ArticleProceedings of the National Academy of Sciences of the United States of America2023
Turning high-throughput structural biology into predictive inhibitor design.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Structure-agnostic protein-ligand binding affinity prediction via hierarchical representation alignment.Bioinformatics (Oxford, England) · 2026Article
- How many crystal structures do you need to trust your docking results?bioRxiv : the preprint server for biology · 2025Article
- A New Fragment-Based Pharmacophore Virtual Screening Workflow Identifies Potent Inhibitors of SARS-CoV-2 NSP13 Helicase.Journal of computational chemistry · 2025Article
- Optimized pipeline and designer cells for synthetic-biology-based high-throughput screening of viral protease inhibitors.Cell reports methods · 2025Article
- Structural biology at the National Synchrotron Light Source II.Journal of synchrotron radiation · 2025Article
- Toward target 2035: EUbOPEN - a public-private partnership to enable & unlock biology in the open.RSC medicinal chemistry · 2025Article
- Identification and Ranking of Binding Sites from Structural Ensembles: Application to SARS-CoV-2.Viruses · 2024Article
- Leveraging bounded datapoints to classify molecular potency improvements.RSC medicinal chemistry · 2024Article
- Multiscale topology-enabled structure-to-sequence transformer for protein-ligand interaction predictions.Nature machine intelligence · 2024Article
- On the origins of SARS-CoV-2 main protease inhibitors.RSC medicinal chemistry · 2024Review
- Review
- GAABind: a geometry-aware attention-based network for accurate protein-ligand binding pose and binding affinity prediction.Briefings in bioinformatics · 2023Article
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning.Journal of cheminformatics · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
A common challenge in drug design pertains to finding chemical modifications to a ligand that increases its affinity to the target protein. An underutilized advance is the increase in structural biology throughput, which has progressed from an artisanal endeavor to a monthly throughput of hundreds of different ligands against a protein in modern synchrotrons. However, the missing piece is a framework that turns high-throughput crystallography data into predictive models for ligand design. Here, we designed a simple machine learning approach that predicts protein-ligand affinity from experimental structures of diverse ligands against a single protein paired with biochemical measurements. Our key insight is using physics-based energy descriptors to represent protein-ligand complexes and a learning-to-rank approach that infers the relevant differences between binding modes. We ran a high-throughput crystallography campaign against the SARS-CoV-2 main protease (M
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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.