REVOLUTIONIZING DRUG DEVELOPMENT: HOW AI AND MACHINE LEARNING ARE SHAPING THE FUTURE OF MEDICINE-A REVIEW
DOI:
https://doi.org/10.22159/ijap.2025v17i3.53457Keywords:
Artificial intelligence, Machine learning, Drug development, Preclinical studies, Clinical studiesAbstract
AI and Machine Learning are revolutionizing drug development, which was previously characterized as being complicated, time-consuming, and costly. Through the application of enormous biomedical data and high-performance computing capabilities, AI/ml algorithms speed up different aspects of drug discovery, such as target identification, lead optimization, and clinical trial design. The technologies uncover intricate biological patterns, making it possible to have personalized treatments and more effective drug targeting at the bedside.
AI methods have demonstrated phenomenal success in protein structure prediction, virtual compound screening, and de novo drug design, shortening project duration from years to weeks. Some of the highlights are Alpha Fold's record-breaking accuracy in protein structure predictions by DeepMind and Deep Tox's 86% accuracy for predicting toxicity. Neural networks have reached 92% accuracy in predicting protein-protein interactions, while AI integrated with organ-on-chip systems has cut down early-stage drug screening time by 60% and enhanced prediction accuracy by 40%. Joint efforts such as melloddy allow the pharmaceutical industry to collaborate without compromising data privacy and security measures.
However considerable issues remain around data quality, interpretability of models, and regulation compliance in the pharma industry. Advances in AI in the future, fueled by cross-disciplinary cooperation and ongoing technological progress, will play a vital role in surmounting these issues and making AI/ml bring about revolutionary changes in drug discovery to make therapies more available and affordable to global patients. The science keeps on unfolding, tipping the balance between potential transformation and built-in complexities in pharmaceutical usage.
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