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ZINC database

The ZINC database, formally known as ZINC Is Not Commercial, is a free, publicly accessible collection of commercially available or synthesizable small molecules curated specifically for in computational . Developed by John J. Irwin and Brian K. Shoichet at the (UCSF), it originated in 2005 as a resource providing approximately 728,000 purchasable compounds in ready-to-dock, three-dimensional (3D) formats with biologically relevant protonation states and conformations. Over time, ZINC has evolved through successive versions, expanding dramatically in scale and functionality to support ligand discovery by structural biologists, medicinal chemists, and computational researchers. Key milestones include ZINC12 (2012), which offered over 35 million compounds sourced from vendor catalogs, emphasizing drug-like properties and efficient querying via web interfaces. ZINC20 (2020) marked a significant advancement, incorporating 1.4 billion enumerated molecules—1.3 billion of which are purchasable—from 310 catalogs across 150 suppliers, and introducing innovative search tools like for rapid similarity matching and Arthor for substructure queries, enabling sublinear-time searches across ultralarge chemical spaces. The most recent iteration, ZINC-22 (2023), further scales to over 37 billion 2D-searchable compounds (with more than 4.5 billion in formats), focusing on make-on-demand libraries up to 29 heavy atoms, organized into tranches by properties such as atom count, , and charge to facilitate scalable and analog exploration. ZINC's core value lies in democratizing access to tangible chemical space, lowering barriers for structure-based screening by providing annotated molecules in formats compatible with popular docking software (e.g., mol2, SDF), alongside tools for property prediction, diversity analysis, and procurement details from suppliers. Unlike exhaustive theoretical libraries, ZINC prioritizes purchasability and synthesizability, ensuring hits from virtual screens can transition efficiently to experimental validation, and it has been instrumental in numerous drug discovery campaigns by enabling the exploration of diverse scaffolds and reducing reliance on proprietary datasets. Freely available via web portals like zinc.docking.org, it continues to update regularly to reflect growing commercial catalogs, underscoring its role as a cornerstone resource in cheminformatics.

Introduction and History

Overview

The ZINC database is a free, curated collection of commercially available small molecules designed primarily for in . It provides researchers with access to purchasable compounds formatted in ready-to-dock, 3D structures to facilitate workflows, ensuring molecules are tangible and orderable from suppliers. This resource targets biologists, chemists, and researchers in the pharmaceutical, , and academic sectors who require high-quality chemical data for discovery and structure-based modeling. The database emphasizes compounds that can be directly procured, bridging the gap between computational predictions and experimental validation in pipelines. As of 2025, ZINC20 offers over 230 million in-stock compounds alongside 750 million searchable analogs, while ZINC-22 expands dramatically to approximately 55 billion molecules, including 54.9 billion in 2D representations and 5.9 billion in , with a strong emphasis on make-on-demand catalogs from vendors like and . This growth in scale, evolving from earlier versions like ZINC15 with over 100 million compounds, reflects the increasing availability of diverse chemical space for screening. The database is supported by the Irwin and Shoichet Laboratories at the , with funding from the National Institute of General Medical Sciences (grant GM71896).

Development History

The ZINC database was launched in late 2004 by John J. Irwin and Brian K. Shoichet in the at the (UCSF) Department of Pharmaceutical Chemistry. Its creation addressed the need for a freely accessible, standardized collection of commercially available small molecules in ready-to-use 3D formats, facilitating structure-based in . The project has been supported since its inception by continuous funding from the National Institute of General Medical Sciences (NIGMS) under grant R01GM071896. The foundational 2005 publication introduced as a database of 727,842 purchasable compounds, each with precomputed conformations and vendor information, emphasizing ease of download and integration into workflows. By 2012, updates enabled continuous catalog refreshes from vendors and the curation of property-based subsets, such as "clean" or "drug-like" collections, to streamline exploration while maintaining over 35 million compounds. The release of 15 marked a significant expansion to over 100 million purchasable molecules in biologically relevant, ready-to-dock formats, incorporating annotations for targets and to enhance discoverability. Subsequent milestones included the 2020 ZINC20 version, which scaled to 1.4 billion searchable compounds by integrating enumerated analogs from purchasable scaffolds, alongside improved search tools for ultralarge chemical spaces. A pivotal evolution occurred with ZINC-22, detailed in a 2022 and 2023 publication, shifting from primarily in-stock catalogs to multi-billion-scale make-on-demand libraries, enabling access to over 54.9 billion enumerated, synthesizable small molecules (as of 2025). This progression underscores ZINC's adaptation to growing chemical availability, with ZINC-22 now supporting analog searching across billions of tangible compounds for modern .

