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Target-Specific Support Vector Machine Scoring in Structure-Based Virtual Screening: Computational Validation, In Vitro Testing in Kinases, and Effects on Lung Cancer Cell Proliferation
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function
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A semiempirical free energy force field with charge‐based desolvation
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Efficient comprehensive scoring of docked protein complexes using probabilistic support vector machines
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Predicting protein complex geometries with a neural network
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Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
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Ligand-based and structure-based approaches in identifying ideal pharmacophore against c-Jun N-terminal kinase-3
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Customizing scoring functions for docking
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Robust optimization of scoring functions for a target class
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Structure of the Autoinhibited Kinase Domain of CaMKII and SAXS Analysis of the Holoenzyme
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Review of epidermal growth factor receptor biology
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Physics-Based Scoring of Protein−Ligand Complexes: Enrichment of Known Inhibitors in Large-Scale Virtual Screening
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Novel, Customizable Scoring Functions, Parameterized Using N-PLS, for Structure-Based Drug Discovery
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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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Prediction and Evaluation of Protein Farnesyltransferase Inhibition by Commercial Drugs
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A General and Fast Scoring Function for Protein−Ligand Interactions: A Simplified Potential Approach
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Evaluation of PMF Scoring in Docking Weak Ligands to the FK506 Binding Protein
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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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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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Identification of a chemical probe for NAADP by virtual screening
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Docking for fragment inhibitors of AmpC β-lactamase
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A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
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An accurate, residue‐level, pair potential of mean force for folding and binding based on the distance‐scaled, ideal‐gas reference state
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Neuronal Ca2+/Calmodulin-Dependent Protein Kinase II: The Role of Structure and Autoregulation in Cellular Function
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Scoring Functions for Protein-Ligand Docking
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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