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Classification of Genetic Information Using Machine Learning
Predicting the binding of compounds to cell receptors
Various receptors on the surface of cells play important roles in maintaining homeostasis and environmental responses, but it is difficult to identify compounds that can bind to them. We propose a method for narrowing down the candidates for binding compounds by using machine learning.
Research
Although the human genome has been deciphered and many of the genes have been elucidated, the structure and function of receptors, which play an important role in homeostasis and environmental responses, have not been fully elucidated, because most of them are membrane proteins and their expression levels are low. Many receptors, however, are expected to be major targets for drug discovery in the future because of their functional aspects, and are thought to be the factors that cause individual differences. We are applying machine learning technology to efficiently narrow down compounds that can bind to receptors.
Toshinori Endo Professor
