AI DRIVEN DRUG DISCOVERY FOR FAST, COST EFFECTIVE THERAPEUTIC DISCOVERY

Le résumé fourni par la source

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].

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.