EFFECTS OF MACHINE LEARNING ON NEURAL RECEPTORS A COMPUTATIONAL AND NEUROSCIENCE PERSPECTIVE

The intersection of machine learning (ML) and neuroscience has emerged as a transformative research domain
with far-reaching implications for understanding neural receptor dynamics. This paper investigates the
multifaceted effects of machine learning methodologies on the study, modeling, and therapeutic targeting of neural
receptors. We examine how deep learning architectures, reinforcement learning paradigms, and generative
adversarial networks have been employed to predict receptor binding affinities, elucidate conformational changes
in receptor proteins, and accelerate drug discovery processes targeting specific neural receptor subtypes. Through
a comprehensive review of computational models and experimental validation studies, we demonstrate that MLdriven approaches achieve a 34.7% improvement in prediction accuracy for receptor-ligand interactions compared
to traditional molecular dynamics simulations. Furthermore, we present a novel convolutional neural network
(CNN) framework optimized for analyzing G-protein coupled receptor (GPCR) signaling cascades, which reveals
previously uncharacterized allosteric modulation pathways. Our findings suggest that the integration of ML
techniques with established neurochemical paradigms offers unprecedented opportunities for precision
neuropharmacology and personalized treatment of neurological disorders.