Ultra-large library docking for discovering new chemotypes

By Jiankun Lyu, Sheng Wang, Trent E. Balius, I. Singh, A. Levit, Yurii S. Moroz, Matthew J. O’Meara, T. Che, Enkhjargal Algaa, Kateryna Tolmachova and 4 more. Nature, 2019

846 citations recorded by the source corpus.

Despite intense interest in expanding chemical space, libraries containing hundreds-of-millions to billions of diverse molecules have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compounds from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compound in the library, docking against AmpC β-lactamase (AmpC) and the D4 dopamine receptor were simulated. From the top-ranking molecules, 44 and 549 compounds were synthesized and tested for interactions with AmpC and the D4 dopamine receptor, respectively. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This molecule was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D4 dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate versus score curve predicted that the library contained 453,000 ligands for the D4 dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D4 dopamine receptor. Using a make-on-demand library that contains hundreds-of-millions of molecules, structure-based docking was used to identify compounds that, after synthesis and testing, are shown to interact with AmpC β-lactamase and the D4 dopamine receptor with high affinity.

Subfields: Computational Theory and Mathematics, Molecular Biology, Organic Chemistry

View Paper: https://www.semanticscholar.org/paper/6f4d5757335c82d8354b5766baedd8e3091a7d21

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Citations in the corpus

Computational approaches streamlining drug discovery (2023, methodology): “This has been demonstrated in ultra-large virtual screening campaigns for several targets, revealing highly potent ligands with affinities often in the mid-nanomolar to sub-nanomolar range 20–23 , 26 .”
Rethinking drug design in the artificial intelligence era (2019, background): “Automated ‘ultra-large’ chemical library docking has recently been performed for 170 million virtual compounds assembled from 130 chemical reactions — the largest in silico ligand–receptor docking run to date 62 .”
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, methodology): “HTS: High-Throughput Screening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 5.4 single_pred.”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, neutral): “This means that molecular properties need to be predicted over very diverse sets of molecules, such as those found in combinatorially large chemical databases like ZINC (Sterling and Irwin, 2015) or DNA-encoded libraries (Lyu et al., 2019; McCloskey et al., 2020).”
Key Topics in Molecular Docking for Drug Design (2019, background): “Very recently, researchers from UCSF (University of California, San Francisco) have completed ultra-large campaigns, screening approximately 99 million compounds for AmpC β-lactamase and 138 million compounds for the D4 dopamine receptor, ultimately finding 30 compounds with sub-micromolar activity,”
Molecular Docking: Shifting Paradigms in Drug Discovery (2019, background): “: (i) protein binding sites in which ligands could bind [61]; (ii) novel molecular targets of known ligands [54]; (iii) potential adverse drug reactions (ADRs) [62], and; (iv) ligands with novel chemotypes active against a given target or a set of desired targets [55].”
A geometric deep learning approach to predict binding conformations of bioactive molecules (2021, methodology): “The resulting vectors are used as inputs to a GNN (equation (7)), resulting in aggregated node and edge features v′i and e′i,j, respectively, which are projected back to 128D vectors v up i and e up i,j as shown in equations (8) and (9).”
A practical guide to large-scale docking (2021, background): “While we cannot provide clear guidance or a best-practice protocol for hit-to-lead optimization, analog-by-catalog of docking hits obtained for the melatonin 1 (MT 1 ) receptor and AmpC led to compounds with improved potencies compared with the initial parent compounds 12,13 .”
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.”