Cheminformatics
Cheminformatics, also known as chemoinformatics, is an interdisciplinary field that integrates principles from chemistry, computer science, and information science to manage, analyze, and interpret large volumes of chemical data, enabling the storage, retrieval, and prediction of molecular properties and behaviors.[1][2] This discipline focuses on representing chemical structures in digital formats, such as graphs or fingerprints, to facilitate tasks like similarity searching, virtual screening, and quantitative structure-activity relationship (QSAR) modeling.[3][4] The term "cheminformatics" was coined in 1998 to describe the application of informatics techniques to chemical problems, building on earlier computational chemistry methods that date back to the mid-20th century.[2] It gained prominence in the pharmaceutical industry during the late 1990s and early 2000s, driven by the explosion of chemical databases and the need for efficient data handling in drug discovery pipelines.[2][4] Key components include chemical database management systems, algorithms for molecular descriptor generation, and machine learning approaches for property prediction, all of which address the vast chemical space estimated to contain over 10^60 possible molecules.[3][1] In practice, cheminformatics plays a pivotal role in drug design by supporting virtual high-throughput screening of compound libraries, identifying potential leads through pharmacophore modeling, and optimizing absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles using rules like Lipinski's Rule of Five.[4][2] Beyond pharmaceuticals, it extends to materials science for polymer property prediction and agrochemical development, where it aids in archiving reaction pathways and extracting trends from spectroscopic data.[1] Challenges in the field include standardizing representations of complex structures like stereoisomers and tautomers, as well as integrating heterogeneous data sources such as PubChem, which holds over 119 million compounds as of 2025.[3][5] Overall, cheminformatics enhances decision-making in chemical research by transforming raw data into actionable insights, fostering collaboration across disciplines.[3][4]History
Origins and Early Developments
The origins of cheminformatics trace back to the late 1950s, when early computational efforts focused on storing and searching chemical structures in digital databases. In 1957, Louis C. Ray and Russell A. Kirsch at the National Bureau of Standards developed the first algorithm for substructure searching, treating chemical structures as labeled graphs to enable automated retrieval of molecular records from punched-card systems.[6] This work laid the groundwork for handling chemical data computationally, addressing the growing volume of chemical literature that manual indexing could no longer manage efficiently.[7] During the 1960s, the field advanced through pioneering applications in structure elucidation, property prediction, and synthesis planning, driven by the advent of accessible computing. The DENDRAL project, initiated in 1965 by Joshua Lederberg, Edward Feigenbaum, and Carl Djerassi at Stanford University, produced the first expert system for inferring molecular structures from mass spectrometry data, employing heuristic rules to generate and evaluate possible structures.[3] Concurrently, Corwin Hansch and Toshio Fujita introduced quantitative structure-activity relationship (QSAR) analysis in 1964, correlating biological activity with physicochemical descriptors using linear regression models, which formalized the quantitative prediction of chemical properties. That same year, the Chemical Abstracts Service (CAS) launched the CAS REGISTRY system under a National Science Foundation contract, creating a unique numbering scheme for chemical substances to support indexing and avoid duplication in abstracts.[8] The late 1960s and 1970s saw further consolidation with tools for synthetic design and database expansion. In 1969, E.J. Corey and W. Todd Wipke published the first computer-assisted organic synthesis system (OCSS), which used graph-based retrosynthetic analysis to generate pathways for complex molecules, marking a shift toward automated planning in organic chemistry.[9] The establishment of the Journal of Chemical Documentation in 1961 (later renamed the Journal of Chemical Information and Computer Sciences in 1975) provided a dedicated forum for these emerging methods, reflecting the field's transition from ad hoc computations to a structured discipline. By the 1980s, these foundations enabled widespread adoption of substructure search systems like DARC and MACCS, though the term "cheminformatics" would not be coined until 1998.[10]Evolution and Modern Milestones
