Designing organic photoredox catalysts as a fragment-based puzzle solved by reinforcement learning
Year: 2026
Type: preprint
Abstract: Reinforcement learning (RL) is a promising strategy for identifying optimal solutions in vast search spaces. We have developed a framework in which the design of organic photoredox catalysts (OPCs) is deliberately formulated as a fragment-based puzzle that is well suited to RL. In this framework, an RL agent sequentially selects and orders molecular fragments and attempts to identify fragment combinations that afford high rewards, as calculated based on the similarity between the properties of the generated molecules and those of 4CzIPN, a well-known OPC. In addition to the rediscovery of a previously reported high-performance OPC, experimental validation of the proposed candidates revealed a TADF emitter with redox potentials similar to those of 4CzIPN, as well as an OPC with superior catalytic activity in several photoreactions.
Source: ChemRxiv
Institutions: Integrated Research Consortium on Chemical Sciences, Nagoya University, Nagoya University, Waseda University, Waseda Research Institute for Science and Engineering, Hokkaido University, Kyoto University, RIKEN Center for Biosystems Dynamics Research, Osaka Metropolitan University, Institute for Chemical Reaction Design and Discovery
Language: English
Subfield: Organic Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7221094065