ArticleChemical science2021
Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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The trial behind it
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Who cites it
29 citing papers in PubMed.
- Chemical Distance-Based Acceleration of Large Library Docking with ChemSTEP.ACS central science · 2026Article
- Modeling the Sensitivity of Large-Scale Virtual Screening to Scoring Function Accuracy, Artifacts, and Library Composition.Journal of chemical information and modeling · 2026Article
- Generating multimillion chemical space based on the Ugi four-center three-component reaction with oxocarboxylic acids.Molecular diversity · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- D2Screen: Embedding Pretrained Representation Learning Model and Molecular Docking for Virtual Screening.ACS medicinal chemistry letters · 2025Article
- Structure-based discovery of inhibitors of Mac1 domain of nonstructural protein-3 of SARS-CoV-2 by machine learning-augmented screening of chemical space.bioRxiv : the preprint server for biology · 2025Article
- The Six Ds of Exponentials and drug discovery: A path toward reversing Eroom's law.Drug discovery today · 2025Review
- Inhibitors of SARS-CoV-2 Main Protease (Mpro) as Anti-Coronavirus Agents.Biomolecules · 2024Review
- Multiscale topology-enabled structure-to-sequence transformer for protein-ligand interaction predictions.Nature machine intelligence · 2024Article
- Article
- Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.Molecular diversity · 2024Article
- Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces.ACS central science · 2024Article
- Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR.Nature reviews. Drug discovery · 2024Review
- On the origins of SARS-CoV-2 main protease inhibitors.RSC medicinal chemistry · 2024Review
- Molecular Dynamics and Other HPC Simulations for Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2024Article
- HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries.Molecular informatics · 2024Article
- Review
- Computational Drug Design Strategies for Fighting the COVID-19 Pandemic.Advances in experimental medicine and biology · 2024Article
- ChemFlow_py: a flexible toolkit for docking and rescoring.Journal of computer-aided molecular design · 2023Article
- An interaction-based drug discovery screen explains known SARS-CoV-2 inhibitors and predicts new compound scaffolds.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent explosive growth of 'make-on-demand' chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.