ArticleProceedings of the National Academy of Sciences of the United States of America2021
Leveraging nonstructural data to predict structures and affinities of protein-ligand complexes.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 38 citations in OpenAlex.
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- Dual-LAO for calculating fast and robust relative binding free energies of simple and complex transformations.Communications chemistry · 2026Article
- Label-Free Measurement of Ligand Interactions Using SABRE Hyperpolarization at Low Magnetic Fields.Analytical chemistry · 2026Article
- Collision-free morgan fingerprints: a principled approach to enhance machine learning performance and interpretability in chemistry.Journal of cheminformatics · 2026Article
- Drug-Target Interaction Prediction with PIGLET.bioRxiv : the preprint server for biology · 2026Article
- Discovery of IID432 for Chagas Disease: A Cyanotriazole Inhibitor ofJournal of medicinal chemistry · 2026Article
- Protein Structural Model Selection Informed by Comparison of Predicted Ligand Binding Poses.Journal of chemical information and modeling · 2025Article
- The Art and Science of Molecular Docking.Annual review of biochemistry · 2024Review
- Reducing overconfident errors in molecular property classification using Posterior Network.Patterns (New York, N.Y.) · 2024Article
- CarsiDock: a deep learning paradigm for accurate protein-ligand docking and screening based on large-scale pre-training.Chemical science · 2024Article
- Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein-Protein Binding Affinity.ACS omega · 2024Article
- Article
- Open-ComBind: harnessing unlabeled data for improved binding pose prediction.Journal of computer-aided molecular design · 2023Article
- Article
- Binding affinity predictions with hybrid quantum-classical convolutional neural networks.Scientific reports · 2023Article
- PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions.Protein science : a publication of the Protein Society · 2023Article
- Transferability of Geometric Patterns from Protein Self-Interactions to Protein-Ligand Interactions.Pacific Symposium on Biocomputing. Pacific Symposium on BiocomputingArticle
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
Over the past five decades, tremendous effort has been devoted to computational methods for predicting properties of ligands-i.e., molecules that bind macromolecular targets. Such methods, which are critical to rational drug design, fall into two categories: physics-based methods, which directly model ligand interactions with the target given the target's three-dimensional (3D) structure, and ligand-based methods, which predict ligand properties given experimental measurements for similar ligands. Here, we present a rigorous statistical framework to combine these two sources of information. We develop a method to predict a ligand's pose-the 3D structure of the ligand bound to its target-that leverages a widely available source of information: a list of other ligands that are known to bind the same target but for which no 3D structure is available. This combination of physics-based and ligand-based modeling improves pose prediction accuracy across all major families of drug targets. Using the same framework, we develop a method for virtual screening of drug candidates, which outperforms standard physics-based and ligand-based virtual screening methods. Our results suggest broad opportunities to improve prediction of various ligand properties by combining diverse sources of information through customized machine-learning approaches.
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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.