ArticleJournal of chemical information and modeling2013
CSAR data set release 2012: ligands, affinities, complexes, and docking decoys.
Article in Journal of chemical information and modeling, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- A 4D tensor-enhanced multi-dimensional convolutional neural network for accurate prediction of protein-ligand binding affinity.Molecular diversity · 2025Article
- RosettaAMRLD: A Reaction-Driven Approach for Structure-Based Drug Design from Combinatorial Libraries with Monte Carlo Metropolis Algorithms.Journal of chemical information and modeling · 2025Article
- Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency.PLoS computational biology · 2025Article
- iScore: A ML-Based Scoring Function for De Novo Drug Discovery.Journal of chemical information and modeling · 2025Article
- The Need for Continuing Blinded Pose- and Activity Prediction Benchmarks.Journal of chemical information and modeling · 2025Review
- Rationalizing protein-ligand interactions via the effective fragment potential method and structural data from classical molecular dynamics.The Journal of chemical physics · 2025Article
- Machine learning approaches for predicting protein-ligand binding sites from sequence data.Frontiers in bioinformatics · 2025Review
- Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities.Computational and structural biotechnology journal · 2024Review
- Structure-based, deep-learning models for protein-ligand binding affinity prediction.Journal of cheminformatics · 2024Review
- Multi-task bioassay pre-training for protein-ligand binding affinity prediction.Briefings in bioinformatics · 2023Article
- The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks.International journal of molecular sciences · 2023Article
- PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications.Scientific data · 2022Article
- Protein-Ligand Docking in the Machine-Learning Era.Molecules (Basel, Switzerland) · 2022Review
- Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.Frontiers in bioinformatics · 2022Article
- Sfcnn: a novel scoring function based on 3D convolutional neural network for accurate and stable protein-ligand affinity prediction.BMC bioinformatics · 2022Article
- Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics.Journal of chemical theory and computation · 2022Article
- Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning.Journal of chemical information and modeling · 2021Article
- Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions.BMC bioinformatics · 2021Article
- The critical role of QM/MM X-ray refinement and accurate tautomer/protomer determination in structure-based drug design.Journal of computer-aided molecular design · 2021Article
- Dynamic Docking Using Multicanonical Molecular Dynamics: Simulating Complex Formation at the Atomistic Level.Methods in molecular biology (Clifton, N.J.) · 2021Article
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12 authors.
Funding
Abstract
A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) has collected several data sets from industry and added in-house data sets that may be used for this purpose ( www.csardock.org). CSAR has currently obtained data from Abbott, GlaxoSmithKline, and Vertex and is working on obtaining data from several others. Combined with our in-house projects, we are providing a data set consisting of 6 protein targets, 647 compounds with biological affinities, and 82 crystal structures. Multiple congeneric series are available for several targets with a few representative crystal structures of each of the series. These series generally contain a few inactive compounds, usually not available in the literature, to provide an upper bound to the affinity range. The affinity ranges are typically 3-4 orders of magnitude per series. For our in-house projects, we have had compounds synthesized for biological testing. Affinities were measured by Thermofluor, Octet RED, and isothermal titration calorimetry for the most soluble. This allows the direct comparison of the biological affinities for those compounds, providing a measure of the variance in the experimental affinity. It appears that there can be considerable variance in the absolute value of the affinity, making the prediction of the absolute value ill-defined. However, the relative rankings within the methods are much better, and this fits with the observation that predicting relative ranking is a more tractable problem computationally. For those in-house compounds, we also have measured the following physical properties: logD, logP, thermodynamic solubility, and pK(a). This data set also provides a substantial decoy set for each target consisting of diverse conformations covering the entire active site for all of the 58 CSAR-quality crystal structures. The CSAR data sets (CSAR-NRC HiQ and the 2012 release) provide substantial, publically available, curated data sets for use in parametrizing and validating docking and scoring methods.
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