Content and Scope

Compound Coverage

The ZINC database primarily comprises small molecules, typically with molecular weights under 500 , that are commercially purchasable and suitable for ligand discovery in . These include both in-stock compounds available immediately from vendors and make-on-demand molecules that can be synthesized upon request, emphasizing biologically relevant representations such as protomers and tautomers to account for pH-dependent forms. The database prioritizes drug-like and lead-like compounds, with subsets filtered for compliance with (molecular weight ≤ 500 , ≤ 5, hydrogen bond donors ≤ 5, acceptors ≤ 10), alongside fragment-like options for earlier-stage screening. It also incorporates specialized coverage of natural products, which occupy a distinct chemical space recognized by biological targets, and includes compounds with potential covalent reactivity, though these are often filtered for controlled inclusion based on biological relevance. Compounds in are aggregated from catalogs, excluding or non-purchasable entries to ensure for research. Early iterations drew from over 130 catalogs across dozens of suppliers, including in-stock providers like ChemBridge; later versions expanded to 310 catalogs from 150 companies, with major contributions from make-on-demand specialists such as (REAL library), (GalaXi), and Mcule (Ultimate). As of November 2025, the core sources remain four large catalogs— (grown from approximately 34 billion compounds in 2023), (2.5 billion), Mcule (128 million), and a small in-stock set of 4 million from ZINC20—totaling 54.9 billion enumerated molecules. This aggregation ensures broad coverage of tangible chemistry while maintaining filters for properties like (e.g., logP < 4) and reactivity to prioritize viable candidates. Diversity in is achieved through extensive scaffold representation and property distributions, with over 19 million Bemis-Murcko scaffolds in ZINC20 and further expansion in ZINC-22 to over 96 million scaffolds (as of 2023), where higher heavy atom counts (24-25) contribute most to novelty. Subsets emphasize drug-like properties, such as lead-like molecules adhering to (molecular weight ≤ 350 Da, ≤ 4, etc.), comprising 736 million entries in ZINC20, alongside subsets that enhance biological relevance. Unique organizational features include "tranches," groupings by heavy atom count and , which facilitate tracking of purchasability and targeted subset selection for applications like . Filters for , reactivity, and biological annotations further refine diversity, ensuring compounds align with practical synthesis success rates exceeding 85% for on-demand items. The coverage has evolved significantly across versions to scale with computational advances in ligand discovery. Initial releases, such as around 2012, offered about 20 million purchasable molecules focused on in-stock availability. ZINC15 (circa 2015) expanded to over 100 million total, including 13 million in-stock compounds with added make-on-demand options. ZINC20 (2020) introduced analogs and ultra-large-scale , reaching 1.4 billion compounds (1.3 billion purchasable) for enhanced analog searching. ZINC-22 (launched ) initially scaled to 37 billion enumerated entries via massive make-on-demand libraries, with 4.5 billion prepared in formats; as of November 2025, it has grown to 54.9 billion 2D-searchable compounds (5.9 billion in formats) due to ongoing vendor expansions, prioritizing tangible, diverse chemical space over exhaustive inclusion. This progression reflects a shift from catalog aggregation to enumerated, vendor-partnered expansion, enabling billion-scale docking while preserving focus on purchasable, drug-relevant molecules.

