ArticleJournal of chemical information and modeling2011
Combined application of cheminformatics- and physical force field-based scoring functions improves binding affinity prediction for CSAR data sets.
Article in Journal of chemical information and modeling, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Predicting Protein-Ligand Docking Structure with Graph Neural Network.Journal of chemical information and modeling · 2022Review
- GPU-Accelerated Flexible Molecular Docking.The journal of physical chemistry. B · 2021Article
- Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization.Journal of computer-aided molecular design · 2017Article
- The Development of Target-Specific Pose Filter Ensembles To Boost Ligand Enrichment for Structure-Based Virtual Screening.Journal of chemical information and modeling · 2017Article
- Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data.BMC bioinformatics · 2017Article
- A D3R prospective evaluation of machine learning for protein-ligand scoring.Journal of computer-aided molecular design · 2016Article
- Different combinations of atomic interactions predict protein-small molecule and protein-DNA/RNA affinities with similar accuracy.Proteins · 2015Article
- Multipose binding in molecular docking.International journal of molecular sciences · 2014Article
- Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches.Journal of chemical information and modeling · 2013Article
- Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise.Journal of chemical information and modeling · 2013Article
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6 authors.
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Abstract
The curated CSAR-NRC benchmark sets provide valuable opportunity for testing or comparing the performance of both existing and novel scoring functions. We apply two different scoring functions, both independently and in combination, to predict the binding affinity of ligands in the CSAR-NRC data sets. One reported here for the first time employs multiple chemical-geometrical descriptors of the protein-ligand interface to develop Quantitative Structure Binding Affinity Relationships (QSBAR) models. These models are then used to predict binding affinity of ligands in the external data set. Second is a physical force field-based scoring function, MedusaScore. We show that both individual scoring functions achieve statistically significant prediction accuracies with the squared correlation coefficient (R(2)) between the actual and predicted binding affinity of 0.44/0.53 (Set1/Set2) with QSBAR models and 0.34/0.47 (Set1/Set2) with MedusaScore. Importantly, we find that the combination of QSBAR models and MedusaScore into consensus scoring function affords higher prediction accuracy than any of the contributing methods achieving R(2) values of 0.45/0.58 (Set1/Set2). Furthermore, we identify several chemical features and noncovalent interactions that may be responsible for the inaccurate prediction of binding affinity for several ligands by the scoring functions employed in this study.
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