data from clinical and genomic sources.^[66] Guide to PHARMACOLOGY (IUPHAR/BPS), an expert-curated resource, provides searchable data on targets such as receptors and ion channels, ligands, and linked diseases, emphasizing quantitative pharmacological parameters for precision in target annotation.^[67] More specialized resources like HCDT 2.0, updated in 2025, integrate multi-omics data to deliver highly confident, experimentally verified drug-target associations, expanding to RNA interactions and incorporating over 500,000 entries from prior validations.^[68] GETdb, launched in 2024, focuses on genetic and evolutionary aspects of targets, linking approximately 4,000 targets to over 29,000 drugs via data from DrugBank, TTD, and DGIdb for evolutionary conservation analysis in druggability assessment.^[69] Bioinformatics tools complement these databases by enabling computational target identification and analysis. Network-based approaches, such as those using protein-protein interaction maps from STRING or pathway enrichment via KEGG and Gene Ontology, help prioritize targets by integrating genomic and phenotypic data for causal inference in disease mechanisms.^[70]^[71] In silico prediction tools, including comparative genomics pipelines and molecular docking software like AutoDock, simulate target-ligand binding to validate druggability from sequence and structural data.^[72] Platforms like e-TSN offer interactive visualization for target-disease associations, ranking candidates by biometric scores such as novelty and significance derived from integrated omics datasets.^[73] These tools, often open-source, support iterative querying across databases to filter targets by criteria like expression levels or evolutionary conservation, enhancing empirical validation in drug discovery workflows.^[74]

Recent Computational Advances

AlphaFold 3, released on May 8, 2024, by DeepMind and Isomorphic Labs, represents a pivotal advancement in computational structural biology, enabling highly accurate predictions of biomolecular complexes including proteins, DNA, RNA, ligands, and ions.^[75] This diffusion-based architecture improves upon AlphaFold 2 by modeling joint structures of interacting molecules, which facilitates the identification of binding sites on biological targets and enhances virtual screening for drug candidates targeting previously intractable proteins.^[76] In drug discovery, AlphaFold 3 has demonstrated superior performance in static protein-ligand interaction predictions with minimal conformational changes, outperforming traditional docking methods and accelerating target validation by providing atomic-level insights into potential therapeutic interfaces.^[77] Machine learning techniques, particularly graph neural networks (GNNs) and transformers, have advanced drug-target interaction (DTI) prediction, integrating multi-omics data for more precise target prioritization.^[78] For instance, GNN models like RWGNN achieve high AUC scores (e.g., 0.957) in predicting interactions by representing molecular graphs, while transformer-based approaches such as Mol-BERT extract contextual features from chemical and biological datasets to identify novel targets.^[78] These methods, reviewed in studies from 2019–2024, enable systematic analysis of genomic, proteomic, and phenotypic data, reducing reliance on experimental validation and addressing biases in underrepresented chemical spaces through transfer learning.^[78] Computational detection of cryptic binding pockets—transient sites on target proteins that open upon ligand binding—has progressed with algorithms combining molecular dynamics simulations and machine learning, identifying druggable hotspots invisible in static structures.^[79] Tools like those in canSAR, updated in 2024, incorporate enhanced structural ligandability assessments and chemical standardization to validate target tractability early in discovery pipelines.^[80] Additionally, fragment-based prediction models such as FragFold, developed in early 2025, use AI to forecast protein fragments that bind or inhibit targets, offering alternatives to small-molecule drugs for challenging biological targets like protein-protein interfaces.^[81] These advances collectively shorten timelines from target identification to validation, with AI-driven platforms demonstrating up to 50% improvements in hit identification rates in virtual screening campaigns, though challenges persist in model interpretability and generalization to novel targets.^[78] Integration of knowledge graphs further refines predictions by unifying disparate data sources, as seen in frameworks like DrugReAlign for repurposing existing drugs against validated targets.^[78]

