Young Scientist Award Acceptance Speech		[Big hall]　10/26
	AI-Driven Molecular and Materials Design at the Edge of the Known
	Kei Terayama
	Graduate School of Medical Life Science, Yokohama City University / School of Life Science and Technology, Institute of Science Tokyo

 Data-driven predictive and generative models capture structures and correlations contained in training data and can exhibit strong performance in regions where such tendencies are preserved. In drug discovery, life science, and materials science, however, “discoveries” often appear slightly outside such well-supported and ordinary regions. These regions lie at the interface between existing knowledge and unknown possibilities, but they are also where the reliability of predictive models can become uncertain. In this talk, I will introduce our explorations and trials in searching such regions using AI, informatics, and simulation.

   I will first focus on the ChemTS series, reinforcement-learning-based molecular generative AI frameworks, and describe their basic concepts, applications to the design and validation of drug candidates and fluorescent molecules, and limitations revealed through these studies. I will then discuss recent developments, including DyRAMO for addressing applicability-domain issues and reward hacking, PROTAC linker design, and ChemTSv3 for more flexible exploration of chemical space. Beyond molecular generation, I will also introduce attempts to explore boundary-like or less conventional regions, including active-learning-based phase diagram construction, BLOX for searching exceptional candidates, and Bayesian optimization for molecular and materials design. Finally, I will introduce SELLM, a method that uses large language models to generate research ideas from different disciplinary perspectives, and discuss how AI may broaden the landscape of scientific discovery.

[1] S. Ishida, T. Aasawat, M. Sumita, M, Katouda, T. Yoshizawa, K. Yoshizoe, K. Tsuda, K. Terayama, “ChemTSv2: Functional molecular design using de novo molecule generator”, WIRES Comput. Mol. Sci. 2023, 13, e1680.
[2] T. Yoshizawa, S. Ishida, T. Sato, M. Ohta, T. Honma, K. Terayama, “A data-driven generative strategy to avoid reward hacking in multi-objective molecular design”, Nat. Commun., 2025, 16, 2409.
[3] Y. Murakami, S. Ishida, N. Cho, H. Yuki, M. Ohta, T. Honma, Y. Demizu, K. Terayama, “Data-Driven Design of PROTAC Linkers to Improve PROTAC Cell Membrane Permeability”, JACS Au, 2026, 6(2), 1400-1410.
[4] K. Terayama, M. Sumita, R. Tamura, D. T. Payne, M. K. Chahal, S. Ishihara, K. Tsuda, “Pushing property limits in materials discovery via boundless objective-free exploration”, Chem. Sci. 2020, 11, 5959–5968.
[5] H. Tomita, N. Nakamura, S. Ishida, T. Kamiya, K. Terayama, “Extracting effective solutions hidden in large language models via generated comprehensive specialists: case studies in developing electronic devices”, Commun. Mater., 2025, 6, 207.