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Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis

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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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Acknowledgements

VAD, AG, and USN M acknowledge the funding from the Ministry of Electronics and Information Technology (MeitY), Govt. of India, New Delhi (project reference number No(4)12/2021-ITEA). BUH and KDS acknowledge NIPER Guwahati for financial support and travel grants.

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  1. Anish Gomatam and Bhakti Umesh Hirlekar have equally contributed to this work.

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  1. 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

    Anish Gomatam, Bhakti Umesh Hirlekar, Krishan Dev Singh, Upadhyayula Suryanarayana Murty & Vaibhav A. Dixit

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Contributions

VAD (PI) conceptualized the work and wrote the article and supervised the work done by AG, BUH, and KDS. AG collected the dataset for and developed the regression models, wrote python scripts, performed MMPA analysis and model interpretation, wrote the manuscript, and participated in offline/online discussions. BUH and KDS collected the data for and developed classification models, built and tested KNIME workflows, wrote the manuscript, and participated in offline/online discussions. USN is the coordinator of the MeitY-sponsored project and participated in offline/online discussions. #AG and BUH contributed equally to this work. All authors have read and agreed with the contents of the manuscript.

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Correspondence to Vaibhav A. Dixit.

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Gomatam, A., Hirlekar, B.U., Singh, K.D. et al. Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis. Mol Divers (2024). https://doi.org/10.1007/s11030-024-10809-9

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