Challenges, Limitations, and Debates

Key Challenges in Target Selection

One major challenge in biological target selection is establishing a robust causal link between the target and disease pathology, as many candidates emerge from correlative associations in high-throughput omics data rather than direct evidence of necessity. Preclinical validation often relies on animal models or cell lines that exhibit poor translatability to human physiology, contributing to reproducibility crises where up to 50% of published findings fail replication. For instance, the kinase MELK was pursued as a cancer target based on initial knockdown studies, but subsequent CRISPR/Cas9 experiments demonstrated no dependency in tumor cells, highlighting risks of false positives from inadequate genetic perturbation methods.^[6]^[6]^[6] Assessing druggability— the potential for a target to be modulated by orally bioavailable small molecules with sufficient affinity and selectivity—poses significant hurdles, particularly for non-enzymatic proteins lacking well-defined pockets. Traditional methods like fragment-based screening are resource-intensive, and even validated targets from druggable families, such as GPCRs, do not guarantee success for homologs due to structural variations. This leads to high attrition, with target-related issues accounting for approximately two-thirds of late-stage clinical failures, exacerbating the overall drug development success rate of only 10-20% from Phase I to approval.^[6]^[82]^[83] Additional complexities arise from biological redundancy, where pathway compensation or polypharmacology can undermine single-target interventions, and from the inaccessibility of certain targets, such as intracellular proteins shielded by membranes or blood-brain barrier restrictions. Early-stage de-risking is further complicated by the absence of reliable biomarkers for target engagement, resulting in 97% failure rates from target identification through preclinical development. These factors collectively drive the need for multifaceted validation strategies, yet traditional in vivo and in vitro approaches remain time-consuming and costly, often spanning years and millions in investment per candidate.^[6]^[84]^[2]

Controversies: Target-Based vs. Phenotypic Approaches

The debate between target-based and phenotypic drug discovery approaches centers on their relative efficacy in identifying novel therapeutics, particularly for complex diseases involving multifactorial biology. Target-based discovery, dominant since the 1990s following advances in genomics and high-throughput screening, focuses on modulating a predefined molecular target, such as a protein kinase, using biochemical assays to guide structure-activity relationship (SAR) optimization. In contrast, phenotypic screening assays compounds in cellular or organismal models to detect desired physiological effects without prior target knowledge, relying on subsequent deconvolution to elucidate mechanisms of action (MOA).^[85] Proponents of target-based methods argue they enable precise drug design and rapid iteration based on known biology, but critics contend this reductionist paradigm often fails to account for polypharmacology and emergent phenotypes, contributing to high attrition rates in clinical trials. Empirical analyses of approved drugs highlight phenotypic approaches' edge in generating first-in-class innovations. A 2011 review of 218 new molecular entities approved between 1999 and 2008 found that phenotypic screening originated 28 first-in-class drugs (those acting via novel mechanisms), compared to 17 from target-based screening, while target-based methods predominated for "follower" drugs mimicking established mechanisms (36 versus 11 for phenotypic). This pattern aligns with historical trends: prior to 1990, phenotypic strategies drove most approvals, including blockbusters like aspirin and penicillin, whereas the post-genomic shift to target-based discovery correlated with stagnant productivity, as evidenced by Eroom's law—the exponential rise in R&D costs per new drug since 1980 despite technological advances.^[86] Follow-up studies, including a 2013 analysis, reinforced that phenotypic discovery yields higher proportions of mechanistically novel agents (38% first-in-class versus 23% for target-based), particularly for antibacterials and antiparasitics where single-target inhibition proves insufficient.^[87] Controversies persist over methodological biases and practical hurdles. Target-based advocates, often from industry, emphasize the challenges of phenotypic hit progression, such as opaque MOA impeding SAR and raising reproducibility risks in complex assays; for instance, without target validation, compounds may exhibit false positives from assay artifacts.^[85] Conversely, phenotypic supporters, citing cases like the kinase inhibitor imatinib (initially phenotypic but retrospectively target-linked to BCR-ABL), argue that overreliance on unvalidated targets—many of which fail in vivo due to compensatory pathways—exacerbates the drug discovery crisis, with estimates suggesting up to 95% of target-based candidates attrition before Phase III.^[86] Recent evidence from oncology and rare diseases underscores polypharmacology's role, where multi-target effects drive efficacy overlooked by single-target screens, prompting hybrid strategies that integrate phenotypic validation early.^[85] Despite these insights, institutional inertia in pharmaceutical R&D favors target-based pipelines for their perceived tractability and patentability, though phenotypic revival has yielded successes like nusinersen for spinal muscular atrophy in 2016.^[86]

