Skip to main content Advertisement Search Log in Search SpringerLink Search Associated Content Part of a collection: AI and ML for Small Molecule Drug Discovery in the Big Data Era Original Article Published: 09 October 2021 Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors Odame Agyapong1,2, Whelton A. Miller3,4,5, Michael D. Wilson2,3 & Samuel K. Kwofie   ORCID: orcid.org/0000-0002-1093-15171,6  Molecular Diversity (2021)Cite this article 82 Accesses Metrics details Abstract Microtubules are receiving enormous interest in drug discovery due to the important roles they play in cellular functions. Targeting tubulin polymerization presents an excellent opportunity for the development of anti-tubulin drugs. Drug resistance and high toxicity of currently used tubulin-binding agents have necessitated the pursuit of novel drug candidates with increased therapeutic potency. The design of novel drug candidates can be achieved using efficient computational techniques to support existing efforts. Proteochemometric (PCM) modeling is a computational technique that can be employed to elucidate the bioactivity relations between related targets and multiple ligands. We have developed a PCM-based Support Vector Machine (SVM) approach for predicting the bioactivity between tubulin receptors and small, drug-like molecules. The bioactivity datasets used for training the SVM algorithm were obtained from the Binding DB database. The SVM-based PCM model yielded a good overall predictive performance with an area under the curve (AUC) of 87%, Matthews correlation coefficient (MCC) of 72%, overall accuracy of 93%, and a classification error of 7%. The algorithm allows the prediction of the likelihood of new interactions based on confidence scores between the query datasets, comprising ligands in SMILES format and protein sequences of tubulin targets. The algorithm has been implemented as a web server known as TubPred, accessible via http://35.167.90.225:5000/. This is a preview of subscription content, access via your institution. Access options Buy single article Instant access to the full article PDF. 34,95 € Price includes VAT (Ghana) Tax calculation will be finalised during checkout. Rent this article via DeepDyve. 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Wilson School of Engineering and Applied Science, Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA Whelton A. Miller Department of Molecular Pharmacology and Neuroscience, Loyola University Medical Center, Maywood, IL, 60153, USA Whelton A. Miller West African Centre for Cell Biology of Infectious Pathogens, Department of Biochemistry, Cell and Molecular Biology, College of Basic and Applied Sciences, University of Ghana, Accra, Ghana Samuel K. Kwofie Authors Odame Agyapong View author publications You can also search for this author in PubMed Google Scholar Whelton A. Miller View author publications You can also search for this author in PubMed Google Scholar Michael D. Wilson View author publications You can also search for this author in PubMed Google Scholar Samuel K. Kwofie View author publications You can also search for this author in PubMed Google Scholar Contributions SKK, MDW and OA conceptualized the project. OA and SKK undertook the computational work with inputs from WAM and MDW. OA and SKK co-wrote the first draft. All authors read, revised, and accepted the final draft for submission. SKK was the principal supervisor with MDW as the co-supervisor of the work. Corresponding author Correspondence to Samuel K. Kwofie. Additional information Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Reprints and Permissions About this article Cite this article Agyapong, O., Miller, W.A., Wilson, M.D. et al. Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors. Mol Divers (2021). https://doi.org/10.1007/s11030-021-10329-w Download citation Received: 29 March 2021 Accepted: 23 September 2021 Published: 09 October 2021 DOI : https://doi.org/10.1007/s11030-021-10329-w Share this article Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative Keywords Proteochemometric Support vector machine Tubulin Bioactivity Machine learning Associated Content Part of a collection: AI and ML for Small Molecule Drug Discovery in the Big Data Era Access options Buy single article Instant access to the full article PDF. 34,95 € Price includes VAT (Ghana) Tax calculation will be finalised during checkout. Rent this article via DeepDyve. 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