ZINC20 – A Free Ultra Large-Scale Chemical Database for Ligand Discovery

By John J. Irwin, Khanh G. Tang, Jennifer Young, Chinzorig Dandarchuluun, B. R. Wong, Munkhzul Khurelbaatar, Yurii S. Moroz, J. W. Mayfield, Roger A. Sayle. Journal of Chemical Information and Modeling, 2020

849 citations recorded by the source corpus.

Identifying and purchasing new small molecules to test in biological assays is enabling for ligand discovery, but as purchasable chemical space continues to grow into the tens of billions based on inexpensive make-on-demand compounds, simply searching this space becomes a major challenge. We have therefore developed ZINC20, a new version of ZINC with two major new features: billions of new molecules and new methods to search them. As a fully enumerated database, ZINC can be searched precisely using explicit atomic-level graph-based methods, such as SmallWorld for similarity, Arthor for pattern and substructure search, as well as 3D methods such as docking. Analysis of the new make-on-demand compound sets by these and related tools reveals startling features. For instance, over 97% of the core Bemis-Murcko scaffolds in make-on-demand libraries are unavailable from “in stock” collections. Correspondingly, the number of new Bemis-Murcko scaffolds is rising almost as a linear fraction of the elaborated molecules. Thus, an 88-fold increase in the number of molecules in the make-on-demand vs the in-stock sets is built upon a 16-fold increase in the number of Bemis-Murcko scaffolds. The make-on-demand library is also more structurally diverse than physical libraries, with a massive increase in disc- and sphere-like shaped molecules. The new system is freely available at zinc20.docking.org.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Pharmacology

View Paper: https://www.semanticscholar.org/paper/8c2b647a4ddd5d490ada2ed21e28aed460640926

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Citations in the corpus

Computational approaches streamlining drug discovery (2023, background): “Driven by new reactions and diverse building blocks, the fully enumerated REAL database has grown from approximately 170 million compounds in 2017 to more than 5.5 billion compounds in 2022 and comprises the bulk of the popular ZINC20 virtual screening database 42 .”
Structure-based drug design with equivariant diffusion models (2022, neutral): “Pock-etFlow was pretrained on about 8 million molecules from the ZINC database 56 and finetuned on a different subset of the CrossDocked dataset.”
CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding (2021, background): “From a financial perspective, the creation of ultra-large libraries of chemicals that can be described in silico and procured on demand 2,19 significantly reduces the cost associated with accessing chemical matter to test predictions.”
Augmenting large language models with chemistry tools (2023, methodology): “By taking a molecule’s SMILES representation as input, the patent checker tool informs the LLM if a patent exists for that particular molecule, thus helping it avoid potential intellectual property conflicts and determine whether a given compound is novel.”