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Druggability

Druggability is the capacity of a biological target, most commonly a protein, to bind small, drug-like molecules with sufficient affinity and specificity to enable therapeutic modulation, thereby facilitating the development of effective small-molecule drugs.^[1] The concept of druggability emerged in the early 2000s as a critical framework in drug discovery to prioritize targets likely to yield viable therapeutics, with Andrew L. Hopkins and Colin R. Groom introducing the term "druggable genome" in 2002 to describe the subset of the human proteome—estimated at more than 3,000 proteins—amenable to binding orally bioavailable small molecules.^[2] This subset has since been refined and expanded through genomic and structural analyses, with contemporary estimates identifying approximately 4,500 genes encoding proteins in the druggable genome, of which fewer than 700 are currently targeted by FDA-approved drugs.^[3] Druggability assessment is essential because undruggable targets contribute to roughly 60% of small-molecule drug discovery project failures, underscoring the need for early evaluation to streamline research and reduce costs.^[1] Key methods for determining druggability include experimental approaches like nuclear magnetic resonance (NMR)-based fragment screening, which measures hit rates in protein binding sites to predict ligandability, and computational tools such as DrugFEATURE, which analyzes physicochemical microenvironments in potential binding pockets with correlations to experimental outcomes (e.g., R² = 0.47 for NMR data).^[1] Physics-based algorithms like SiteMap further evaluate binding site geometry, hydrophobicity, and enclosure to score druggability.^[1] In recent years, initiatives like the National Institutes of Health's Illuminating the Druggable Genome (IDG) Program have advanced this field by integrating multi-omics data into resources such as the Pharos database, which classifies targets into tiers (Tclin for clinically validated, Tdark for understudied) and supports the exploration of challenging protein families including G protein-coupled receptors (GPCRs), ion channels, and kinases.^[3] These efforts have illuminated over 120 previously "dark" proteins and funded more than 100 research projects, generating substantial publications and reagents to broaden the therapeutic landscape.^[3] Overall, druggability continues to evolve with structural biology and machine learning, enabling the identification of novel targets for diseases like cancer and neurodegeneration where traditional enzymes dominate less.^[1]^[3]

Core Concepts

Definition and Scope

Druggability refers to the potential of a biological target, typically a protein, to bind a small-molecule ligand with sufficient affinity to enable therapeutic modulation of its function, often defined by a dissociation constant (Ki) below 10 μM for high-affinity interactions.^[4] This concept emphasizes not only the binding capability but also the target's ability to elicit a selective functional response without undue toxicity, making it a critical filter in early drug discovery stages.^[5] A key criterion for druggability is the presence of a well-defined binding pocket on the target's surface, which can accommodate small molecules for selective inhibition or activation while supporting high-affinity binding. The term "druggability" originated in the early 2000s amid the structural genomics initiatives following the human genome sequencing, which aimed to determine protein structures to identify viable drug targets systematically.^[6] Coined by Hopkins and Groom in their seminal 2002 paper, it was introduced to delineate the subset of the human proteome amenable to small-molecule modulation, initially estimating around 400–500 targets based on sequence and structural homology to known drug-binding proteins; later analyses have refined this to approximately 4,500 proteins.^[5]^[3] These efforts highlighted the need to prioritize targets with inherent binding sites suitable for drug-like compounds, distinguishing the "druggable genome" from the broader proteome.^[7] While primarily applied to proteins, the scope of druggability extends to other biomolecules such as nucleic acids when assessing their potential to interact with small-molecule drugs, though it explicitly focuses on chemical entities rather than biologics like antibodies.^[8] Druggability differs from ligandability, the latter describing a target's capacity to bind any small molecule with measurable affinity, whereas druggability additionally requires the ligand to exhibit drug-like properties, including favorable absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles for clinical advancement.^[9] This distinction ensures that druggable targets support not just binding but viable therapeutic agents.^[5]

