11 research outputs found
BgN-Score and BsN-Score: Bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes
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Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins
No full textNonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions
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Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity
No full textInternational audienceMolecular docking enables large-scale prediction of whether and how small molecules bind to a macromolecular target. Machine-learning scoring functions are particularly well suited to predict the strength of this interaction. Here we describe how to build RF-Score, a scoring function utilizing the machine-learning technique known as Random Forest (RF). We also point out how to use different data, features, and regression models using either R or Python programming languages
Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment
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Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data
No full textData driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR
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