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Gomatam A, Hirlekar BU, Singh KD, Murty US, Dixit VA. Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis. Mol Divers 2024:10.1007/s11030-024-10809-9. [PMID: 38374474 DOI: 10.1007/s11030-024-10809-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/07/2024] [Indexed: 02/21/2024]
Abstract
The poly (ADP-ribose) polymerase-1 (PARP-1) enzyme is an important target in the treatment of breast cancer. Currently, treatment options include the drugs Olaparib, Niraparib, Rucaparib, and Talazoparib; however, these drugs can cause severe side effects including hematological toxicity and cardiotoxicity. Although in silico models for the prediction of PARP-1 activity have been developed, the drawbacks of these models include low specificity, a narrow applicability domain, and a lack of interpretability. To address these issues, a comprehensive machine learning (ML)-based quantitative structure-activity relationship (QSAR) approach for the informed prediction of PARP-1 activity is presented. Classification models built using the Synthetic Minority Oversampling Technique (SMOTE) for data balancing gave robust and predictive models based on the K-nearest neighbor algorithm (accuracy 0.86, sensitivity 0.88, specificity 0.80). Regression models were built on structurally congeneric datasets, with the models for the phthalazinone class and fused cyclic compounds giving the best performance. In accordance with the Organization for Economic Cooperation and Development (OECD) guidelines, a mechanistic interpretation is proposed using the Shapley Additive Explanations (SHAP) to identify the important topological features to differentiate between PARP-1 actives and inactives. Moreover, an analysis of the PARP-1 dataset revealed the prevalence of activity cliffs, which possibly negatively impacts the model's predictive performance. Finally, a set of chemical transformation rules were extracted using the matched molecular pair analysis (MMPA) which provided mechanistic insights and can guide medicinal chemists in the design of novel PARP-1 inhibitors.
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Affiliation(s)
- Anish Gomatam
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Govt. of India, Sila Katamur (Halugurisuk), Dist: Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India
| | - Bhakti Umesh Hirlekar
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Govt. of India, Sila Katamur (Halugurisuk), Dist: Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India
| | - Krishan Dev Singh
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Govt. of India, Sila Katamur (Halugurisuk), Dist: Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India
| | - Upadhyayula Suryanarayana Murty
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Govt. of India, Sila Katamur (Halugurisuk), Dist: Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India
| | - Vaibhav A Dixit
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Govt. of India, Sila Katamur (Halugurisuk), Dist: Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India.
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