Artificial intelligence (AI) presents the possibility to alter the drug discovery process and has grown significantly, in the past few years. Over 150 companies have applied AI-based drug discovery approaches to raise funds and progress molecules into clinical trials. Before diving into the use of Artificial Intelligence in drug discovery, a very high-level approach to traditional drug discovery methods needs to be understood as it involves several elaborate, expensive, and time-consuming steps. Target identification involves identifying the right biological target, which may be a gene, protein, or transcript involved in a physiological pathway. This first step is followed by hit identification, which involves going through many levels of screening for generating lead compounds. Despite this extensive process of evaluating and optimizing the lead candidate, there are always uncertainties about a candidate's progress to the next phase of development because of insufficient bioavailability, unacceptable toxicity, or the inability to replicate lab success in living systems. There has been considerable interest in deciphering ways to improve the success rate in drug discovery, and AI and Machine Learning (ML) advances can help at every stage of the drug discovery process The pharmaceutical industry generates hundreds of gigabytes of complex data of to identify… Read full this story
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