Formats and Subsets

The ZINC database offers compounds in multiple file formats tailored to computational workflows, emphasizing usability for and chemical informatics. Ready-to-dock 3D structures are provided in MOL2 and formats, containing low-energy conformers generated using the software to ensure biologically relevant poses. For searching and similarity analysis, 2D representations are available as SMILES strings and InChI notations. Additionally, formats such as PDBQT and DB2 support applications, with all structures including protonated protomers and tautomers selected canonically at physiological (approximately 7.4) using ChemAxon's JChem suite for biological relevance. In terms of scale, ZINC-22 as of November 2025 includes 5.9 billion compounds with precomputed structures and 54.9 billion in , reflecting its expansion from make-on-demand catalogs. By contrast, the preceding ZINC20 version contained 230 million structures, focusing primarily on in-stock purchasable compounds. These representations are organized into tranches by properties such as heavy atom count, (logP), and charge, enabling efficient subset selection. Subsets in ZINC are curated collections that filter the full database by physical properties or thematic criteria, providing static, downloadable versions for reproducible research. Property-based subsets emphasize usability in screening; for example, the "clean" subset excludes reactive or unstable molecules via filters for PAINS and other interference compounds, while "drug-like" adheres to (molecular weight <500 Da, logP <5, etc.), "lead-like" prioritizes smaller, more soluble entities for early discovery, and "fragment-like" targets low-molecular-weight compounds (<300 Da) for fragment-based design. Thematic subsets include CNS-active molecules optimized for blood-brain barrier penetration and PAINS-free collections to minimize false positives in assays. These subsets are versioned with preparation dates and molecule counts, such as the lead-like subset with over 7 million entries in older releases. Organizational features in ZINC-22 further enhance versioning and accessibility through alphabetic generations (a-z), where each letter denotes a distinct or update layer—for instance, generation "g" incorporates legacy ZINC20 in-stock data, while larger ones like "n" draw from expansive make-on-demand sources, with updates continuing into 2025. This structure allows users to track changes and select specific eras of the database, with protomers and tautomers standardized to a single per compound to avoid redundancy in downstream analyses. The preparation pipeline integrates conformer generation via , followed by atomic charge assignment and solvation energy calculations using AMSOL, ensuring high-quality 3D models suitable for direct .

Curation and Maintenance

Curation Process

The curation process for the ZINC database starts with selecting compounds based on verified commercial availability from catalogs provided by major suppliers (e.g., , , Mcule), ensuring synthesizability and purchasability, while prioritizing unique, non-redundant structures identified via InChI keys to avoid duplicates across sources. Compounds unsuitable for , such as those containing metals, , , large peptides, or exceeding certain size limits (e.g., Rule-of-4 for lead-like molecules with molecular weight under 400 g/mol and under 4), are excluded to focus on biologically relevant screening sets. Standardization follows selection, where salts are neutralized to retain the largest component, and tautomers and protomers are resolved into physiologically relevant forms at 7.0 using tools like ChemAxon's JChem to reflect biological conditions. Three-dimensional conformers are then generated for -ready formats (e.g., MOL2, ) using software such as Corina and OpenEye's , ensuring low-energy, diverse poses while removing unstable or reactive molecules like peroxides. This step produces standardized representations in SMILES or InChI, with open-source libraries like RDKit handling property calculations such as and molecular weight. Quality control entails rigorous validation against problematic structures, including flagging Pan-Assay Interference Compounds (PAINS) and covalent warheads that could lead to false positives in assays, alongside removal of non-benign functional groups like thiols or aldehydes. Periodic audits verify ongoing purchasability, guided by the 90/90/90 rule ensuring 90% of catalogs are refreshed every 90 days with 90% of compounds available within three months. Custom scripts integrate vendor data, and the process relies on clusters (up to 1700 cores) to manage multi-billion-scale datasets like ZINC-22's 37 billion molecules without compromising accuracy. Challenges such as ensuring pH-adjusted for biological relevance and scaling curation for make-on-demand libraries are addressed through and automated filtering. Subsets are regenerated quarterly to output updated, curated collections.