The evolution of cheminformatics built upon its early foundations in chemical documentation and computational searching, transitioning in the early 1960s and 1970s toward quantitative structure-activity relationship (QSAR) modeling and molecular similarity techniques. In 1962, Corwin Hansch and colleagues introduced Hansch analysis, a foundational QSAR method using multiple linear regression to correlate molecular descriptors with biological activity, marking a shift toward predictive modeling in drug design. By 1965, H.L. Morgan's canonicalization algorithm enabled unique graph-based representations of molecules, facilitating the Chemical Abstracts Service (CAS) Registry System for systematic chemical indexing.[11] The 1970s saw further advancements in similarity searching, with Adamson and Bush's 1973 method employing fragment bit-strings to compare molecular structures, influencing library design in pharmaceutical research.[12] The 1980s and 1990s accelerated progress with three-dimensional (3D) modeling and combinatorial chemistry's rise. In 1988, Richard Cramer's Comparative Molecular Field Analysis (CoMFA) pioneered 3D QSAR by aligning molecules in a lattice to compute steric and electrostatic fields, revolutionizing ligand-based drug design.[13] The term "chemoinformatics" was coined in 1998 by Frank K. Brown, emphasizing its role in managing chemical data for drug discovery. Christopher Lipinski's 1997 "Rule of Five" provided guidelines for drug-likeness based on physicochemical properties, guiding compound selection in high-throughput screening.[14] The decade's explosion in combinatorial libraries necessitated diversity analysis, with methods like those from David Weininger advancing substructure searching via SMILES notation. Entering the 2000s, open-source tools and public databases transformed cheminformatics into a collaborative field. The Chemistry Development Kit (CDK) launched in 2000, offering modular libraries for molecular manipulation and cheminformatics workflows. Open Babel (2001) and RDKit (2003) followed, enabling seamless file format interconversion and descriptor calculations, respectively, and democratizing access for researchers.[15] PubChem's 2004 debut as a free repository has grown to over 100 million compounds as of 2024, spurring data-driven discoveries, while ChEMBL (2010) integrated bioactivity data from literature, supporting virtual screening.[16] The International Chemical Identifier (InChI), standardized in 2005, ensured unambiguous structure representation across systems.[17] Modern milestones since the 2010s emphasize artificial intelligence (AI) and machine learning (ML) integration, addressing big data challenges in drug discovery. The adoption of FAIR (Findable, Accessible, Interoperable, Reusable) principles in 2016 enhanced data sharing, exemplified by initiatives like NFDI4Chem. In 2018, generative adversarial networks (GANs) were applied to de novo molecule design, enabling exploration of vast chemical spaces beyond traditional enumeration. By the early 2020s, graph neural networks (GNNs) improved molecular property prediction, as in the 2017 Message Passing Neural Network (MPNN) framework for reaction prediction.[18] Recent advancements include AI-driven ultra-large virtual libraries, with models from 2023 generating billions of synthesizable compounds for target identification. These developments, rooted in open science movements like the Blue Obelisk, have accelerated hit-to-lead optimization, reducing drug discovery timelines. In 2024, large language models began integrating into cheminformatics for automated chemical reasoning and synthesis planning.[19]Fundamentals
Definition and Scope
Cheminformatics, also known as chemoinformatics, is defined as the application of informatics methods to address chemical problems, particularly through the manipulation and analysis of structural chemical information. The term was introduced in 1998 by Frank K. Brown, who described it as "the mixing of those information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug lead identification and organization." This field emphasizes the use of computational techniques to handle chemical data, distinguishing it from broader computational chemistry by its focus on information management rather than purely physical simulations.[20] The scope of cheminformatics encompasses the collection, storage, retrieval, analysis, and visualization of chemical data, including molecular structures, properties, spectra, and bioactivities. It involves representing chemical entities in digital formats suitable for database management and machine processing, enabling tasks such as similarity searching and property prediction. Core activities include developing algorithms for substructure matching and quantitative structure-activity relationship (QSAR) modeling, which integrate chemical structures with biological or physicochemical outcomes to support decision-making in research. This scope extends beyond small molecules to polymers and materials, but remains centered on information science applications to chemistry.[21][3] Originally emerging to accelerate drug discovery by streamlining data handling in pharmaceutical pipelines, cheminformatics now intersects with multiple disciplines, including bioinformatics and materials science, to facilitate virtual screening, compound library design, and predictive toxicology. Its boundaries are fluid, overlapping with computational chemistry in molecular modeling while prioritizing scalable data integration over quantum-level calculations. By providing open standards for chemical data interchange, such as SMILES and InChI notations, the field promotes interoperability across databases like PubChem, which contains over 119 million compounds as of 2025.[5] This interdisciplinary approach enhances efficiency in handling vast chemical datasets, reducing experimental costs and time in discovery processes.[20][21][3]Interdisciplinary Nature