Importance in Drug Discovery

Druggability assessment plays a pivotal role in target selection during pharmaceutical research by enabling researchers to prioritize proteins likely to bind small-molecule drugs with sufficient affinity and selectivity, thereby reducing the high failure rates associated with pursuing non-viable targets.^[1] In the pharmaceutical industry, early evaluation of druggability helps focus efforts on targets where hit identification—the discovery of initial active compounds—has a higher likelihood of success, with virtual screening hit rates typically ranging from 1% to 40%, compared to the low hit rates (0.01-0.14%) of traditional high-throughput screening.^[10] This strategic filtering minimizes resource waste in the preclinical phase, where up to 90% of potential drug candidates fail due to inadequate target engagement.^[11] By integrating druggability screening early in the pipeline, pharmaceutical companies can enhance overall efficiency and cut development costs substantially, with estimates suggesting reductions of 20-40% through AI-assisted target identification and validation that avoid investment in intractable proteins.^[12] This approach accelerates progression from target identification to clinical candidates, addressing the escalating R&D expenses that reached an average of $2.23 billion per approved drug as of 2024 amid rising complexity in therapeutic areas.^[13]^[14] Such cost efficiencies are critical as biopharma invests over $300 billion annually in R&D, yet faces persistent attrition challenges.^[15] Beyond immediate pipeline gains, druggability assessments broaden therapeutic opportunities by facilitating the exploration of novel disease pathways, particularly in oncology and neurodegeneration, where traditional enzyme or receptor targets are often exhausted.^[16] For instance, systematic druggability evaluations have been instrumental in advancing kinase inhibitors, such as imatinib for chronic myeloid leukemia, by confirming the presence of suitable binding pockets in kinase domains, leading to transformative outcomes in cancer therapy with improved patient survival rates.^[17] These tools thus support precision medicine efforts, enabling the pursuit of previously challenging targets while referencing strategies for overcoming undruggable ones in specialized contexts.^[18]

Prediction Methods

Precedence-Based Approaches

Precedence-based approaches to druggability prediction rely on empirical analogies drawn from historical data on proteins that have been successfully modulated by small-molecule drugs, inferring the likelihood of druggability for a novel target based on its sequence or functional similarity to established drug targets.^[19] This method assumes that proteins sharing significant homology, such as greater than 30% sequence identity in key domains, are likely to possess conserved binding sites amenable to similar ligand interactions, as seen in kinase families where such thresholds indicate preserved ATP-binding pockets suitable for inhibition.^[20] For instance, a target protein exhibiting homology to members of the G protein-coupled receptor (GPCR) family, which has numerous approved modulators, would be assigned a high druggability score due to the precedent of successful small-molecule targeting within this class.^[21] Central to these approaches are databases that catalog known drug-target interactions, enabling queries for precedents. Key resources include ChEMBL, which provides bioactivity data for over 2.8 million compounds against protein targets,^[22] and DrugBank, containing detailed annotations on approximately 2,000 drug targets with associated ligands.^[19] Protein family databases like Pfam and PROSITE further support functional classification, allowing researchers to group targets by shared domains and assess collective druggability precedents within families such as enzymes or ion channels.^[19] These databases facilitate rapid lookup: for a query protein, similarity searches identify orthologs or homologs with known ligands, with scoring often based on the quantity and quality of documented binders, such as the number of distinct small molecules achieving sub-micromolar affinity.^[23] The typical workflow begins with sequence alignment using tools like BLAST to scan against databases of known drug targets, identifying hits above a predefined similarity threshold (e.g., E-value <10^{-3} for significant homology).^[21] Matches are then evaluated for the presence and diversity of ligands in ChEMBL or DrugBank, culminating in a druggability score derived from the count of unique modulators or their potency profiles; for example, targets with more than five high-affinity ligands from precedents receive elevated rankings.^[19] This process is computationally lightweight, requiring no three-dimensional structural information, and can be applied genome-wide.^[20] These methods offer simplicity and speed, achieving prediction accuracies around 80-85% for orthologous proteins where evolutionary conservation preserves ligand-binding features, as demonstrated in cross-validation studies on human drug targets.^[24] They excel in prioritizing targets within well-characterized families like kinases, where historical data reduces false positives by leveraging proven modifiability.^[19] However, limitations include the inability to identify druggable novel protein folds lacking close homologs and an inherent bias toward overrepresented classes such as enzymes (which comprise over 50% of known targets) at the expense of understudied groups like transcription factors.^[20] This reliance on existing datasets can perpetuate gaps in exploring truly innovative targets.^[19]