Off-Target Effects and Safety Concerns

Off-target effects refer to the unintended interactions of a therapeutic agent with biological molecules other than its primary target, often resulting from structural similarities among proteins or the inherent promiscuity of small-molecule compounds. These interactions can produce pharmacological activities distinct from the desired therapeutic outcome, encompassing both related and unrelated biological pathways. In drug discovery, such effects complicate selectivity profiling, as compounds may bind unintended receptors, enzymes, or ion channels with affinities comparable to the on-target interaction.^[88] Safety concerns arise primarily from the potential for off-target modulation to induce toxicity, idiosyncratic reactions, or exaggerated pharmacology, contributing significantly to adverse drug reactions (ADRs) and clinical trial failures.^[89] ADRs linked to off-target binding affect 10–20% of hospitalized patients and up to 25% of outpatients, imposing a substantial burden on healthcare systems through prolonged hospital stays and increased mortality.^[90] In the United States, serious ADRs occur in over two million patients annually, with around 100,000 associated deaths, many traceable to unanticipated off-target liabilities rather than dose-related on-target effects.^[91] For example, kinase inhibitors frequently exhibit off-target activity against homologous family members, leading to organ-specific toxicities such as cardiotoxicity via hERG channel blockade or dermatological reactions from epidermal growth factor receptor crosstalk.^[92] Predicting and mitigating these risks remains challenging, as empirical screening panels often miss low-affinity interactions that manifest at therapeutic concentrations, while in silico models struggle with incomplete protein structural data.^[93] Up to 90% of clinical-stage drug candidates fail partly due to toxicity profiles unmasked by off-target effects, underscoring the need for comprehensive secondary pharmacology assessments early in development.^[94] Although polypharmacology can sometimes yield beneficial outcomes, such as drug repurposing (e.g., crizotinib's activity against ROS1 in non-small cell lung cancer beyond its ALK primary target), uncontrolled off-target engagement predominantly heightens safety liabilities, including hypersensitivity or carcinogenic potential from chronic unintended signaling.^[92]

Emerging Developments

Advances in Targeting Difficult Proteins

Targeted protein degradation (TPD) technologies, such as proteolysis-targeting chimeras (PROTACs), have enabled the modulation of previously undruggable proteins by hijacking the ubiquitin-proteasome system to induce selective degradation rather than relying on orthosteric inhibition. PROTACs consist of a target-binding ligand linked to an E3 ligase recruiter, facilitating ubiquitination and proteasomal breakdown of proteins lacking traditional druggable pockets, including transcription factors and scaffolding proteins. As of 2024, over 20 PROTAC candidates have entered clinical trials, with notable efficacy against oncogenic targets like androgen receptor and CDK9 in preclinical models.^[95]^[96] Molecular glues represent a complementary TPD modality, where small molecules stabilize aberrant protein-E3 ligase interactions to promote degradation without a bifunctional linker, offering advantages in oral bioavailability and tissue penetration for difficult targets such as intrinsically disordered proteins (IDPs). Recent developments include dual PROTACs that simultaneously degrade multiple proteins, enhancing potency against complex pathways in cancer, as demonstrated in 2024 studies targeting CDK families. Clinical progress includes lenalidomide derivatives, which glue cereblon to neosubstrates like IKZF1/3, validating the approach for hematologic malignancies since approvals in the 2010s, with expanded applications to solid tumors by 2025.^[97] For IDPs, which constitute ~30% of the human proteome and drive diseases like neurodegeneration through fuzzy interactions, advances in computational design have yielded binders that stabilize disordered states or disrupt condensates. Ensemble docking methods, accounting for conformational ensembles, identified hits against IDP targets in 2025 bioRxiv preprints, while de novo protein design pipelines generated high-affinity binders to arbitrary disordered regions, as reported in a July 2025 Science study. These tools address the dynamic nature of IDPs, previously hindering small-molecule engagement, with potential for stapled peptides or macrocycles to lock transient conformations.^[98]^[99] Allosteric modulators have progressed for transcription factors (TFs), historically challenging due to flat DNA-binding interfaces, by exploiting remote sites to alter conformational dynamics or cofactor recruitment. A 2024 strategy using pharmacophore modeling identified TF modulators disrupting protein-protein interactions, while covalent allosteric inhibitors target mutant forms like KRAS beyond G12C, with adagrasib approval in 2022 paving the way for broader application. These approaches, combined with cryo-EM-resolved structures, have yielded selective inhibitors for MYC and STAT3 in preclinical stages as of 2025, emphasizing event-driven degradation over transient binding for sustained efficacy.^[96]^[100]