Updates and Versions

The ZINC database employs a versioning scheme that reflects its evolution in scale and focus, with major releases introducing expanded compound libraries and improved accessibility. ZINC15, released in 2015, provided approximately 13 million in-stock compounds ready for , emphasizing purchasable molecules in formats from commercial suppliers. ZINC20, launched in 2020, significantly scaled up to over 220 million in-stock compounds, incorporating billions more via make-on-demand while enhancing search capabilities for . ZINC-22, introduced in 2023 following a 2022 , expanded dramatically to over 37 billion total compounds in representations (with over 4.5 billion in ), prioritizing multi-billion-scale enumeration from select vendors. Updates to the core database occur continuously, with weekly additions of new molecules, repairs to existing entries, and synchronization with vendor catalogs to reflect availability changes. Static subsets, such as property-filtered collections for drug-likeness or lead-likeness, are refreshed quarterly to maintain relevance for specific screening workflows. Annual major releases incorporate comprehensive catalog refreshes, ensuring alignment with evolving commercial offerings while preserving for prior versions. From 2023 to 2025, the primary advancement was the ZINC-22 release, which integrated make-on-demand compounds from major vendors including , , and Mcule, enabling access to vast synthetic libraries without physical stockpiling. As of November 2025, no major version updates have occurred since ZINC-22, though ongoing vendor synchronizations continue to refine availability and pricing data across the database. ZINC20 remains in parallel maintenance alongside ZINC-22, specifically to support users focused on in-stock, immediately purchasable compounds.

Access and Tools

Accessing the Database

The ZINC database is freely available for use by researchers worldwide, encompassing both and applications in ligand discovery and . Access is provided through dedicated websites: zinc.docking.org for ZINC20, which hosts over 230 million purchasable compounds in ready-to-dock formats, and cartblanche22.docking.org for ZINC-22, featuring a multi-billion-scale collection of tangible molecules. Licensing terms allow unrestricted access and use of the for , with mandatory attribution to the ZINC database via citations to the originating publications. Redistribution of search results or small subsets is permitted, but sharing major portions of the database requires express written permission from the lead author, Irwin, to prevent unauthorized proliferation. The database is provided "as is," with users assuming responsibility for any errors or quality issues, and no warranties are extended. Bulk downloads are facilitated through HTTP endpoints using tools such as or for subsets in formats including MOL2, SDF, PDBQT, and SMILES, while larger transfers support or for efficiency. Due to the immense scale—ZINC-22 alone includes over 50 billion 2D molecules and ~6 billion structures as of 2025—full database access is not offered as a monolithic file; instead, users navigate or predefined subsets via the . No public exists for comprehensive database queries, though limited subset retrieval is possible through web-based tools.

Interfaces and Search Tools

The ZINC database offers web-based interfaces tailored to its versions for user interaction. For ZINC-22, Cartblanche serves as the main interface, enabling browsing to explore chemical spaces grouped by heavy atom count and , as well as Zinc ID searches for specific compounds. The ZINC20 site provides substance and catalog browsing, allowing users to navigate over 230 million purchasable compounds organized by suppliers like ChemBridge and . Search functionalities support substructure queries via the Arthor engine, which interactively retrieves up to 20,000 matching molecules in seconds from billions of entries. Similarity searches, such as those using extended connectivity fingerprints (ECFP) in ZINC20, identify analogs across large scales, completing in under one minute for millions of compounds. Physical property filters, including net charge and calculated , refine results through tranche-based selection in Cartblanche. Advanced tools extend search capabilities; the 2014 multi-fingerprint browser extension allows nearest-neighbor retrieval in multi-dimensional spaces defined by fingerprints like ECFP4, atom pairs, and path-based descriptors. ZINC integrates with the software suite, supplying compounds in ready-to-dock formats such as MOL2 and PDBQT for seamless workflows. Output from searches includes downloadable hit lists in or SMILES formats, with conformer visualization available for subsets via embedded viewers in Cartblanche. Limitations encompass no API for programmatic access and caps on interactive searches, such as 20,000 results, to prevent overload from heavy usage.

