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CSAR Data Set Release 2012: Ligands, Affinities, Complexes, and Docking Decoys
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Molecular docking using shape descriptors
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Label‐free determination of protein–ligand binding constants using mass spectrometry and validation using surface plasmon resonance and isothermal titration calorimetry
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Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
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Development and validation of a modular, extensible docking program: DOCK 5
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If we designed airplanes like we design drugs…
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Can we really do computer-aided drug design?
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Computer-aided molecular design under the SWOTlight
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A very short history of structure-based design: how did we get here and where do we need to go?
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The errors of our ways: taking account of error in computer-aided drug design to build confidence intervals for our next 25 years
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Variability in docking success rates due to dataset preparation
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Determining kinetics and affinities of protein interactions using a parallel real-time label-free biosensor, the Octet
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Healthy skepticism: assessing realistic model performance
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Essential considerations for using protein–ligand structures in drug discovery
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A New Generation of Crystallographic Validation Tools for the Protein Data Bank
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Retrieval of Crystallographically-Derived Molecular Geometry Information
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Construction and Test of Ligand Decoy Sets Using MDock: Community Structure–Activity Resource Benchmarks for Binding Mode Prediction
inferredcites →https://doi.org/10.1021/ci200082topenalex-snapshot
CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes
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CSAR Benchmark Exercise of 2010: Combined Evaluation Across All Submitted Scoring Functions
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SERAPhiC: A Benchmark for in Silico Fragment-Based Drug Design
inferredcites →https://doi.org/10.1021/ci400025fopenalex-snapshot
CSAR Benchmark Exercise 2011–2012: Evaluation of Results from Docking and Relative Ranking of Blinded Congeneric Series
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Improving the Prediction of Absolute Solvation Free Energies Using the Next Generation OPLS Force Field
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Using shape complementarity as an initial screen in designing ligands for a receptor binding site of known three-dimensional structure
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Conformational Analysis of Drug-Like Molecules Bound to Proteins: An Extensive Study of Ligand Reorganization upon Binding
inferredcites →https://doi.org/10.1021/jm051256oopenalex-snapshot
Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein−Ligand Complexes
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Prediction of Protein−Ligand Interactions. Docking and Scoring: Successes and Gaps
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Diverse, High-Quality Test Set for the Validation of Protein−Ligand Docking Performance
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The Experimental Uncertainty of Heterogeneous Public Ki Data
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DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases
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BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities
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ChEMBL: a large-scale bioactivity database for drug discovery
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The Uppsala Electron-Density Server
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Direct comparison of binding equilibrium, thermodynamic, and rate constants determined by surface‐ and solution‐based biophysical methods
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High-Density Miniaturized Thermal Shift Assays as a General Strategy for Drug Discovery
inferredcites →https://doi.org/10.1261/rna.1563609openalex-snapshot
DOCK 6: Combining techniques to model RNA–small molecule complexes
inferredcites →https://doi.org/10.1371/journal.pone.0036899openalex-snapshot
Thermodynamics of Aryl-Dihydroxyphenyl-Thiadiazole Binding to Human Hsp90
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Enhancing Protein-Ligand Binding Affinity Prediction via Parameter-Efficient Fine-Tuning of Protein and Chemical Language Models
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New Trends in Virtual Screening
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HybridDock: A Hybrid Protein–Ligand Docking Protocol Integrating Protein- and Ligand-Based Approaches
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Protein–Ligand Docking in the Machine-Learning Era
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Estimation of binding free energies with Monte Carlo atomistic simulations and enhanced sampling
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Additional file 1 of Sfcnn: a novel scoring function based on 3D convolutional neural network for accurate and stable protein–ligand affinity prediction
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Additional file 1 of Structure-based, deep-learning models for protein-ligand binding affinity prediction
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Multi-level, multi-body atomic interaction graphs for machine learning-based prediction of protein-ligand binding energies
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DeepBindRG: a deep learning based method for estimating effective protein–ligand affinity
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