ArticleJournal of computer-aided molecular design2018
Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.
Article in Journal of computer-aided molecular design, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The dawn of biophysical representations in computational immunology.QRB discovery · 2025Review
- From UK-2A to florylpicoxamid: Active learning to identify a mimic of a macrocyclic natural product.Journal of computer-aided molecular design · 2024Article
- Leveraging nonstructural data to predict structures and affinities of protein-ligand complexes.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Design, Synthesis, and Evaluation of Novel 2-Methoxyestradiol Derivatives as Apoptotic Inducers Through an Intrinsic Apoptosis Pathway.Biomolecules · 2020Article
- Electrostatic-field and surface-shape similarity for virtual screening and pose prediction.Journal of computer-aided molecular design · 2019Article
- BCL::MolAlign: Three-Dimensional Small Molecule Alignment for Pharmacophore Mapping.Journal of chemical information and modeling · 2019Article
- Recommendations for evaluation of computational methods.Journal of computer-aided molecular designReview
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification.
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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.