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- Classification of Genetic Information Using Machine Learning
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.
Content of 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.
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Classification of receptivity to various odorants using machine learning
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Validation by 3D structure modeling
Potential for social implementation
- ・Prediction of individual differences in the senses of taste and smell
- ・Prediction of individual differences in drug efficacy and side effects
Appealing points to industry and local governments
From the viewpoint of personal information protection, legal compliance is required for application to personally identifiable data, but the scope of application can be expanded by using the public genome diversity database.
It can also be applied to animals.
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