ArticleJournal of computer-aided molecular design2016
A D3R prospective evaluation of machine learning for protein-ligand scoring.
Article in Journal of computer-aided molecular design, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
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
12 citing papers in PubMed.
- Poisson-Boltzmann-based machine learning model for electrostatic analysis.Biophysical journal · 2024Article
- Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions.Journal of chemical information and modeling · 2022Review
- Virtual Screening with Gnina 1.0.Molecules (Basel, Switzerland) · 2021Article
- Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition.Methods (San Diego, Calif.) · 2020Review
- Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions.Journal of chemical information and modeling · 2019Article
- Prediction of various freshness indicators in fish fillets by one multispectral imaging system.Scientific reports · 2019Article
- Improving small molecule virtual screening strategies for the next generation of therapeutics.Current opinion in chemical biology · 2018Review
- Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2.Journal of computer-aided molecular design · 2018Article
- Rama: a machine learning approach for ribosomal protein prediction in plants.Scientific reports · 2017Article
- Geminivirus data warehouse: a database enriched with machine learning approaches.BMC bioinformatics · 2017Article
- Performance of machine-learning scoring functions in structure-based virtual screening.Scientific reports · 2017Article
- Improved pose and affinity predictions using different protocols tailored on the basis of data availability.Journal of computer-aided molecular design · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We assess the performance of several machine learning-based scoring methods at protein-ligand pose prediction, virtual screening, and binding affinity prediction. The methods and the manner in which they were trained make them sufficiently diverse to evaluate the utility of various strategies for training set curation and binding pose generation, but they share a novel approach to classification in the context of protein-ligand scoring. Rather than explicitly using structural data such as affinity values or information extracted from crystal binding poses for training, we instead exploit the abundance of data available from high-throughput screening to approach the problem as one of discriminating binders from non-binders. We evaluate the performance of our various scoring methods in the 2015 D3R Grand Challenge and find that although the merits of some features of our approach remain inconclusive, our scoring methods performed comparably to a state-of-the-art scoring function that was fit to binding affinity data.
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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.