Artificial intelligence and multi-omics in drug discovery: A deep learning-powered revolution

Abstract

In the pharmaceutical sector, artificial intelligence (AI) is changing the entire drug discovery and development process by contributing to speed, cost, and precision. This article illustrates the application of AI to early-stage target identification, molecular docking, and de novo drug design using deep learning and generative models trained on chemical and biological datasets. AI-enabled toxicity simulations and drug-drug interaction predictions during preclinical testing show potential as well. In addition to its use in early-stage drug development, AI supports drug repurposing and demonstrates its value during health crises by accelerating the development of therapeutics. The article also examined AI's use for improving clinical trial designs by recruiting patients efficiently, conducting real-time monitoring of subjects, and employing adaptive trial designs. The paper also discusses the important tools underpinning these advances, including but not limited to AlphaFold, Chemception, and graph neural networks to predict various protein structures and molecular properties. The merging of AI with nascent technologies, including quantum computing and biosensors, is producing advances in pharmacokinetics, toxicity prediction, and personalized medicine. Finally, it examined some important challenges such as data populating, model interpretability, and regulatory accountability, as well as ethical issues, but underscored the promise of AI to make therapeutic development less risky and more effective. Hence, AI does hold a transformative potential, but it is important to acknowledge that it is a complement and not a replacement for experimental research and domain expertise. The consensus is that AI, when applied responsibly and alongside traditional methods, can significantly enhance the drug development process and accelerate the delivery of safer, more effective therapeutics.

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