Structure-Based Approaches

Structure-based approaches to druggability prediction rely on the three-dimensional architecture of proteins to evaluate the suitability of binding pockets for small-molecule ligands. These methods analyze key physicochemical properties of potential binding sites, such as enclosure (the degree to which the pocket is buried and shielded from solvent), volume (typically requiring >300 Å³ to accommodate drug-like molecules), and hydrophobicity (favoring non-polar environments for high-affinity binding). Well-enclosed, sufficiently large, and hydrophobic pockets are more likely to support potent interactions with small molecules, as they promote van der Waals contacts and minimize desolvation penalties.^[25]^[26] Key tools for this analysis include FTMap, which performs computational mapping of small organic probe molecules to identify binding hot spots on protein surfaces, revealing regions with high ligand affinity potential. FTMap simulates billions of probe-protein interactions to cluster binding poses, prioritizing sites that bind multiple probe types, which correlates with druggability. Complementing this, DoGSiteScorer automates pocket detection using a difference-of-Gaussians filter and assigns druggability scores (ranging 0-1), where scores >0.5 indicate high likelihood of druggability based on integrated descriptors like volume, enclosure, and lipophilicity. A representative metric in these assessments is the pocket enclosure index, which quantifies burial by the ratio of enclosed to exposed surface area, while the druggability index (DI) is often derived as DI = (enclosed volume / total volume) × hydrophobicity factor, emphasizing compact, apolar sites.^[27]^[28]^[26] Illustrative examples highlight the discriminatory power of these approaches: kinase binding pockets, such as those in EGFR, typically exhibit deep, enclosed cavities with volumes exceeding 400 Å³ and high hydrophobicity, rendering them highly druggable and enabling inhibitors like erlotinib. In contrast, protein-protein interaction (PPI) interfaces often feature flat, solvent-exposed surfaces with low enclosure and volumes <200 Å³, making them inherently less druggable without induced-fit changes. A notable success is the anti-apoptotic protein BCL-2, initially deemed undruggable due to its shallow groove, where structure-guided design using NMR-derived structures identified a cryptic hydrophobic pocket, leading to the development of venetoclax, a potent inhibitor approved for leukemia treatment.^[29]^[30]^[31] Recent advances have integrated structure-based methods with AI-driven predictions, particularly AlphaFold models post-2021, enabling druggability assessments for proteins lacking experimental structures. AlphaFold-generated conformations allow pocket detection and scoring on orphan targets, expanding applicability to the ~70% of human proteome without solved structures, as validated in virtual screening pipelines for novel binders. This synergy has accelerated identification of druggable sites in challenging targets like GPCRs.^[32]^[33]

Machine Learning and Other Computational Methods

Machine learning models have emerged as powerful tools for predicting druggability by integrating diverse physicochemical properties of proteins, such as pocket flexibility, electrostatics, and binding site characteristics, to classify targets as druggable without relying solely on experimental screening. These approaches train classifiers on features derived from protein sequences, structures, and annotations to estimate the likelihood of small-molecule binding with sufficient affinity for therapeutic modulation. For instance, ensemble methods combine multiple algorithms to enhance predictive robustness, leveraging features like hydrophobicity profiles and charge distributions to score potential binding sites.^[34]^[35] Key machine learning techniques include random forests and neural networks applied to features such as amino acid composition and evolutionary conservation. Random forests, for example, aggregate decision trees trained on sequence-based descriptors to identify patterns associated with known drug targets, achieving high discriminatory power through feature importance ranking. Neural networks, particularly deep variants, process multidimensional inputs like one-hot encoded sequences or embedding vectors to capture non-linear relationships, as demonstrated in models like DrugMiner, which uses convolutional layers on protein sequences for druggability classification. A notable example is DrugTar, a 2025 model that integrates protein language model embeddings with ontologies via a deep neural network, outperforming prior methods with an area under the curve (AUC) of 0.94 on benchmark datasets.^[34]^[36]^[37] Beyond static features, predictions incorporate dynamic properties from molecular dynamics (MD) simulations, assessing pocket stability and transient conformational changes that influence ligand binding. MD-derived metrics, such as fluctuation amplitudes and solvent accessibility over time, serve as inputs to machine learning classifiers to evaluate how structural flexibility affects druggability, enabling the identification of cryptic pockets that open during simulations. Network analysis complements this by evaluating target essentiality within protein-protein interaction (PPI) networks, where centrality measures like degree and betweenness centrality indicate proteins with high interconnectivity, correlating with drug target prioritization due to their role in critical pathways.^[38]^[39]^[40] Recent advances leverage graph neural networks (GNNs) to model PPI druggability, representing proteins as nodes and interactions as edges to propagate features and predict modularity impacts on target viability. GNNs excel in capturing relational dependencies, such as how disrupting a PPI hub enhances druggability scores for network-disrupting compounds. Integration of multi-omics data further refines holistic scoring by fusing genomics, transcriptomics, and proteomics layers through ensemble models, as in DF-CAGE, which identifies cancer druggable genes with an AUROC of approximately 0.9 by prioritizing pathway perturbations. These developments enable genome-wide assessments, prioritizing targets with synergistic evidence across data modalities.^[41]^[42]^[43] Validation of these models typically involves cross-validation on curated datasets of known drug targets and decoys, such as those derived from ProTargetDB or extended binding site libraries, yielding AUC values exceeding 0.9 for top-performing ensembles like DrugnomeAI. These benchmarks confirm generalizability, with external testing on independent sets demonstrating robust performance in distinguishing druggable from undruggable proteins.^[34]^[36]

