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Machine learning and deep learning for drug discovery and pharmaceutical technology innovation

The pharmaceutical research and drug development can be roughly divided into two fields: drug discovery and pharmaceutical technology innovation. In the field of drug discovery, machine learning has been used in the past to study quantitative structure-activity relationships, and more recently, artificial intelligence (AI) has been used to predict drug-target-molecule interactions, efficacy, safety, drug repositioning, and ADMET. In the field of pharmaceutical technology innovation, the design of optimal dosage forms and the development of new DDS have been considered by using AI. In addition, it is necessary that individual researchers and developers become more literate in AI. From this backdrop, we have planned four symposia in this symposium with the aim of understanding the current status and future development of the use of AI in both fields. Dr. Arima presents “Machine learning and deep learning for drug discovery and pharmaceutical technology innovation”, Dr. Yamanishi gives a talk on “Data-driven drug discovery using machine learning”, Dr. Sekijima introduces “Application and sophistication of artificial intelligence in new drug research and development”, and Dr. Sese describes “Artificial intelligence technology that should be used by the members of the Pharmaceutical Society of Japan who are focused on experiments”. We hope that this symposium will be of assistance to the participants in the pharmaceutical research and drug development. 

Use of artificial intelligence in drug discovery and pharmaceutical technology innovation

The 141st Annual Meeting of the Pharmaceutical Society of Japan (Hiroshima)

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[S12-1]Machine learning and deep learning for drug discovery and pharmaceutical technology innovation

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