ArticleMolecular diversity2015
Consensus models for CDK5 inhibitors in silico and their application to inhibitor discovery.
Article in Molecular diversity, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 30 citations in OpenAlex.
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- The Mechanisms of Bushen-Yizhi Formula as a Therapeutic Agent against Alzheimer's Disease.Scientific reports · 2018Article
- Discovery of VEGFR2 inhibitors by integrating naïve Bayesian classification, molecular docking and drug screening approaches.RSC advances · 2018Article
- In silico prediction of ROCK II inhibitors by different classification approaches.Molecular diversity · 2017Article
- AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease.PloS one · 2017Article
- Docking Screens for Novel Ligands Conferring New Biology.Journal of medicinal chemistry · 2016Review
- Discovery of Influenza A virus neuraminidase inhibitors using support vector machine and Naïve Bayesian models.Molecular diversity · 2016Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cyclin-dependent kinase 5 (CDK5) has emerged as a principal therapeutic target for Alzheimer's disease. It is highly desirable to develop computational models that can predict the inhibitory effects of a compound towards CDK5 activity. In this study, two machine learning tools (naive Bayesian and recursive partitioning) were used to generate four single classifiers from a large dataset containing 462 CDK5 inhibitors and 1,500 non-inhibitors. Then, two types of consensus models [combined classifier-artificial neural networks (CC-ANNs) and consensus prediction] were applied to combine four single classifiers to obtain superior performance. The results showed that both consensus models outperformed four single classifiers, and (MCC = 0.806) was superior to consensus prediction (MCC = 0.711) for an external test set. To illustrate the practical applications of the CC-ANN model in virtual screening, an in-house dataset containing 29,170 compounds was screened, and 40 compounds were selected for further bioactivity assays. The assay results showed that 13 out of 40 compounds exerted CDK5/p35 inhibitory activities with IC50 values ranging from 9.23 to 229.76 μM. Interestingly, three new scaffolds that had not been previously reported as CDK5 inhibitors were found in this study. These studies prove that our protocol is an effective approach to predict small-molecule CDK5 affinity and identify novel lead compounds.
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