ArticleBMC bioinformatics2017
Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Uncovering potential CDK9 inhibitors from natural compound databases through docking-based virtual screening and MD simulations.Journal of molecular modeling · 2024Article
- Open-ComBind: harnessing unlabeled data for improved binding pose prediction.Journal of computer-aided molecular design · 2023Article
- Exploring Natural Alkaloids from Brazilian Biodiversity as Potential Inhibitors of theMolecules (Basel, Switzerland) · 2023Article
- Recent Advances in In Silico Target Fishing.Molecules (Basel, Switzerland) · 2021Review
- In Silico Study Identified Methotrexate Analog as Potential Inhibitor of Drug Resistant Human Dihydrofolate Reductase for Cancer Therapeutics.Molecules (Basel, Switzerland) · 2020Article
- Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions.Journal of computer-aided molecular design · 2019Article
- Molecular Docking: Shifting Paradigms in Drug Discovery.International journal of molecular sciences · 2019Review
- Prediction Methods of Herbal Compounds in Chinese Medicinal Herbs.Molecules (Basel, Switzerland) · 2018Review
- Development of a Fingerprint-Based Scoring Function for the Prediction of the Binding Mode of Carbonic Anhydrase II Inhibitors.International journal of molecular sciences · 2018Article
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6 authors.
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Abstract
backgroundIn structure-based drug design, binding affinity prediction remains as a challenging goal for current scoring functions. Development of target-biased scoring functions provides a new possibility for tackling this problem, but this approach is also associated with certain technical difficulties. We previously reported the Knowledge-Guided Scoring (KGS) method as an alternative approach (BMC Bioinformatics, 2010, 11, 193-208). The key idea is to compute the binding affinity of a given protein-ligand complex based on the known binding data of an appropriate reference complex, so the error in binding affinity prediction can be reduced effectively.
resultsIn this study, we have developed an upgraded version, i.e. KGS2, by employing 3D protein-ligand interaction fingerprints in reference selection. KGS2 was evaluated in combination with four scoring functions (X-Score, ChemPLP, ASP, and GoldScore) on five drug targets (HIV-1 protease, carbonic anhydrase 2, beta-secretase 1, beta-trypsin, and checkpoint kinase 1). In the in situ scoring test, considerable improvements were observed in most cases after application of KGS2. Besides, the performance of KGS2 was always better than KGS in all cases. In the more challenging molecular docking test, application of KGS2 also led to improved structure-activity relationship in some cases.
conclusionsKGS2 can be applied as a convenient "add-on" to current scoring functions without the need to re-engineer them, and its application is not limited to certain target proteins as customized scoring functions. As an interpolation method, its accuracy in principle can be improved further with the increasing knowledge of protein-ligand complex structures and binding affinity data. We expect that KGS2 will become a practical tool for enhancing the performance of current scoring functions in binding affinity prediction. The KGS2 software is available upon contacting the authors.
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