Application of artificial intelligence (AI), particularly machine learning (ML), in medicine has significantly advanced drug discovery. When it comes to bridging the knowledge gap between possible therapeutic molecules and illness understanding, artificial intelligence is a potent catalyst. The most recent developments in artificial intelligence and its use in drug discovery are comprehensively summarized in this review. Starting with disease identification and covering diagnostics, target identification, screening, and lead discovery, different phases of the drug discovery process have been briefly discussed. In these phases, artificial intelligence's capacity to examine large datasets and identify trends is crucial, improving forecasts and productivity in the areas of disease detection, medication development, and clinical trial administration. It is made clear how AI may speed up drug development through the analysis of large amounts of data, which would cut down on the time and expense of introducing new drugs to the market. Significance of algorithm instruction, data quality, and ethical issues, particularly when managing information about patients in clinical trials is also of greater concern. AI has the potential to revolutionize medication development by taking these aspects into account, with significant benefits for both patients and society.
Ahmad et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: