ORIGINAL RESEARCH article
Front. Chem.
Sec. Medicinal and Pharmaceutical Chemistry
Volume 13 - 2025 | doi: 10.3389/fchem.2025.1600945
This article is part of the Research TopicMedicinal Chemistry for Neglected Tropical Diseases Using In-vitro, In-vivo and In Silico ApproachesView all 8 articles
Two-Dimensional QSAR-Driven Virtual Screening for Potential Therapeutics against Trypanosoma cruzi
Provisionally accepted
- 1Department of Basic Medical Science, College of Applied Medical Science, King Khalid University, Abha, Saudi Arabia
- 2Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham University, Kochi, Kerala, India
- 3Jamia Hamdard University, New Delhi, National Capital Territory of Delhi, India
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Trypanosoma cruzi is the cause of Chagas disease (CD), a major health issue that affects 6-7 million individuals globally. Once considered a local problem, migration and non-vector transmission have caused it to spread. Efforts to eliminate CD remain challenging due to insufficient awareness, inadequate diagnostic tools, and limited access to healthcare, despite its classification as a neglected tropical disease (NTD) by the WHO. One of the foremost concerns remains the development of safer and more effective anti-Chagas therapies. In our study, we developed a standardized and robust machine learning-driven QSAR (ML-QSAR) model using a dataset of 1183 T. cruzi inhibitors curated from the ChEMBL database to speed up the drug discovery process. Following the calculation of molecular descriptors and feature selection approaches, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) models were developed and optimized to elucidate and predict the inhibition mechanism of novel inhibitors. The ANN-driven QSAR model utilizing CDK fingerprints exhibited the highest performance, proven by a Pearson correlation coefficient of 0.9874 for the training set and 0.6872 for the test set, demonstrating exceptional prediction accuracy.Twelve possible inhibitors with pIC50 ≥ 5 were further identified through screening of large chemical libraries using the ANN-QSAR model and ADMET-based filtering approaches. Molecular docking studies revealed that F6609-0134 was the best hit molecule. Finally, the stability and high binding affinity of F6609-0134 were further validated by molecular dynamics simulations and free energy analysis, bolstering its continued assessment as a possible treatment option for Chagas disease.
Keywords: Chagas Disease, Trypanosoma cruzi, quantitative structure activity relationships, machine learning, artificial neural network, Virtual Screening, molecular docking, molecular dynamics
Received: 27 Mar 2025; Accepted: 27 May 2025.
Copyright: © 2025 Maliyakkal, KUMAR, Bhowmik, Vishwakarma, Yadav and Mathew. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Bijo Mathew, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham University, Kochi, 682 041, Kerala, India
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