Two-dimensional QSAR-driven virtual screening for potential therapeutics against .

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 1,183 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 pIC ≥ 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.