Applications and Impact

Virtual Screening Applications

The ZINC database plays a central role in workflows, particularly in , where it serves as a primary source of libraries for simulations against protein targets. Researchers typically retrieve subsets of ZINC's commercially available compounds—over 230 million purchasable as of 2024, or billions of make-on-demand molecules in ZINC-22—and perform high-throughput to identify potential hits that bind to specific binding pockets. For instance, in ZINC-22, screening of multi-billion-scale make-on-demand libraries has enabled the identification of novel inhibitors for targets like kinases and proteases by prioritizing molecules with favorable scores and drug-like properties. Early applications of demonstrated its utility in discovering inhibitors for , a key target in antiretroviral therapy. In one study, of the ZINC database using a novel 3D-shape descriptor identified diverse scaffolds with micromolar binding affinities, outperforming traditional 2D similarity searches and providing leads for further optimization. Similarly, for beta-secretase (BACE1), an enzyme implicated in , pharmacophore-based of ZINC yielded indole acylguanidine derivatives as potent inhibitors, with values in the nanomolar range, validated through subsequent enzymatic assays. These case studies highlight ZINC's effectiveness in prioritizing purchasable compounds that advance to experimental validation. A key advantage of in is the availability of ready-to-dock 3D structures, which eliminates time-intensive preprocessing and allows rapid of millions of compounds. Additionally, the focus on commercially available molecules facilitates seamless transition from computational hits to wet-lab testing, with details enabling direct for or purchase. integrates seamlessly with popular software, enhancing high-throughput (HTVS) efficiency. For example, it is commonly paired with for flexible receptor-ligand , yielding sub-micromolar leads in campaigns against G-protein-coupled receptors, and with Schrödinger's Glide for precise pose prediction in kinase inhibitor discovery. Such integrations have supported HTVS of over 500 million Rule-of-Four molecules, identifying novel chemotypes with improved potency over baseline libraries. The impact of on is evident in its widespread adoption, with thousands of researchers accessing the database monthly and downloading terabytes of data weekly for ligand discovery projects as of 2020. Seminal works, such as the original publication, underscore its foundational role in structure-based .

Broader Scientific Uses

The database extends its utility beyond primary applications to support quantitative structure-activity relationship (QSAR) studies, where subsets of its compounds are employed to train predictive models for molecular properties such as , , and binding affinity. For instance, pharmacophore-guided QSAR models have been developed using data to screen and prioritize compounds from herbal sources and commercial libraries, enabling the identification of potential inhibitors for enzymes like α-glucosidase. Similarly, substructure-based QSAR-random forest models validated on and datasets have been used to predict drug-like properties associated with targets such as TNF-α, demonstrating the database's role in enhancing model generalizability and accuracy in property prediction tasks. In chemical , facilitates diversity analysis and library design by providing access to billions of commercially viable molecules, allowing researchers to generate focused compound libraries tailored to specific therapeutic needs, including rare diseases. Tools integrated with , such as multi-fingerprint browsers, enable the and traversal of multi-dimensional chemical spaces defined by molecular descriptors, supporting the of scaffolds through nearest-neighbor searches and combinatorial . For example, connectivity and cyclic feature analyses of alongside have defined realistic chemical spaces for lead , aiding in the propagation of bioactive motifs across vast molecular landscapes. ZINC also serves educational purposes in cheminformatics tutorials and as a dataset for evaluating algorithms and other computational tools. In training workflows, subsets like ZINC-250K are preprocessed for models, providing standardized for teaching molecular representation and property prediction. For , ZINC's diverse structures have been used to assess stereoselective methods and large-scale performance, with databases built from its compounds enabling reproducible evaluations of algorithm efficiency on millions of entries. Beyond these, ZINC contributes to natural product analog design through its dedicated catalogs of biogenic and nonhuman metabolites, which inspire the synthesis of modified variants with improved pharmacological profiles. In artificial intelligence and machine learning applications, ZINC data trains generative models for de novo drug design, such as deep reinforcement learning systems that retrospectively recover commercially available compounds while proposing novel structures adhering to synthesizability constraints. By 2025, ZINC subsets have been widely used to train large-scale molecular language models for property prediction and scaffold generation in drug discovery. Community-driven extensions, including open-source browsers like the Multi-Fingerprint Browser for ZINC, enhance accessibility and foster contributions in non-drug discovery areas, with citations appearing in materials science for exploring organic ligands in metal-organic frameworks.

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