ADVANCEMENTS IN GEN AI FOR DRUG DISCOVERY ACCELERATING RESEARCH AND DEVELOPMENT
Keywords:
Machine Learning (ML) , Artificial Intelligence (AI), Drug DevelopmentAbstract
The application of machine learning (ML) and artificial intelligence (AI) in healthcare was a watershed moment in the history of medication development. Artificial intelligence serves as a significant catalyst in the process of reducing the information gap between the understanding of diseases and the identification of prospective therapeutic medicines. This paper offers a comprehensive description of the most recent developments in artificial intelligence and how they might be applied to the process of drug discovery. Starting with the diagnosis of the ailment and continuing through the identification of targets, screening, and lead finding, we examine the many steps involved in drug development. The capacity of artificial intelligence to sift through massive datasets in search of patterns is crucial throughout the phases of disease detection, drug development, and clinical trial administration. This allows for improved forecasts and increased efficiency. Emphasis is placed on the role that artificial intelligence plays in accelerating the discovery of new drugs, with particular attention paid to its capacity to analyse massive amounts of data, hence lowering the amount of time and money required to bring a new drug to market. One of the topics that is discussed is the significance of ethical issues, algorithm training, and data quality, particularly in relation to the handling of patient data during clinical trials. AI has the potential to revolutionise the process of medication development by taking into account these elements, which will result in major benefits for both patients and society.
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Copyright (c) 2024 Harish Narne (Author)
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