AI DRIVEN DRUG DISCOVERY FOR FAST, COST EFFECTIVE THERAPEUTIC DISCOVERY

Abstract

Artificial intelligence (AI) is transforming the landscape of drug discovery by enabling faster, more accurate, and cost-efficient identification of potential therapeutic compounds [1], [2]. Traditional drug development, often hindered by high expenses and long timelines, can be significantly accelerated through AI-driven computational modeling, molecular property prediction, and generative design algorithms [3], [4]. This study explores an AIbased framework that integrates deep learning, predictive analytics, and molecular simulation to identify novel drug candidates with optimized efficacy and reduced toxicity [5]–[7]. By leveraging large-scale biomedical data and computational intelligence, the model enhances target identification, minimizes experimental failures, and streamlines lead optimization [8]–[10]. The proposed framework demonstrates how AI can bridge the gap between data-rich biological research and practical therapeutic innovation [11], [12]. Advanced machine-guided prediction models facilitate improved screening accuracy and early identification of potential adverse drug interactions [13], [14]. Furthermore, the system integrates cheminformatics-guided approaches for molecular scaffold discovery and binding affinity optimization, enhancing both efficacy and safety in drug design [15]–[18]. Overall, this approach showcases how data-driven computational pharmacology can revolutionize the pharmaceutical pipeline, leading to faster and more affordable drug discovery adaptable to various disease domains [19]–[22].