Integration of AI and Multi-Omics Data

Multi-omics approaches generate vast datasets encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics, which collectively provide a systems-level view of biological processes relevant to target identification.^[101] Artificial intelligence, particularly machine learning and deep learning algorithms, addresses the challenges of integrating these heterogeneous, high-dimensional data by performing dimensionality reduction, feature extraction, and pattern recognition to uncover potential drug targets.^[102] For instance, AI models can correlate genetic variants with protein expression changes and phenotypic outcomes, prioritizing targets based on causal associations rather than isolated correlations.^[103] Recent advancements leverage graph neural networks and transformer-based models to fuse multi-omics layers, enabling predictive modeling of disease mechanisms and target-drug interactions.^[104] In a 2023 study, AI integration of The Cancer Genome Atlas datasets identified novel therapeutic targets by analyzing multi-omics profiles across tumor types, revealing pathways overlooked in single-omics analyses.^[105] Large language models applied to single-cell multi-omics data, as explored in 2025 research, expand target discovery by processing spatiotemporal gene expression and chromatin accessibility data to predict cell-type-specific vulnerabilities.^[106] This integration enhances target validation through in silico simulations of multi-omics perturbations, reducing reliance on costly wet-lab experiments.^[107] For example, AI-driven frameworks have accelerated biomarker and repurposing efforts by quantifying off-target risks via integrated proteomics and metabolomics profiles.^[108] However, effective implementation requires robust data harmonization to mitigate batch effects and biases inherent in omics platforms, with ongoing developments focusing on federated learning for privacy-preserving multi-source integration.^[109] These methods have demonstrated up to 30% improvements in target prediction accuracy in benchmark studies compared to traditional statistical approaches.^[110]

