MOLECULAR PATHWAY PREDICTION USING REINFORCEMENT LEARNING FOR THERAPEUTIC TARGET DISCOVERY

A major hurdle for therapeutic target identification is the intricacy of biological pathways underpinning disease processes. Conventional methods often overlook the dynamic and interdependent character of these routes. The paper presents RL-MPTT, a new framework that uses reinforcement learning (RL) to forecast changes in molecular pathways (MP) and find important therapeutic targets (TT). Molecular networks derived from freely accessible pathway and interaction datasets are navigated and optimized by the RL-MPTT using RL agents. Nodes in the networks stand for molecules and edges for interactions; the networks were built using curated datasets such as Reactome. To achieve therapeutic goals like route stabilization or the suppression of disease-associated activity, the RL agent engages with these networks by mimicking interventions, such as stimulating or inhibiting particular nodes. Genomics data, such as gene expression profiles, were included to improve biological integrity and guarantee pathway relevance. The RL-MPTT also uses computational and experimental validation to verify the biological plausibility of anticipated targets. The results show that the RL model is predictively powerful since it reliably finds important routes corresponding to recognized treatment targets. Furthermore, this method finds new targets that could be the basis for future therapeutic development in neurological disorders and cancer. RL-MPTT shows how reinforcement learning may change the game for finding therapeutic targets. It can make predictions about molecular route dynamics much more accurate and scalable.