Modeling the expansion of virtual screening libraries

Lyu, Jiankun; Irwin, John J.; Shoichet, Brian K

doi:10.1038/s41589-022-01234-w

Cited by 85 publications

(100 citation statements)

References 50 publications

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“…Work from our lab suggests that as the database grows, docking can identify ever-better-fitting molecules. 30 ■ DISCUSSION Three themes emerge from this work. First, a new database, ZINC-22, is now freely available on our website.…”

Section: ■ Resultsmentioning

confidence: 99%

ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery

Tingle

Tang

Castanon

et al. 2023

J. Chem. Inf. Model.

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Purchasable chemical space has grown rapidly into the tens of billions of molecules, providing unprecedented opportunities for ligand discovery but straining the tools that might exploit these molecules at scale. We have therefore developed ZINC-22, a database of commercially accessible small molecules derived from multi-billion-scale make-on-demand libraries. The new database and tools enable analog searching in this vast new space via a facile GUI, CartBlanche, drawing on similarity methods that scale sublinearly in the number of molecules. The new library also uses data organization methods, enabling rapid lookup of molecules and their physical properties, including conformations, partial atomic charges, c Log P values, and solvation energies, all crucial for molecule docking, which had become slow with older database organizations in previous versions of ZINC. As the libraries have continued to grow, we have been interested in finding whether molecular diversity has suffered, for instance, because certain scaffolds have come to dominate via easy analoging. This has not occurred thus far, and chemical diversity continues to grow with database size, with a log increase in Bemis–Murcko scaffolds for every two-log unit increase in database size. Most new scaffolds come from compounds with the highest heavy atom count. Finally, we consider the implications for databases like ZINC as the libraries grow toward and beyond the trillion-molecule range. ZINC is freely available to everyone and may be accessed at cartblanche22.docking.org, via Globus, and in the Amazon AWS and Oracle OCI clouds.

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Section: ■ Resultsmentioning

confidence: 99%

ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery

Tingle

Tang

Castanon

et al. 2023

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

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“…One the one hand, there is a vast list available of examples regarding small-scale virtual screening campaigns that kick-started drug discovery projects, and on the other hand, we consider that it is yet too early to entirely judge large-scale efforts, and results regarding a vast spectrum of protein targets as well as different docking programs are needed. Nevertheless, we consider that large-scale campaigns are another valid possibility within the early stages of drug discovery and, as some studies suggest, that screening large chemical libraries appears to lead to the identification of chemically diverse compounds as potential ligands, as well as better docking scores and better-fitting ligands. ,, It remains interesting to explore this possibility while being able to perform these large-scale campaigns in a consistent manner. As the brute-force approach of docking every compound in a large chemical database is unfeasible, ML implementations have excelled at the task of finding a shortcut for virtual hit identification.…”

Section: Discussionmentioning

confidence: 99%

The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking

Cavasotto

Filippo

2023

J. Chem. Inf. Model.

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Structure-based virtual screening methods are, nowadays, one of the key pillars of computational drug discovery. In recent years, a series of studies have reported docking-based virtual screening campaigns of large databases ranging from hundreds to thousands of millions compounds, further identifying novel hits after experimental validation. As these larg-scale efforts are not generally accessible, machine learning-based protocols have emerged to accelerate the identification of virtual hits within an ultralarge chemical space, reaching impressive reductions in computational time. Herein, we illustrate the motivation and the problem behind the screening of large databases, providing an overview of key concepts and essential applications of machine learning-accelerated protocols, specifically concerning supervised learning methods. We also discuss where the field stands with these novel developments, highlighting possible insights for future studies.

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“…While known drugs carry the benefit of faster path to clinical distribution, and bio-like molecules are generally perceived as being more likely to successfully translate to clinical relevance, there is reason to expect that exploration of a much larger set of candidates may yield drugs that are unlike others identified previously. For example, Lyu et al 95 observe that billion-scale libraries are dramatically diminished for bio-like molecules relative to more focused libraries, yet still contain many experimentally-confirmed actives, as well as thousands of high-ranking molecules in docking assays. This observation justifies continued emphasis on development of methods for computationally screening billion-scale libraries.…”

Section: Discussionmentioning

confidence: 99%

A community effort to discover small molecule SARS-CoV-2 inhibitors

Schimunek

Seidl

Elez

et al. 2023

Preprint

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The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of a community effort, the “Billion molecules against Covid-19 challenge”, to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 potentially active molecules, which were subsequently ranked to find ‘consensus compounds’. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (Nsp12 domain), and (alpha) spike protein S. Overall, 27 potential inhibitors were experimentally confirmed by binding-, cleavage-, and/or viral suppression assays and are presented here. All results are freely available and can be taken further downstream without IP restrictions. Overall, we show the effectiveness of computational techniques, community efforts, and communication across research fields (i.e., protein expression and crystallography, in silico modeling, synthesis and biological assays) to accelerate the early phases of drug discovery.

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Modeling the expansion of virtual screening libraries

Cited by 85 publications

References 50 publications

ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery

ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery

The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking

A community effort to discover small molecule SARS-CoV-2 inhibitors

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