To address the problem of side effects associated with schizophrenia medications, he constructed a predictive model using machine learning with the goal of identifying new drug candidates. By training the model on existing compound data to learn binding affinity and potency with receptors, and then applying it to numerous unevaluated molecules, he proposed candidate molecules that are expected to exhibit desirable effects with fewer side effects. Furthermore, a mechanism to evaluate prediction reliability was introduced, enabling the identification of compounds with high reliability and properties suitable for drug candidates. As a result, promising new drug candidates were successfully identified. The above research content and poster presentation were evaluated, and KIMURA received the Outstanding Student Award.
Winner: KIMURA Shoei (2nd-year pre-doctoral student, KANEKO Hiromasa Lab.)
Award name: Outstanding Poster Award
Conference name: The SCEJ 56th Autumn Meeting
Dates: September 16 to 18, 2025
Presentation title: Proposal of new drug candidates for schizophrenia by machine learning model
Presenters: ○KIMURA Shoei, KANEKO Hiromasa