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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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Vahedi N, Mohammadhosseini M, Nekoei M. QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches. CURR ANAL CHEM 2020. [DOI: 10.2174/1573411016999200518083359] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily
present in eukaryotes.
Methods:
In the present report, some efficient linear and non-linear methods including multiple linear
regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully
used to develop and establish quantitative structure-activity relationship (QSAR) models
capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP
inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set
and selection of the training and test sets. A genetic algorithm (GA) variable selection method was
employed to select the optimal subset of descriptors that have the most significant contributions to
the overall inhibitory activity from the large pool of calculated descriptors.
Results:
The accuracy and predictability of the proposed models were further confirmed using crossvalidation,
validation through an external test set and Y-randomization (chance correlations) approaches.
Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed
models. The results revealed that non-linear modeling approaches, including SVM and ANN
could provide much more prediction capabilities.
Conclusion:
Among the constructed models and in terms of root mean square error of predictions
(RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for
the training set, the predictive power of the GA-SVM approach was better. However, compared with
MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.
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Affiliation(s)
- Nafiseh Vahedi
- Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran
| | - Majid Mohammadhosseini
- Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran
| | - Mehdi Nekoei
- Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran
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Perrone S, Troisi L, Salomone A. Heterocycle Synthesis through Pd-Catalyzed Carbonylative Coupling. European J Org Chem 2019. [DOI: 10.1002/ejoc.201900439] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Serena Perrone
- Dipartimento di Scienze e Tecnologie Biologiche ed Ambientali; Università del Salento; Campus Ecotekne, Prov.le Lecce-Monteroni 73100 Lecce Italy
| | - Luigino Troisi
- Dipartimento di Scienze e Tecnologie Biologiche ed Ambientali; Università del Salento; Campus Ecotekne, Prov.le Lecce-Monteroni 73100 Lecce Italy
| | - Antonio Salomone
- Dipartimento di Scienze e Tecnologie Biologiche ed Ambientali; Università del Salento; Campus Ecotekne, Prov.le Lecce-Monteroni 73100 Lecce Italy
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Naushad SM, Janaki Ramaiah M, Stanley BA, Prasanna Lakshmi S, Vishnu Priya J, Hussain T, Alrokayan SA, Kutala VK. In silico approaches to identify the potential inhibitors of glutamate carboxypeptidase II (GCPII) for neuroprotection. J Theor Biol 2016; 406:137-42. [DOI: 10.1016/j.jtbi.2016.07.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 07/09/2016] [Accepted: 07/13/2016] [Indexed: 11/30/2022]
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