ArticlePloS one2012
Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1).
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- DockM8: an all-in-one open-source platform for consensus virtual screening in drug design.Journal of cheminformatics · 2026Article
- Development of Novel Bacterial Topoisomerase Inhibitors Assisted by Computational Screening.ACS medicinal chemistry letters · 2024Article
- ChemFlow_py: a flexible toolkit for docking and rescoring.Journal of computer-aided molecular design · 2023Article
- Exploration on learning molecular docking with deep learning models.Quantitative biology (Beijing, China) · 2023Article
- Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening.Molecules (Basel, Switzerland) · 2022Review
- In Silico Investigations into the Selectivity of Psychoactive and New Psychoactive Substances in Monoamine Transporters.ACS omega · 2022Article
- dockECR: Open consensus docking and ranking protocol for virtual screening of small molecules.Journal of molecular graphics & modelling · 2021Article
- Key Topics in Molecular Docking for Drug Design.International journal of molecular sciences · 2019Review
- Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns.International journal of molecular sciences · 2019Article
- Exponential consensus ranking improves the outcome in docking and receptor ensemble docking.Scientific reports · 2019Article
- Hybrid approach to sieve out natural compounds against dual targets in Alzheimer's Disease.Scientific reports · 2019Article
- Changing Paradigm from one Target one Ligand Towards Multi-target Directed Ligand Design for Key Drug Targets of Alzheimer Disease: An Important Role of In Silico Methods in Multi-target Directed Ligands Design.Current neuropharmacology · 2018Article
- Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges.Frontiers in pharmacology · 2018Review
- Fusing Docking Scoring Functions Improves the Virtual Screening Performance for Discovering Parkinson's Disease Dual Target Ligands.Current neuropharmacology · 2017Article
- Repositioning FDA Drugs as Potential Cruzain Inhibitors from Trypanosoma cruzi: Virtual Screening, In Vitro and In Vivo Studies.Molecules (Basel, Switzerland) · 2017Article
- Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme.Scientific reports · 2017Article
- vSDC: a method to improve early recognition in virtual screening when limited experimental resources are available.Journal of cheminformatics · 2016Article
- Evaluation of 11 scoring functions performance on matrix metalloproteinases.International journal of medicinal chemistry · 2014Article
- Docking challenge: protein sampling and molecular docking performance.Journal of chemical information and modeling · 2013Article
- Enhanced ranking of PknB Inhibitors using data fusion methods.Journal of cheminformatics · 2013Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundIn order to identify novel chemical classes of β-secretase (BACE-1) inhibitors, an alternative scoring protocol, Principal Component Analysis (PCA), was proposed to summarize most of the information from the original scoring functions and re-rank the results from the virtual screening against BACE-1.
methodGiven a training set (50 BACE-1 inhibitors and 9950 inactive diverse compounds), three rank-based virtual screening methods, individual scoring, conventional consensus scoring and PCA, were judged by the hit number in the top 1% of the ranked list. The docking poses were generated by Surflex, five scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, and PMF_Score) were used for pose extraction. For each pose group, twelve scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, PMF_Score, LigScore1, LigScore2, PLP1, PLP2, jain, Ludi_1, and Ludi_2) were used for the pose rank. For a test set, 113,228 chemical compounds (Sigma-Aldrich® corporate chemical directory) were docked by Surflex, then ranked by the same three ranking methods motioned above to select the potential active compounds for experimental test.
resultsFor the training set, the PCA approach yielded consistently superior rankings compared to conventional consensus scoring and single scoring. For the test set, the top 20 compounds according to conventional consensus scoring were experimentally tested, no inhibitor was found. Then, we relied on PCA scoring protocol to test another different top 20 compounds and two low micromolar inhibitors (S450588 and 276065) were emerged through the BACE-1 fluorescence resonance energy transfer (FRET) assay.
conclusionThe PCA method extends the conventional consensus scoring in a quantitative statistical manner and would appear to have considerable potential for chemical screening applications.
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