Role of Training Data

The quality and composition of training datasets are pivotal in machine learning models for druggability prediction, as they directly determine the models' ability to generalize across diverse protein targets without introducing systematic errors. To mitigate biases toward overrepresented classes, such as G protein-coupled receptors (GPCRs), training sets should encompass a broad range of targets, including at least 500 drugged proteins balanced against non-drugged ones, ensuring representation from various families and structural types.^[44] Imbalanced or narrow datasets can skew predictions, leading to overoptimistic assessments for common targets while underestimating potential for novel ones.^[45] Commonly used datasets, such as PDBbind—which contains over 27,000 protein-ligand complexes with binding affinity data—provide a foundation for training but suffer from significant underrepresentation of undruggable targets.^[46] For instance, intrinsically disordered proteins (IDPs), which constitute about 10% of the proteome, make up less than 10% of entries in such structural databases due to their lack of stable folds, limiting models' exposure to dynamic binding scenarios.^[47] Similarly, sc-PDB and NRDLD datasets, while useful for pocket-level annotations, predominantly feature soluble proteins, exacerbating gaps in coverage for challenging targets. A major bias in these datasets is overfitting to soluble proteins, as membrane proteins—despite comprising around 27% of the human proteome—are underrepresented owing to crystallization difficulties, resulting in skewed training that hampers predictions for lipid-embedded sites.^[48] Early models, trained on such imbalanced sets, often failed on membrane proteins, misclassifying their pockets as non-druggable. To counter this, strategies like data augmentation through homology modeling and transfer learning from structure prediction tools such as AlphaFold have been employed, leveraging predicted structures to enrich datasets with underrepresented classes like IDPs and transmembrane proteins.^[49] These approaches enhance model robustness by simulating conformational diversity without requiring extensive experimental data. Poor training data quality contributes to elevated error rates, with biased models generating 20-30% false positives in druggability assessments, particularly for atypical targets, as seen in pocket detection tools that overpredict ligandable sites in non-orthosteric regions.^[38] Addressing this requires future development of curated, open-access datasets that integrate emerging modalities, such as covalent binders, exemplified by resources like CovalentInDB 2.0, which catalogs thousands of such interactions to expand coverage beyond reversible small-molecule binding.^[50] By 2025 and beyond, incorporating multi-omics data and dynamic simulations into these datasets will be essential for reliable, unbiased predictions across the proteome.

Undruggable Targets

Characteristics and Identification

Undruggable protein targets are primarily characterized by structural features that preclude effective binding of traditional small-molecule drugs, such as the absence of deep, hydrophobic pockets suitable for high-affinity interactions. Protein-protein interaction (PPI) interfaces, for instance, often present flat, expansive surfaces with shallow pockets averaging around 54 Å³ in volume, far below the typical 500 Å³ threshold required for druggable sites that can accommodate small molecules with sufficient potency.^[51]^[52] Additionally, many undruggable targets exhibit intrinsic disorder, lacking stable three-dimensional structures and defined binding pockets; intrinsically disordered proteins (IDPs) like p53 rely on flexible regions for function, complicating ligand design due to transient conformations. Multi-subunit complexes further exacerbate this by distributing interaction sites across dynamic assemblies, where binding often requires modulating large, adaptive interfaces rather than isolated pockets.^[53]^[54]^[55] The prevalence of such targets is substantial, with approximately 85% of the human proteome deemed undruggable under traditional small-molecule criteria, encompassing classes like transcription factors and Ras family proteins that drive diseases such as cancer but lack conventional binding sites. Notable examples include the oncogene KRAS, historically classified as undruggable owing to its smooth, featureless surface devoid of deep pockets for inhibitor binding, and MYC, whose intrinsically disordered regions prevent stable small-molecule engagement. These characteristics highlight why only a fraction of the proteome—primarily enzymes with well-defined active sites—has been successfully targeted to date.^[56]^[57]^[58] Identification of undruggable targets occurs early in drug discovery through computational and experimental assessments focused on surface topology and binding potential. Druggability indices, such as the Dscore from SiteMap analysis, flag sites as undruggable when scores fall below 0.8, indicating low ligand efficiency and poor prospects for high-affinity binding. Hotspot mapping tools, like FTMap, reveal flat PPI surfaces by probing for energetic hotspots, while experimental fragment-based screens assess druggability by testing libraries for hits; failure to yield binders at PPI interfaces or disordered regions confirms undruggability. These methods prioritize targets with viable pockets, allowing deprioritization of the majority of candidates.^[59]^[60]^[19] A key challenge in identification is the risk of false negatives from rigid screening paradigms that overlook cryptic or allosteric sites, which may only form transiently and evade detection in static structures or standard fragment screens. Such oversights can prematurely dismiss potentially modulable targets, underscoring the need for dynamic simulations to capture conformational flexibility.^[61]^[62]