References

[1]
Biological Target - an overview | ScienceDirect Topics
Target, also known as biological target, is any part of the living organisms to which the drugs bind in order to bring the physiological change. Targets are the ...
[2]
The Art of Finding the Right Drug Target: Emerging Methods and ...
Drug targets are specific molecules in biological tissues and body fluids that interact with drugs. Drug target discovery is a key component of drug ...
[3]
Biological Target - an overview | ScienceDirect Topics
Biological targets refer to biopolymers or aggregates that drug candidates are designed to address, which can include multiple molecular targets relevant to ...
[4]
Target identification and mechanism of action in chemical biology ...
Target identification uses target-based (reverse) or phenotype-based (forward) approaches, with direct biochemical, genetic, and computational methods.
[5]
Target identification of small molecules: an overview of the current ...
Oct 10, 2023 · Target identification is an essential part of the drug discovery and development ... biological target for a given drug can be extremely difficult ...
[6]
Improving target assessment in biomedical research: the GOT-IT ...
Nov 16, 2020 · ... biological target have a reproducible effect on human physiology. Such genetic information can complement existing lines of evidence, and ...
[7]
Target Identification and Validation in Drug Development
Nov 4, 2022 · The process of drug discovery begins with the identification of a possible biological target and elucidating its role in the disease. A ...
[8]
Drug discovery and development: Role of basic biological research
Nov 11, 2017 · Most often, the development of a new medicine starts when basic scientists learn of a biological target (e.g., a receptor, enzyme, protein, gene ...
[9]
Target Identification & Validation in Drug Discovery
Feb 15, 2024 · In target-based drug discovery, biological (drug) targets are already established (or 'discovered') before lead discovery starts – hence target ...
[10]
Pharmacological Target - an overview | ScienceDirect Topics
The major protein target classes are membrane receptors, enzymes, ion channels and transporter proteins. Of these, the most prominent drug targets are receptors ...
[11]
Therapeutic target database update 2022: facilitating drug discovery ...
Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. These include poor binders and non-binders ...
[12]
Paul Ehrlich (1854-1915) and His Contributions to the Foundation ...
Feb 5, 2016 · The Concept of 'Magic Bullets' (Zauberkugeln). The next logical step was to extend the receptor-ligand concept and to exploit the specific ...
[13]
From magic bullets to modern therapeutics: Paul Ehrlich, the ...
Paul Ehrlich was a pioneering Immunobiologist and physician who coined the term 'complement' in the year 1899.
[14]
A brief history of pharmacology - American Chemical Society
The concept was first proposed about a hundred years ago by Paul Ehrlich, the great bacteriologist and chemist who synthesized salvarsan (also known as “606”) ...<|separator|>
[15]
The Contributions of Paul Ehrlich to Pharmacology - NIH
Paul Ehrlich stood out because he went beyond the study of drugs and toxic substances: new drugs needed to be synthesized for specific targets.
[16]
The fall and rise of pharmacology – (Re-)defining the discipline?
Pharmacology is an integrative discipline that originated from activities, now nearly 7000 years old, to identify therapeutics from natural product sources.
[17]
A Historical Overview of Natural Products in Drug Discovery - PMC
This brief review aims to highlight historically significant bioactive marine and terrestrial natural products, their use in folklore and dereplication ...
[18]
The evolution of drug discovery: from phenotypes to targets, and back
This review chronicles major trends and transformative events in the evolution of drug discovery, and underscores the importance of phenotypic approaches.
[19]
The utility of target-based discovery - Taylor & Francis Online
By some measures, target-based drug discovery has been highly successful. Of 113 first in class drugs approved by the US FDA from 1999 to 2013, 70% were ...
[20]
Mechanisms of ligand binding | Biophysics Reviews | AIP Publishing
Nov 16, 2020 · Many processes in chemistry and biology involve interactions of a ligand with its molecular target. Interest in the mechanism governing such ...
[21]
Inhibitor, Activator, Agonist, Antagonist, Reverse Agonist, Blocker
Oct 30, 2024 · Inhibitors are classified into reversible and irreversible inhibitors. Reversible inhibitors bind to enzymes through non-covalent bonds.
[22]
Pharmacodynamics - StatPearls - NCBI Bookshelf - NIH
Jan 29, 2023 · Pharmacodynamics studies a drug's molecular, biochemical, and physiologic effects or actions, and the action of the drug on the organism.
[23]
Toward Understanding “the Ways” of Allosteric Drugs
Sep 13, 2017 · However, allosteric drugs, or drugs that alter target activity by binding at a site that is distinct from the active or orthosteric site, often ...
[24]
Mechanisms of ligand binding - PubMed - NIH
Ligand binding mechanisms include induced fit, where conformational changes follow binding, and conformational selection, where the ligand selects the optimal ...Missing: pharmacology | Show results with:pharmacology
[25]
Drug–Receptor Interactions - Clinical Pharmacology - MSD Manuals
Agonists and antagonists · Agonists activate receptors to produce the desired response. Conventional agonists increase the proportion of activated receptors.
[26]
Site Selectivity - Drugs - Merck Manual Consumer Version
Drugs that target receptors are classified as agonists or antagonists. ... Drugs that target enzymes are classified as inhibitors or activators (inducers).
[27]
How residence time works in allosteric drugs - ScienceDirect.com
Aug 30, 2025 · Drug residence time is the duration a drug is bound to its target. Allosteric drugs determine this by the population shift, which influences ...
[28]
Expanding the Number of “Druggable” Targets: Non-Enzymes ... - NIH
