International Journal of Science, Engineering and TechnologyAuthors: Bhupendra Ram, Anurag Chandna, Sohan Lal
Abstract: Artificial Intelligence (AI) is the modern-day revolutionary force for drug discovery, offering a solution for the cost, time, and efficiency issues [1]. Unveiling a new drug through traditional pipelines takes over a decade and costs billions of dollars, and the high success rates in later stages have been failing [2]. The process of target identification, molecular design, and virtual screening is being transformed by AI-backed platforms and deep learning, graph neural networks (GNNs), and reinforcement learning (RL). The ability of algorithms to traverse large chemical spaces with greater precision and speed has been demonstrated by recent advances, such as AlphaFold in protein structure prediction and AI-aided molecule generation. The application of GANs and hybrid reinforcement learning methods to optimize molecules for both efficacy and safety is on the rise. In this paper, we present an overview of cutting-edge AI-enabled drug discovery platforms, highlight methodological advances, and propose a hybrid framework that integrates GNNs and generative models for efficient candidate optimization. Data privacy and replicability, as well as ethical and regulatory issues, are also discussed. Artificial intelligence drug discovery thus can lead to accelerated therapeutic development, cut costs, and enable personalized medicine advancements [3].
DOI: http://doi.org/10.5281/zenodo.20338788
