ArticleJournal of computational chemistry2017
Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest.
Article in Journal of computational chemistry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 126 papers, 1 of them a synthesis that pooled it.
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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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
126 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review on the application of machine learning to quantitative structure-activity relationship modeling against Plasmodium falciparum.Molecular diversity · 2022Pooled it
- Persistent Local Laplacian Prediction of Protein-Ligand Binding Affinities.Journal of chemical information and modeling · 2026Article
- Multiomics reveals fatty acid metabolism and immune remodeling in retinal artery occlusion.iScience · 2026Article
- Article
- Reindeer: a protein-ligand feature generator software for machine learning algorithms.BMC bioinformatics · 2026Article
- Systematic evaluation of computational tools to predict the effects of mutations on protein-ligand binding affinity in the absence of experimental structures.Briefings in bioinformatics · 2026Article
- Clinical Deployment of Interpretable AI: Bridging Routine Clinical Tests and Proteomic Signatures for Preeclampsia Risk Stratification.Current drug targets · 2026Article
- Calculating Enzyme Inhibition with Random Forests.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Hands-On Docking with Molegro Virtual Docker.Methods in molecular biology (Clifton, N.J.) · 2026Article
- RA-PLA: Retrieval-augmented graph convolutional networks for protein-ligand binding affinity prediction.PloS one · 2026Article
- Extremely Randomized Trees to Determine Binding Affinity.Methods in molecular biology (Clifton, N.J.) · 2026Article
- SCORCH2: A Generalized Heterogeneous Consensus Model for High-Enrichment Interaction-Based Virtual Screening.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Interconversion of the (+)-O-desmethyltramadol to the lowest-energy conformer when coupled to µ-opioid receptor: comprehensive analysis using in silico molecular modeling.Journal of computer-aided molecular design · 2025Article
- Bioactivity Deep Learning for Complex Structure-Free Compound-Protein Interaction Prediction.Journal of chemical information and modeling · 2025Article
- Hierarchical affinity landscape navigation through learning a shared pocket-ligand space.Patterns (New York, N.Y.) · 2025Article
- Developing an explainable machine learning model to predict false-negative citrin deficiency cases in newborn screening.Orphanet journal of rare diseases · 2025Article
- DRPM: An advanced predictive model for early diabetes detection and risk stratification.Molecular therapy. Nucleic acids · 2025Article
- Comparative Analysis of Quantum-Mechanical and Standard Single-Structure Protein-Ligand Scoring Functions with MD-Based Free Energy Calculations.Journal of chemical information and modeling · 2025Article
- 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
- Machine learning for the prediction of augmented renal clearance (ARC) in patients with sepsis in critical care units.Scientific reports · 2025Article
66 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
The development of new protein-ligand scoring functions using machine learning algorithms, such as random forest, has been of significant interest. By efficiently utilizing expanded feature sets and a large set of experimental data, random forest based scoring functions (RFbScore) can achieve better correlations to experimental protein-ligand binding data with known crystal structures; however, more extensive tests indicate that such enhancement in scoring power comes with significant under-performance in docking and screening power tests compared to traditional scoring functions. In this work, to improve scoring-docking-screening powers of protein-ligand docking functions simultaneously, we have introduced a 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.