... types of targets, including non-enzymes (Figure 1). Non ... Natural products and their biological targets: proteomic and metabolomic labeling strategies.
[29]
Classical Targets in Drug Discovery - Creative Biolabs
Tools such as X-ray crystallography, molecular modeling, PCR, and recombinant DNA technologies provided a sharper and sharper picture of the biological targets ...
[30]
Video: Targets for Drug Action: Overview - JoVE
Sep 22, 2023 · Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
[31]
Artificial intelligence in cancer target identification and drug discovery
May 10, 2022 · Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks.
[32]
A Review of Target Identification Strategies for Drug Discovery
The biological assay, such as RNAi, RNA sequencing, DNA microarray, and Gal4/UAS system, is commonly used to identify the target in recent years. The machine- ...
[33]
How chemoproteomics can enable drug discovery and development
The chemoproteomic method activity-based protein profiling (ABPP) targets the shared mechanistic and structural features of large enzyme classes using active ...
[34]
Therapeutic Target Identification and Drug Discovery Driven by ...
Jul 23, 2024 · This review attempts to summarize methods and illustrative examples of small-molecule target identification via chemical proteomics.
[35]
Chemoproteomic approaches to drug target identification and drug ...
Mar 15, 2012 · This review describes experimental strategies in current chemical proteomics research, discusses recent examples of successful applications, and highlights ...
[36]
Target identification by structure-based computational approaches
Mar 1, 2023 · Computational methods reduce the time required for target identification. IVS, a structure-based method, deciphers the protein targets of ...<|separator|>
[37]
https://www.annualreviews.org/content/journals/10.1146/annurev-biodatasci-101424-120950
[38]
A machine learning-based chemoproteomic approach to identify ...
Aug 21, 2020 · We use machine learning to discern features indicative of drug binding and integrate them into a single score to identify protein targets of ...
[39]
Target Validation: Linking Target and Chemical Properties to ... - NIH
Genetic approaches to target validation can be broadly classified as target (gene) knockout and target (RNA) knockdown methodologies. If structural and ...
[40]
Target (In)Validation: A Critical, Sometimes Unheralded, Role of ...
May 21, 2015 · An important role of medicinal chemistry is to generate molecules that enable the most reliable conclusions from a preclinical target validation/invalidation ...
[41]
Genetic-Driven Druggable Target Identification and Validation - PMC
Here, we discuss opportunities and challenges, and infer criteria for the optimal use of genetic findings in the drug discovery pipeline.
[42]
Model‐informed target identification and validation through ... - NIH
Feb 17, 2022 · The method combines QSP and NBA for target identification, where NBA drives initial identification and QSP validates it. NBA identifies targets ...<|separator|>
[43]
Utilization of phase I studies for target validation of first-in-class drugs
Oct 9, 2024 · This review discusses the growing importance of target validation within phase I (P1) trials as a new trend in drug development.
[44]
Structure-based assessment and druggability classification of ...
May 13, 2022 · This work proposes a PPI-specific classification scheme that will assist researchers in assessing the druggability and identifying inhibitors of the PPI ...Introduction · Druggability Of Apo... · Methods
[45]
Data-driven analysis and druggability assessment methods to ...
The existence of established drug targets in the same protein family is a good indicator of druggability. If no drugs have approved that target within the ...Review · 2. Microarray Experiments... · 3. Assessing The...
[46]
Approaches to target tractability assessment – a practical perspective
Target tractability (a.k.a. ligandability). The likelihood of identifying a modulator that interacts effectively with the target/domain (or pathway).
[47]
Ligandability and druggability assessment via machine learning
Jun 4, 2023 · The ligandability assessment of a pocket, and hence for continuation the druggability assessment of an entire target, can be addressed either as ...Abstract · INTRODUCTION · MACHINE LEARNING... · AUTHOR CONTRIBUTIONS
[48]
TTD: Therapeutic Target Database describing target druggability ...
Sep 15, 2023 · The druggability of a target refers to the likelihood of target being effectively modulated by drug-like agents with various evaluation methods ...Introduction · Factual Content And Data... · Conclusion And Perspectives
[49]
Principles of early drug discovery - PMC - PubMed Central
This review will look at key preclinical stages of the drug discovery process, from initial target identification and validation, through assay development.
[50]
The Process of Structure-Based Drug Design - ScienceDirect.com
This review summarizes the process of structure-based drug design and includes, primarily, the choice of a target, the evaluation of a structure of that target ...
[51]
Fragment-Based Drug Design: From Then until Now, and Toward ...
Feb 24, 2025 · Fragment-based drug design (FBDD) has emerged as a powerful strategy in drug discovery, offering a complementary approach to traditional high-throughput ...Abstract · Subjects · Figure 2
[52]
Structure-Based Virtual Screening for Drug Discovery
In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance ...
[53]
(PDF) Drug Discovery Paradigms: Target-Based Drug Discovery
This chapter describes the main concepts related to target-based drug discovery, including two key steps—target and binding site identification ...<|separator|>
[54]
Phenotypic vs. target-based drug discovery for first-in-class medicines
Target-based drug discovery is hypothesis-driven, while phenotypic relies on phenotypic measures of response. Phenotypic approaches have been more successful ...Missing: rates | Show results with:rates
[55]
Past, present, and future of Bcr-Abl inhibitors: from chemical ...