Strategies for Targeting Undruggable Proteins

One prominent strategy for addressing undruggable proteins involves novel modalities that leverage protein degradation pathways or irreversible binding. Proteolysis-targeting chimeras (PROTACs) and other degraders exploit the ubiquitin-proteasome system to induce ubiquitination and subsequent degradation of target proteins lacking well-defined binding pockets. For instance, ARV-471, a PROTAC degrader targeting the estrogen receptor (ER) in breast cancer, has demonstrated potent ER degradation and antitumor activity in preclinical models and advanced to Phase III clinical trials by 2023; as of 2025, its New Drug Application was submitted to the FDA in July following positive Phase 3 results.^[63] Similarly, covalent inhibitors enable targeting of shallow or transient pockets by forming irreversible bonds with specific residues; sotorasib, a covalent KRAS G12C inhibitor, was FDA-approved in 2021 for non-small cell lung cancer (NSCLC), achieving an objective response rate (ORR) of 37% in clinical trials. These approaches expand the chemical space beyond traditional orthosteric inhibitors, allowing engagement of intrinsically disordered or flat protein surfaces. Allosteric modulation represents another key tactic, focusing on distant sites to induce conformational changes that disrupt protein function without directly competing at active sites. This method is particularly effective for enzymes and signaling proteins with dynamic structures. Trametinib, an allosteric MEK inhibitor that indirectly targets RAF-MEK-ERK signaling in melanoma, was approved by the FDA in 2013 and has shown improved progression-free survival when combined with BRAF inhibitors, highlighting the clinical utility of allosteric strategies in oncology. Advanced techniques further enhance targeting of protein-protein interactions (PPIs), which often underlie undruggability due to large, adaptive interfaces. Macrocycles and bivalent ligands offer increased rigidity and multivalency to mimic protein partners; macrocyclic peptides have been designed to inhibit PPIs by stabilizing helical conformations that engage disordered regions, as seen in libraries screening for binders to undruggable targets like BCL-2 family proteins. Bivalent ligands, such as those in PROTAC architectures, bridge target proteins to E3 ligases for degradation, improving selectivity and potency. Complementing these, AI-designed scaffolds accelerate discovery by generating novel chemical libraries; for example, machine learning workflows in 2025 identified potent BCL-xL inhibitors through virtual screening of hybrid scaffolds, reducing on-target toxicity compared to first-generation BH3 mimetics like navitoclax. By 2025, these strategies have propelled over 40 PROTAC candidates and more than 100 targeted protein degradation (TPD) trials into clinical evaluation, focusing on undruggable targets such as KRAS and ER. No PROTACs have received regulatory approval as of 2025, though progress continues. Covalent inhibitors for KRAS include FDA approvals for sotorasib (2021) and adagrasib (2022) in KRAS G12C-mutated NSCLC; fulzerasib received approval in China (2024). Hybrid approaches integrating machine learning predictions with novel chemistry, such as AI-guided optimization of degrader linkers, have enhanced hit rates and selectivity in PPI targeting. A notable case study is the transcription factor MYC, long considered undruggable due to its lack of deep pockets and role in over 70% of cancers. Stapled peptides, which constrain alpha-helical structures via hydrocarbon staples to improve cell permeability and stability, have disrupted MYC-MAX heterodimerization; IDP-121, a stapled peptide targeting MYC, entered clinical trials for relapsed/refractory hematologic malignancies by 2023, showing disruption of MYC-driven transcription in preclinical models. Concurrently, degraders like the PROTAC ARV-825 indirectly reduce MYC levels via BRD4 degradation, while direct MYC degraders such as WBC100 advanced to Phase I trials in 2022, demonstrating tumor regression in MYC-amplified cancers with minimal off-target effects. These combined modalities illustrate the progression from identification of undruggable features to multifaceted therapeutic intervention.

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However, PPIs are considered undruggable targets due to the structural characteristics of the complex interfaces, such as large and highly hydrophobic ...Missing: disorder | Show results with:disorder
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Here, we define “druggability” as a quantitative estimate of binding sites and affinities for a potential drug acting on a specific protein target.Missing: Ki | Show results with:Ki