Cheminformatics is inherently interdisciplinary, bridging chemistry with computer science and data analysis to manage and interpret chemical information. At its core, it applies computational methods to chemical structures and properties, enabling chemists to leverage algorithms for data processing and modeling. This integration draws from information science for database design and retrieval, while incorporating statistical techniques to derive meaningful insights from large datasets. Such convergence allows for the development of tools that address complex chemical problems beyond traditional experimental approaches.[22] The field intersects with biology and pharmacology, particularly in drug discovery, where chemical data is fused with biological targets to predict molecular interactions and therapeutic outcomes. For instance, cheminformatics facilitates systems chemical biology by linking small molecules to broader biological networks, enhancing applications in high-throughput screening and personalized medicine. In materials science and environmental chemistry, it combines chemical expertise with data analytics to model properties like toxicity or reactivity, requiring collaboration among chemists, biologists, and computational experts. These intersections underscore cheminformatics' role in translating raw chemical data into actionable knowledge across scientific domains.[23][22] Open-source tools and databases further amplify this interdisciplinary character by enabling seamless data sharing and joint research efforts. Resources like PubChem, with millions of molecular records, allow chemists to pose domain-specific questions while computer scientists provide scalable algorithms for analysis, fostering innovations in areas such as ontology-based data integration via Semantic Web technologies. This collaborative framework not only accelerates discovery but also promotes accessibility, uniting diverse expertise to tackle multifaceted challenges in chemical research.[3][23]Chemical Data Representation
Molecular Structures and Descriptors
Molecular structures in cheminformatics are primarily represented using symbolic notations and graph-based models to encode the connectivity and stereochemistry of atoms in a molecule. The Simplified Molecular Input Line Entry System (SMILES), introduced in 1988, is a widely adopted string-based representation that uses linear notation to describe molecular topology, such as C1CC1 for cyclopropane.[24] These representations facilitate computational processing for tasks like similarity searching and property prediction. Graph representations model molecules as nodes (atoms) connected by edges (bonds), enabling the application of graph theory and machine learning algorithms, such as graph neural networks, to capture structural features.[25] Molecular descriptors are numerical values derived from these structural representations, quantifying physicochemical, topological, or geometric properties to enable quantitative structure-activity relationship (QSAR) modeling and virtual screening. They transform qualitative chemical information into quantifiable features, with hundreds reported in the literature, ranging from simple counts to complex multidimensional metrics.[26] Descriptors are classified by dimensionality based on the structural information required for their calculation: 0D (no structural information beyond composition), 1D (linear sequences), 2D (topological connectivity), and 3D (spatial geometry).[27] This classification, formalized in seminal works, aids in selecting appropriate descriptors for specific applications like drug discovery.[28] 0D descriptors, also known as constitutional descriptors, capture bulk molecular properties without considering atom connections, such as molecular weight, atom counts (e.g., number of carbon or hydrogen atoms), and functional group frequencies. These are computationally inexpensive and serve as baseline features in QSAR models, often correlating with solubility or lipophilicity.[29] For instance, the number of hydrogen bond donors is a key 0D descriptor used in Lipinski's Rule of Five for drug-likeness assessment.[24] 1D and 2D descriptors incorporate connectivity and topology. 1D descriptors include fragment counts, like the number of aromatic rings or rotatable bonds, derived from linear molecular formulas. 