Jun 20, 2018 · Bcr-Abl inhibitors paved the way of targeted therapy epoch. Imatinib was the first tyrosine kinase inhibitor to be discovered with high ...
[56]
The development of imatinib as a therapeutic agent for chronic ...
Studies in BCR-ABL+ cell lines treated with imatinib in combination with cytotoxic agents suggest that adhesion to integrins inhibits apoptosis, even in the ...
[57]
Response and Resistance to BCR-ABL1-Targeted Therapies - PMC
The development of the tyrosine kinase inhibitor (TKI) imatinib allows patients with CML to experience near-normal life expectancy. Specific point mutations ...
[58]
Conformational diversity and protein–protein interfaces in drug ...
Jan 12, 2024 · Saquinavir binds to ERBB3 with a lower (better) energy. Saquinavir was approved in 1995, being the first HIV protease inhibitor approved by FDA.
[59]
Protease Inhibitors: Types, How They Work & Side Effects
HIV protease inhibitors · Amprenavir. · Atazanavir. · Darunavir. · Indinavir. · Fosamprenavir. · Lopinavir. · Nelfinavir. · Ritonavir.
[60]
Selection of HIV-1 for resistance to fifth-generation protease ... - eLife
Mar 15, 2023 · Darunavir (DRV) is exceptional among potent HIV-1 protease inhibitors (PIs) in high drug concentrations that are achieved in vivo. Little is ...
[61]
A momentous progress update: epidermal growth factor receptor ...
Jul 7, 2025 · Afatinib, canertinib, pelitinib, dacomitinib, and neratinib are durable second-generation EGFR inhibitors that target additional EGFR mutations ...
[62]
ChEMBL - EMBL-EBI
ChEMBL is a manually curated database of bioactive molecules with drug-like properties. It brings together chemical, bioactivity and genomic data
[63]
ChEMBL Database in 2023: a drug discovery platform spanning ...
Nov 2, 2023 · To date, ChEMBL contains bioactivity data covering all stages of the drug discovery process. As depicted in Figure 2, patent data from ...<|separator|>
[64]
Binding Database Home
The first public molecular recognition database, BindingDB supports research, education and practice in drug discovery, pharmacology and related fields.Targets/Compound · Find my Compound's Targets · BindingDB · About
[65]
DrugBank Online | Database for Drug and Drug Target Info
Access the world's pharmaceutical knowledge database. Information on drugs, drug targets, and more, used by researchers and health professionals globally.
[66]
Therapeutic target database update 2012: a resource for facilitating ...
Internet resources such as Therapeutic Target Database (TTD) (14,15) and DrugBank (16) provide comprehensive information about the targets and drugs in ...
[67]
Drug Discovery Websites and Databases - Drug Hunter
Sep 9, 2024 · The IUPHAR/BPS Guide to Pharmacology is an excellent reference website containing a searchable database of drug targets, ligands, and diseases.
[68]
HCDT 2.0: A Highly Confident Drug-Target Database for ... - Nature
Apr 25, 2025 · Drug-target interactions constitute the fundamental basis for understanding drug action mechanisms and advancing therapeutic discovery.Data Filtering · Drug-Target Classification · Drug-Rnas
[69]
GETdb: A comprehensive database for genetic and evolutionary ...
Apr 3, 2024 · GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to ...
[70]
In silico Methods for Identification of Potential Therapeutic Targets
Nov 26, 2021 · Discovering potential therapeutic targets from the proteins encoded by essential genes can refine the search scope of therapeutic targets.
[71]
Identification of genetic biomarkers, drug targets and agents for ...
Nov 4, 2023 · In this paper we used integrated bioinformatics approaches (such as, gene ontology (GO) and KEGG pathway enrichment analysis, molecular docking, ...
[72]
https://pubsapp.acs.org/subscribe/archive/ci/31/i06/html/06swindells.html
[73]
e-TSN: an interactive visual exploration platform for target–disease ...
Nov 8, 2022 · The interface allows one to rank targets using two parameters, including the significance and novelty scores based on biometrics in the scatter ...
[74]
The role and application of bioinformatics techniques and tools in ...
This paper summarizes bioinformatics technologies and tools in drug research and development and their roles and applications in drug research and development.
[75]
Accurate structure prediction of biomolecular interactions ... - Nature
May 8, 2024 · Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes.Nobel Prize in Chemistry 2024 · Nature Machine Intelligence
[76]
AlphaFold 3 predicts the structure and interactions of all of life's ...
May 8, 2024 · Our new AI model AlphaFold 3 can predict the structure and interactions of all life's molecules with unprecedented accuracy.
[77]
AlphaFold3 in Drug Discovery: A Comprehensive Assessment of ...
Apr 8, 2025 · AF3 excels at predicting static protein-ligand interactions with minimal conformational changes, significantly outperforming traditional docking methods in ...
[78]
AI-Driven Drug Discovery: A Comprehensive Review | ACS Omega
Jun 6, 2025 · This comprehensive review critically analyzes recent advancements (2019–2024) in AI/ML methodologies across the entire drug discovery pipeline.Methods · Theoretical Framework · Review of Findings and... · Conclusion
[79]
Recent computational advances in the identification of cryptic ...
Several new methods and algorithms are being used for the detection and analysis of cryptic sites in target protein molecules. These can be broadly divided in ...
[80]
canSAR 2024—an update to the public drug discovery ...
Nov 13, 2024 · We highlight recent enhancements to our structural ligandability assessment, chemical standardization and registration processes, and overall ...
[81]
AI system predicts protein fragments that can bind to or inhibit a target
Feb 20, 2025 · Researchers developed a computational method, FragFold, to systematically predict which protein fragments may inhibit a target protein's function.
[82]
Data-driven analysis and druggability assessment methods to ... - NIH
Nov 24, 2022 · Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue. 1.
[83]
Improving the odds of drug development success through human ...
