ArticleJournal of chemical information and modeling2019
Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions.
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 38 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
38 citing papers in PubMed.
- Article
- SCORCH2: A Generalized Heterogeneous Consensus Model for High-Enrichment Interaction-Based Virtual Screening.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Hierarchical affinity landscape navigation through learning a shared pocket-ligand space.Patterns (New York, N.Y.) · 2025Article
- Solvation Energetic Costs of Cognate Binding Site Formation.Journal of chemical information and modeling · 2025Article
- A Computational Perspective to Intermolecular Interactions and the Role of the Solvent on Regulating Protein Properties.Chemical reviews · 2025Review
- Using Deep Graph Neural Networks Improves Physics-Based Hydration Free Energy Predictions Even for Molecules Outside of the Training Set Distribution.The journal of physical chemistry. B · 2025Article
- Computable properties of selected monomeric acylphloroglucinols with anticancer and/or antimalarial activities and first-approximation docking study.Journal of molecular modeling · 2025Article
- GRADE and X-GRADE: Unveiling Novel Protein-Ligand Interaction Fingerprints Based on GRAIL Scores.Journal of chemical information and modeling · 2025Article
- Integrating Quantum Mechanics into Protein-Ligand Docking: Toward Higher Accuracy and Reliability.Research square · 2024Article
- Incorporating Water Molecules into Highly Accurate Binding Affinity Prediction for Proteins and Ligands.International journal of molecular sciences · 2024Article
- SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening.Journal of chemical theory and computation · 2024Article
- Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease.Cell reports · 2024Article
- Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.Chemical reviews · 2024Review
- A new paradigm for applying deep learning to protein-ligand interaction prediction.Briefings in bioinformatics · 2024Article
- CarsiDock: a deep learning paradigm for accurate protein-ligand docking and screening based on large-scale pre-training.Chemical science · 2024Article
- Docking strategies for predicting protein-ligand interactions and their application to structure-based drug design.Communications in information and systems · 2024Article
- Integrating natural product research laboratory with artificial intelligence: Advancements and breakthroughs in traditional medicine.BioMedicine · 2024Review
- An overview of recent advances and challenges in predicting compound-protein interaction (CPI).Medical review (2021) · 2023Review
- Integrated Molecular Modeling and Machine Learning for Drug Design.Journal of chemical theory and computation · 2023Review
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Structure-based drug design is critically dependent on accuracy of molecular docking scoring functions, and there is of significant interest to advance scoring functions with machine learning approaches. In this work, by judiciously expanding the training set, exploring new features related to explicit mediating water molecules as well as ligand conformation stability, and applying extreme gradient boosting (XGBoost) with Δ-Vina parametrization, we have improved robustness and applicability of machine-learning scoring functions. The new scoring function Δ
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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.