ArticleJournal of chemical information and modeling2019
Target-Specific Prediction of Ligand Affinity with Structure-Based Interaction Fingerprints.
Article in Journal of chemical information and modeling, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Discovery of Two GSK3β Inhibitors fromCurrent computer-aided drug design · 2026Article
- Predicting the Impact of Drug Resistance Mutations on Inhibitor Potency with Molecular Dynamics and Machine Learning.The journal of physical chemistry. B · 2025Article
- A high-resolution data set of fatty acid-binding protein structures. II. Crystallographic overview, ligand classes and binding pose.Acta crystallographica. Section D, Structural biology · 2025Article
- AI Methods Tailored to Influenza, RSV, HIV, and SARS-CoV-2: A Focused Review.Pathogens (Basel, Switzerland) · 2025Review
- AI applications in HIV research: advances and future directions.Frontiers in microbiology · 2025Review
- Web-based machine learning application for interpretable prediction of prolonged length of stay after lumbar spinal stenosis surgery: a retrospective cohort study with explainable AI.Frontiers in physiology · 2025Article
- The Future of Medicine: AI and ML Driven Drug Discovery Advancements.Current topics in medicinal chemistry · 2025Review
- Artificial Intelligence and Infectious Disease Imaging.The Journal of infectious diseases · 2023Article
- Revealing the Mutation Patterns of Drug-Resistant Reverse Transcriptase Variants of Human Immunodeficiency Virus through Proteochemometric Modeling.Biomolecules · 2021Article
- Interpretation of Ligand-Based Activity Cliff Prediction Models Using the Matched Molecular Pair Kernel.Molecules (Basel, Switzerland) · 2021Article
- Deciphering Complex Mechanisms of Resistance and Loss of Potency through Coupled Molecular Dynamics and Machine Learning.Journal of chemical theory and computation · 2021Article
- Drug Design Strategies to Avoid Resistance in Direct-Acting Antivirals and Beyond.Chemical reviews · 2021Review
- Crystal Structure of SARS-CoV-2 Main Protease in Complex with the Non-Covalent Inhibitor ML188.Viruses · 2021Article
- Structure-based protein-ligand interaction fingerprints for binding affinity prediction.Computational and structural biotechnology journal · 2021Review
- Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Discovery and optimization of small molecule inhibitors as therapeutic drugs have immensely benefited from rational structure-based drug design. With recent advances in high-resolution structure determination, computational power, and machine learning methodology, it is becoming more tractable to elucidate the structural basis of drug potency. However, the applicability of machine learning models to drug design is limited by the interpretability of the resulting models in terms of feature importance. Here, we take advantage of the large number of available inhibitor-bound HIV-1 protease structures and associated potencies to evaluate inhibitor diversity and machine learning models to predict ligand affinity. First, using a hierarchical clustering approach, we grouped HIV-1 protease inhibitors and identified distinct core structures. Explicit features including protein-ligand interactions were extracted from high-resolution cocrystal structures as 3D-based fingerprints. We found that a gradient boosting machine learning model with this explicit feature attribution can predict binding affinity with high accuracy. Finally, Shapley values were derived to explain local feature importance. We found specific van der Waals (vdW) interactions of key protein residues are pivotal for the predicted potency. Protein-specific and interpretable prediction models can guide the optimization of many small molecule drugs for improved potency.
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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.