2D descriptors, such as topological indices, quantify graph invariants; the Wiener index, introduced in 1947, measures molecular branching by summing the shortest path lengths between all atom pairs.[30] Other examples include the Balaban index for graph balance and molecular fingerprints like Extended-Connectivity Fingerprints (ECFP), which encode substructural patterns as bit vectors for similarity computations. These are essential for database searching and diversity analysis in combinatorial chemistry.[29] 3D descriptors require conformational information and account for spatial arrangement, including shape and electrostatic properties. Examples encompass surface-area metrics (e.g., solvent-accessible surface area), quantum-chemical descriptors like HOMO/LUMO energies from density functional theory, and pharmacophore-based features such as those from Volsurf software, which map interaction fields.[31] These enable predictions of binding affinity in protein-ligand interactions but demand conformer generation, increasing computational cost. Higher-dimensional descriptors (4D–6D) extend this by incorporating dynamic aspects, like multiple conformations or time-dependent simulations, as in GRID molecular interaction fields developed in 1985.[31] The Handbook of Molecular Descriptors by Todeschini and Consonni (2000) provides a comprehensive taxonomy, emphasizing that descriptor selection should be guided by performance evaluation rather than intuition, with applications in virtual screening where fingerprints like MACCS keys have demonstrated high efficacy in identifying active compounds. Recent advances integrate descriptors with machine learning, such as using ECFP in random forests for activity prediction, achieving accuracies over 80% in benchmark datasets for kinase inhibitors.[25]Graph and Vector Representations
In cheminformatics, molecules are commonly represented as graphs to capture their structural topology, where atoms serve as nodes and chemical bonds as edges. This graph-based approach encodes the connectivity and valence of atoms, often augmented with node features such as atomic number, hybridization, and degree, as well as edge features like bond order and stereochemistry. The adjacency matrix defines the graph's structure, while feature matrices provide additional chemical attributes, enabling algorithms to process molecules as relational data suitable for tasks like property prediction and similarity searching. Such representations preserve the inherent graph-like nature of molecular structures, facilitating the application of graph theory and machine learning techniques.[32] Seminal developments in graph representations trace back to early efforts in computational chemistry, with Harold L. Morgan's 1965 work introducing unique machine-readable descriptions of molecular graphs via canonical labeling algorithms, which laid the foundation for systematic enumeration of substructures. Modern implementations, such as those in the RDKit toolkit, build on this by generating attributed molecular graphs from formats like SMILES (Simplified Molecular Input Line Entry System), introduced by Weininger in 1988 for linear notation of graph structures. These graphs are particularly valuable in drug discovery for modeling interactions in protein-ligand complexes and enabling de novo molecule generation through graph editing operations. For 3D extensions, spatial coordinates are incorporated as node positions, enhancing representations for conformational analysis, though 2D graphs remain dominant due to their simplicity and sufficiency for many topological tasks.[33][34] Vector representations transform molecular graphs or structures into fixed-length numerical vectors, often called molecular descriptors or fingerprints, to enable efficient computational processing and machine learning integration. Structural fingerprints, such as the MACCS keys (166 predefined substructure bits) developed in the 1990s, provide binary vectors indicating the presence of specific functional groups, while topological fingerprints like Daylight fingerprints use path-based hashing to encode connectivity up to a defined radius. A widely adopted method is the Extended-Connectivity Fingerprint (ECFP), or Morgan fingerprint, introduced by Rogers and Hahn in 2010, which iteratively hashes circular neighborhoods around atoms to produce dense bit vectors (typically 1024–4096 bits) that capture substructural features with low collision rates. These vectors facilitate similarity metrics like Tanimoto coefficients for virtual screening.[35] Advanced vector representations leverage graph neural networks (GNNs) to learn continuous embeddings from molecular graphs, embedding high-dimensional structural information into low-dimensional latent spaces. Message Passing Neural Networks (MPNNs), pioneered by Gilmer et al. in 2017, propagate information across graph edges to generate node and graph-level vectors, outperforming traditional fingerprints in predictive accuracy for properties like solubility and toxicity on benchmarks such as QM9 and MoleculeNet datasets. Self-supervised pretraining on large chemical corpora further refines these embeddings, as in the GROVER model by Rong et al. (2020), which uses motif prediction to yield transferable vectors for downstream tasks. Unlike fixed fingerprints, GNN-derived vectors adapt to specific datasets, offering superior expressiveness for complex cheminformatics applications while maintaining computational tractability.[36]Storage and Management