Dec 11, 2019 · Since clinical phase drug development failure due to incorrect target specification accounts for around two in every three late-stage failures, ...
[84]
In the early stages, 97% of drug development fails. - CancerAppy
Oct 7, 2022 · Because the highest level of risk pertains precisely to the early stages, from target identification to pre-clinical development, where failures ...
[85]
Opportunities and challenges in phenotypic drug discovery - Nature
Jul 7, 2017 · This article focuses on the lessons learned by researchers engaged in PDD in the pharmaceutical industry and considers the impact of 'omics' knowledge.
[86]
Phenotypic Drug Discovery: Recent successes, lessons learned and ...
Jun 1, 2023 · In our experience, phenotypic screening using a chemical library containing highly selective legacy compounds from target-based drug discovery ...
[87]
[PDF] Phenotypic vs. Target-Based Drug Discovery for First-in-Class ...
... drugs, whereas target-based screening was the most successful for follower drugs during the period of this analysis. The total number of medicines.
[88]
Screening Strategies and Methods for Better Off-Target Liability ...
Arguably, the most common method for predicting off-target liability is to test compounds of interest in an in vitro panel of ligand binding assays. To do so, ...
[89]
In silico off-target profiling for enhanced drug safety assessment
4. Conclusions. Off-target interactions frequently occur with drug usage and are a major cause of drug side effects and candidate failure during drug discovery ...
[90]
Pharmacogenomics of off‐target adverse drug reactions - PMC
Off‐target ADRs may or may not be associated with immunological memory, although they can manifest with a variety of shared clinical features, including ...
[91]
Adverse Drug Reactions: The benefits of data mining - eLife
Aug 16, 2017 · It has been estimated that serious versions of adverse drug reactions occur in over two million patients per year in the US, with 100,000 of ...
[92]
Off-target based drug repurposing in cancer - PMC - NIH
Crizotinib arguably represents one of the most powerful examples of the transformative potential of off-target based drug repurposing as this TKI was ...
[93]
Novel Computational Approach to Predict Off-Target Interactions for ...
Jul 16, 2019 · Undesired off-target interactions are often not detected using current drug discovery assays, such as experimental polypharmacological screens.Missing: concerns | Show results with:concerns
[94]
Why 90% of clinical drug development fails and how to improve it?
Despite this validated effort, the overall success rate of clinical drug development remains low at 10%–15%5, 6, 7. Such persistent high failure rate raises ...
[95]
Targeted protein degradation: advances in drug discovery and ...
Nov 6, 2024 · This review thoroughly explores the mechanisms and clinical advancements of TPD, from its initial conceptualization to practical implementation.<|separator|>
[96]
Recent advances in targeting the “undruggable” proteins: from drug ...
Sep 6, 2023 · In this review, we focus on the recent development of drug discovery targeting “undruggable” proteins and their application in clinic.
[97]
Targeting CDKs in cancer therapy: advances in PROTACs ... - Nature
Jun 28, 2025 · This paper highlights the clinical and preclinical developments of PROTACs and molecular glues, investigates the current CDK-targeting therapeutic landscape,
[98]
Ensemble docking for intrinsically disordered proteins - bioRxiv
Jan 26, 2025 · Researchers at Dartmouth College have explored new methods for targeting intrinsically disordered proteins (IDPs) in drug discovery. Traditional ...
[99]
Design of intrinsically disordered region binding proteins - Science
Jul 17, 2025 · Our computational design pipeline enables the design of binding proteins to arbitrary disordered peptides and proteins. Although targeting ...<|separator|>
[100]
Taking Aim at the Undruggable - ASCO Publications
May 14, 2021 · Recent successes in targeting KRAS G12C and HRAS represent significant advances in drugging “the undruggable” and provide proof of concept.
[101]
Advances in Integrated Multi-omics Analysis for Drug-Target ... - MDPI
This review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification.Missing: peer- | Show results with:peer-
[102]
Integrating artificial intelligence in drug discovery and early drug ...
Mar 14, 2025 · Artificial intelligence can transform drug discovery and early drug development. AI can facilitate target identification with multiomics data ...
[103]
[PDF] AI-driven drug discovery and repurposing using multi-omics for ...
Jun 25, 2025 · Developing methods to holistically combine genomics, epigenomics, transcriptomics, proteomics, and metabolomics data is a key frontier for ...
[104]
AI-driven multi-omics integration for multi-scale predictive modeling ...
This framework will integrate multi-omics data across biological levels, organism hierarchies, and species to predict genotype-environment-phenotype ...
[105]
https://ieeexplore.ieee.org/document/10894291
[106]
Application of artificial intelligence large language models in drug ...
Jul 8, 2025 · Notably, single-cell multi-omics language models significantly broaden the scope of target identification by integrating multi-dimensional data, ...1 Introduction · 4.1 Rna Structure Prediction · 4.2 Gene Expression Analysis
[107]
https://www.cell.com/trends/pharmacological-sciences/fulltext/S0165-6147(23)00137-2
[108]
Integrating artificial intelligence into small molecule development for ...
Oct 1, 2025 · When integrated with AI, these high-throughput datasets empower predictive models that can accelerate biomarker discovery, drug repurposing, and ...
[109]
Advances in Integrated Multi-omics Analysis for Drug-Target ...
This review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification.
[110]
Machine learning enhances biomarker discovery: From multi- omics ...
Aug 25, 2025 · Observations: Machine learning and deep learning have proven effective in biomarker discovery by integrating diverse and high-volume data types, ...