Computational Modeling and Biological Evaluation of Benzophenone Derivatives as Antileishmanial Agents

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Abstract
Leishmaniasis is a neglected tropical disease with limited therapeutic options characterized by high toxicity, adverse side effects, and growing resistance to existing treatments. In this study, machine learning (ML) methods were employed to design and evaluate benzophenone and xanthone derivatives as potential antileishmanial agents. A dataset of 73 compounds was curated, and Quantitative Structure-Activity Relationship (QSAR) models were developed using artificial neural networks (ANN), Random Forest (RF), and J48 decision tree classifiers. The ANN model achieved the highest accuracy (86.2%) in predicting antileishmanial activity, validated through in vitro assays. Among 14 newly synthesized benzophenones, compounds 5 and 7 demonstrated significant biological activity with inhibitory concentration 50 (IC50) values of 10.19 and 14.35 μM, respectively, and favorable selectivity indices compared to reference drugs pentamidine and amphotericin B. Structural analysis highlighted the importance of thiosemicarbazone and 4-methyl groups, alongside electronegative substituents at position 11, in enhancing activity. This study underscores the potential of computational tools to streamline the discovery of novel, effective, and selective antileishmanial agents.