Chemical Databases and Repositories
Chemical databases and repositories serve as foundational infrastructure in cheminformatics, enabling the systematic storage, retrieval, and analysis of vast quantities of chemical structures, properties, and associated biological data. These resources facilitate tasks such as similarity searching, virtual screening, and predictive modeling by providing standardized access to molecular information from diverse sources, including experimental measurements, patents, and literature. In cheminformatics workflows, they support the integration of chemical data with computational tools, promoting reproducibility and collaboration in drug discovery and materials science.[22] One of the most prominent repositories is PubChem, managed by the National Center for Biotechnology Information (NCBI) at the U.S. National Institutes of Health (NIH). It aggregates chemical data from over 1,000 sources, offering freely accessible information on structures, physical properties, biological activities, safety data, patents, and literature citations. As of 2025, PubChem contains approximately 119 million unique compounds and 322 million substances, making it the largest open chemical database globally. Its role in cheminformatics includes enabling structure-based searches and integration with bioinformatics tools for high-throughput analysis.[16] ChEMBL, maintained by the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI), focuses on bioactive molecules with drug-like properties, curating data on chemical structures, bioactivities, and genomic targets to aid computational drug discovery. The database integrates manually extracted information from scientific literature, patents, and deposited datasets, supporting applications in quantitative structure-activity relationship (QSAR) modeling and machine learning for target prediction. In its 2023 release (ChEMBL 33), it encompassed over 2.4 million unique compounds, more than 20.3 million bioactivity measurements across 17,000 targets, and data from 1.6 million assays; by 2025 (ChEMBL 36), the compound count exceeded 2.8 million with 17,803 targets. Seminal developments in ChEMBL have emphasized its evolution as a platform for translating genomic data into therapeutic insights.[37][38] ChemSpider, developed and hosted by the Royal Society of Chemistry (RSC), provides a free chemical structure database that aggregates data from hundreds of sources, emphasizing spectral data, synthetic routes, and property predictions. It supports text and substructure searches over more than 130 million structures, serving as a key resource for compound identification and verification in cheminformatics pipelines. Launched in 2007, ChemSpider has grown to include experimental properties and annotations, facilitating integration with publishing workflows and semantic web applications.[39][40] For virtual screening, the ZINC database offers a curated collection of commercially available compounds in ready-to-dock formats, prioritizing purchasable molecules for structure-based drug design. Managed by the Shoichet Laboratory at the University of California, San Francisco, ZINC includes over 230 million compounds, with updates ensuring 3D conformer availability and vendor sourcing details. It plays a critical role in cheminformatics by enabling large-scale ligand enumeration and diversity analysis, with its open-access model supporting reproducible virtual screening campaigns.[41][42] Other notable repositories include DrugBank, a bioinformatics and cheminformatics resource combining detailed pharmacological data on over 19,000 drug entries with target interactions, sequences, and pathways, primarily for in silico drug discovery.[43][44] BindingDB curates experimentally determined binding affinities for small molecules and proteins, holding 3.2 million data points across 1.4 million compounds and 11,400 targets, which is essential for affinity-based QSAR and machine learning models.[45][46] Specialized databases like the Cambridge Structural Database (CSD) focus on crystallographic data for over 1.37 million small-molecule crystal structures as of 2025, underpinning conformer generation and property prediction in cheminformatics.[22][47]| Database | Manager/Organization | Primary Focus | Approximate Size (2023–2025) |
|---|---|---|---|
| PubChem | NCBI/NIH | General chemical structures and bioactivities | 119M compounds, 322M substances |
| ChEMBL | EMBL-EBI | Bioactive drug-like molecules and targets | 2.8M compounds, >20M bioactivities |
