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Optimization of compounds via machine learning and its application to drug discovery

Digital twins and cyber-physical systems that encompass technologies such as artificial intelligence (AI) and simulation engineering are attracting attention in order to further improve the efficiency of drug discovery and medical care. In the drug discovery process, the collection and AI-based analysis of big data such as omics information and Real World Data (RWD) are being used to improve the efficiency of drug discovery and the development of new modalities. In medical fields, digital technologies are being used to support early detection and diagnosis of diseases, medication management and dosing linked to Software as a Medical Device (SaMD), and Decentralized Clinical Trials. As described above, DX is advancing in drug discovery and medical care. However, since AI and IT technologies are advancing at an accelerating pace, it is essential to catch up with information on a daily basis, and furthermore, it is necessary to ensure rapid introduction of such technologies into drug discovery and medical care. In this symposium, titled "Advancement of AI in Drug Discovery and Medical Care", four symposiasts engaged in AI drug discovery and AI medical research are present, Dr. Masakazu Sekijima (Tokyo Institute of Technology), Dr. Yoshihiro Yamanishi (Kyushu Institute of Technology), Dr. Hidetoshi Arima (Daiichi Pharmaceutical Univ.), and Dr. Manabu Kano (Kyoto Univ.). We hope to share information and discuss with participants on AI in drug discovery and medical care through this symposium.

Advancement of AI in Drug Discovery and Medical Care

The 143rd Annual Meeting of the Pharmaceutical Society of Japan (Sapporo)

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[S07-01]Optimization of compounds via machine learning and its application to drug discovery

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