ArticlePloS one2015
MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Advancing the modernization of traditional Chinese medicine through artificial intelligence and multimodal data integration.Chinese medicine · 2026Review
- Binding Activity Classification of Anti-SARS-CoV-2 Molecules using Deep Learning Across Multiple AssaysBalkan medical journal · 2024Article
- Identification of Drug Targets and Their Inhibitors inPharmaceuticals (Basel, Switzerland) · 2023Article
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace.Briefings in bioinformatics · 2021Review
- A Pilot Study of Multi-Input Recurrent Neural Networks for Drug-Kinase Binding Prediction.Molecules (Basel, Switzerland) · 2020Article
- Discovery of Small-Molecule Activators for Glucose-6-Phosphate Dehydrogenase (G6PD) Using Machine Learning Approaches.International journal of molecular sciences · 2020Article
- Review
- Structure-Based Virtual Screening: From Classical to Artificial Intelligence.Frontiers in chemistry · 2020Review
- Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.Briefings in bioinformatics · 2019Review
- Key Topics in Molecular Docking for Drug Design.International journal of molecular sciences · 2019Review
- Deep learning and virtual drug screening.Future medicinal chemistry · 2018Article
- Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review.Current pharmaceutical design · 2018Review
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine learning methods are widely used in drug discovery studies for classification purpose. Here, we aim to develop a new tool, which can classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms. To construct this tool, first, performances of twenty-three different machine learning algorithms are compared by ten different measures, then, ten best performing algorithms have been selected based on principal component and hierarchical cluster analysis results. Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to create two-dimensional structures of compounds. This application is freely available through www.biosoft.hacettepe.edu.tr/MLViS/.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.