ArticleFrontiers in pharmacology2019
Applying Machine Learning to Ultrafast Shape Recognition in Ligand-Based Virtual Screening.
Article in Frontiers in pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- Hypershape Recognition: A General Framework for Moment-Based Molecular Similarity.Journal of chemical information and modeling · 2025Article
- New strategies to enhance the efficiency and precision of drug discovery.Frontiers in pharmacology · 2025Review
- Benzoyl Valine Quasiracemates: Pairing CFCrystal growth & design · 2024Article
- Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Novel Efficient Multistage Lead Optimization Pipeline Experimentally Validated for DYRK1B Selective Inhibitors.Journal of medicinal chemistry · 2022Article
- The Main Protease of SARS-CoV-2 as a Target for Phytochemicals against Coronavirus.Plants (Basel, Switzerland) · 2022Review
- A multi-reference poly-conformational method forPeerJ · 2022Article
- Rapid Identification of Potential Drug Candidates from Multi-Million Compounds' Repositories. Combination of 2D Similarity Search with 3D Ligand/Structure Based Methods and In Vitro Screening.Molecules (Basel, Switzerland) · 2021Review
- Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products.Frontiers in chemistry · 2021Review
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Ultrafast Shape Recognition (USR), along with its derivatives, are Ligand-Based Virtual Screening (LBVS) methods that condense 3-dimensional information about molecular shape, as well as other properties, into a small set of numeric descriptors. These can be used to efficiently compute a measure of similarity between pairs of molecules using a simple inverse Manhattan Distance metric. In this study we explore the use of suitable Machine Learning techniques that can be trained using USR descriptors, so as to improve the similarity detection of potential new leads. We use molecules from the Directory for Useful Decoys-Enhanced to construct machine learning models based on three different algorithms: Gaussian Mixture Models (GMMs), Isolation Forests and Artificial Neural Networks (ANNs). We train models based on full molecule conformer models, as well as the Lowest Energy Conformations (LECs) only. We also investigate the performance of our models when trained on smaller datasets so as to model virtual screening scenarios when only a small number of actives are known
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