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Support Vector Regression Scoring of Receptor–Ligand Complexes for Rank-Ordering and Virtual Screening of Chemical Libraries

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Virtual Screening Workflow Development Guided by the “Receiver Operating Characteristic” Curve Approach. Application to High-Throughput Docking on Metabotropic Glutamate Receptor Subtype 4
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A Knowledge-Based Energy Function for Protein−Ligand, Protein−Protein, and Protein−DNA Complexes
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Benchmarking Sets for Molecular Docking
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A General and Fast Scoring Function for Protein−Ligand Interactions: A Simplified Potential Approach
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Docking to Erlotinib Off-Targets Leads to Inhibitors of Lung Cancer Cell Proliferation with Suitable in Vitro Pharmacokinetics
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Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes
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Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model
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Further development and validation of empirical scoring functions for structure-based binding affinity prediction
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Virtual screening of chemical libraries
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The Importance of a Critical Protonation State and the Fate of the Catalytic Steps in Class A β-Lactamases and Penicillin-binding Proteins
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PDBcal: A Comprehensive Dataset for Receptor–Ligand Interactions with Three‐dimensional Structures and Binding Thermodynamics from Isothermal Titration Calorimetry
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Training v-Support Vector Classifiers: Theory and Algorithms
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BindingDB: A Web-Accessible Molecular Recognition Database
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Scoring Functions for Protein-Ligand Docking

What points at this work

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