Data sharing and prediction of drug efficacy and ADMET using AI

<p>In recent years, data science has been attracting attention as a science and technology for discovering knowledge and creating new values from the explosive growth of big data in all fields. In the field of drug discovery and life sciences, the data explosion caused by the remarkable progress of high-throughput technology and omics measurement technology has made the research and development of big data science an urgent issue. In the face of such a wide variety and vast amount of data, artificial intelligence (AI) has been attracting worldwide attention as a technology for analyzing such big data. Big data, AI, and IoT are considered to be the core technologies of the fourth industrial revolution, and are about to bring social change to all fields and industries.</p><p>The speaker has been working on the application of artificial intelligence and machine learning technology to drug discovery for more than 10 years, and has developed technologies for screening active compounds and automated molecular design. In addition, in November of 2016, the Life Intelligence Consortium (LINC) was launched to promote AI development for the life science field through industry-academia collaboration, aiming to develop a series of AI for drug discovery processes.</p><p>However, several issues have become apparent through AI research and development to date. In particular, since AI is developed by learning from existing data, there is the issue of data volume, which greatly affects the performance of AI. Compared to overseas megapharmaceutical companies, Japanese pharmaceutical companies are small in scale and possess inferior amounts of data. Therefore, it is expected to develop high-performance drug discovery AI by sharing data from multiple companies that are in business competition.</p><p>In this presentation, I would like to introduce an AI technology for federated learning while maintaining confidentiality of data sharing among multiple institutions, and discuss data sharing among companies, using the development of AI for predicting drug efficacy and ADMET as an example.</p>