Integrative deep learning strategies to enhance early-stage drug discovery: optimizing computational structure-activity modeling for pharmacotherapeutic innovation.

The integration of computational intelligence into therapeutic development is increasingly important for accelerating early-stage drug discovery and improving compound prioritization. In this study, we developed an optimized neural network-based predictive framework to support the identification of bioactive compounds with analgesic potential. A dataset of 532 structurally diverse molecules described by 227 molecular descriptors was analyzed, and a stepwise feature elimination procedure reduced the descriptor set to 105 informative variables, improving model robustness and reducing redundancy. The optimized artificial neural network, trained using the Levenberg-Marquardt algorithm, achieved a correlation coefficient of 95.9% with a prediction error of 0.433%, outperforming conventional statistical approaches reported for comparable QSAR tasks. Additional analysis links key descriptor groups, including connectivity and polarity parameters, to physicochemical properties relevant to analgesic activity, improving interpretability for medicinal chemistry applications. The framework is intended to support computational screening and candidate prioritization prior to experimental validation, thereby contributing to more efficient pharmacotherapeutic discovery workflows. This work highlights how data-driven modeling can complement translational strategies aimed at accelerating drug discovery pipelines.