International Journal of Contemporary Research in Multidisciplinary

Abstract:

As the union of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionized the drug discovery sector, it has resulted in the radicalization of the manner in which the procedure now finds new treatments. AI greatly helps in the identification process of potential drug candidates, increasing the molecular design accuracy and reinforcing the predictability of the results in clinical trials, by automating and optimizing the things that were traditionally complex and lengthy. Through this technological synergy, however, researchers can work with voluminous data sets, model interactions within a molecule, and predict whether a drug might be toxic or effective even at a stage before clinical phases commence. Consequently, the total cost, time and the drug failure rate can be significantly lowered. The present paper discusses how artificial intelligence is being used in most areas of drug discovery, including target identification, compound screening, and personalized medicine. It also discusses the challenges that are present (concerning data quality, ethical issues), and the requirement of interdisciplinary collaboration. Lastly, the paper concludes with some positive features of AI that can help speed up the process of finding innovative treatments of various diseases, which will eventually result in better global health outcomes.

Keywords:

Artificial Intelligence (AI), Machine Learning (ML), Drug Discovery, Predictive Modeling, Molecular Design, Personalized Medicine

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Methods