| ChemSpider | Royal Society of Chemistry | Structure search with properties and spectra | >130M structures |
| ZINC | UCSF Shoichet Lab | Commercially available compounds for screening | >230M purchasable compounds |
| DrugBank | DrugBank Inc. | Drugs, targets, and pharmacological data | >19,000 drugs, comprehensive target info |
| BindingDB | BindingDB Project | Protein-small molecule binding affinities | 1.4M compounds, 3.2M binding data points |
File Formats and Interchange Standards
In cheminformatics, file formats and interchange standards are essential for representing, storing, and exchanging chemical structures, properties, and data across software tools, databases, and research workflows. These formats ensure interoperability by providing standardized ways to encode molecular connectivity, stereochemistry, coordinates, and metadata, facilitating tasks such as database integration, virtual screening, and collaborative drug discovery. Without such standards, data silos would hinder computational chemistry applications, as diverse tools from different vendors often require compatible input/output mechanisms.[48][49] Connection table formats, such as the MDL MOLfile and its multi-molecule extension, the Structure-Data File (SDF), are among the most widely used for small organic molecules. The MOLfile V2000 specification, developed by MDL Information Systems (now part of BIOVIA), organizes data into sections for atom counts, bond counts, atom coordinates, bond connections, and optional properties, allowing representation of 2D or 3D structures with up to 999 atoms and 999 bonds. SDF extends this by concatenating multiple MOLfiles with metadata fields, making it ideal for compound libraries; for example, PubChem distributes millions of compounds in SDF format for bulk download. These formats prioritize simplicity and compatibility, supporting aromaticity and basic stereochemistry, though they lack native handling of isotopes or advanced reactions without extensions.[48] Line notation systems like SMILES (Simplified Molecular Input Line Entry System) offer compact, human-readable representations of molecular topology without coordinates. Introduced by Daylight Chemical Information Systems in 1988, SMILES uses ASCII strings to denote atoms (e.g., 'C' for carbon), bonds (e.g., '=' for double), branches (parentheses), and rings (numbers), with canonicalization algorithms ensuring unique strings for identical structures. The OpenSMILES specification, an open extension ratified in 2016, standardizes features like stereochemistry and aromaticity, enabling seamless parsing in tools like RDKit and Open Babel. SMILES is particularly valued for web transmission and database indexing due to its brevity—for instance, ethanol is simply "CCO"—but it omits 3D geometry unless extended with variants like SMILES+3D.[50][51] For unambiguous identification and interchange, the International Chemical Identifier (InChI) serves as a hashed string standard developed by IUPAC and NIST. Released in 2005 and maintained by the InChI Trust, InChI encodes layered information on connectivity, hydrogen atoms, isotopes, stereochemistry, and tautomers into a non-proprietary string (e.g., InChI=1S/C2H6O/c1-2-3/h3H,2H2,1H3 for ethanol), with an InChIKey hash for compact searching. Unlike format-specific representations, InChI prioritizes canonical uniqueness across software, supporting over 100 million compounds in databases like PubChem, and is recommended for patent documentation and data exchange to avoid ambiguity from vendor-specific formats.[49][52] XML-based standards like Chemical Markup Language (CML) provide a flexible, extensible framework for rich chemical data, including spectra, reactions, and semantics. Initiated in 1998 by the Murray-Rust group and now at version 3, CML uses XML schemas to tag elements such as molecules (<molecule>), atoms (<atom>), bonds, and properties, allowing integration with other XML standards like MathML for equations. It supports validation via online services and dictionaries for controlled vocabularies, making it suitable for publishing and archiving complex datasets in journals; for example, a CML document can embed SMILES alongside 3D coordinates and metadata. CML's strength lies in its interoperability with web technologies, though its verbosity limits use in high-throughput computing compared to binary formats.[53][54]
Other specialized formats complement these for broader applications: the Protein Data Bank (PDB) format, standardized since 1971 by the wwPDB, handles macromolecular structures with atomic coordinates and is widely used in cheminformatics for protein-ligand interactions; the Crystallographic Information File (CIF) from the IUCr encodes crystal structures with symmetry and metadata for materials science. Interchange often relies on conversion tools like Open Babel, which supports over 100 formats, ensuring data flow between ecosystems while preserving fidelity. Adoption of these standards has grown with open-source initiatives, reducing proprietary barriers in global research.