Abstract
As large-scale language models continue to expand in size and diversity, their substantial potential and the relevance of their applications are increasingly being acknowledged. The rapid advancement of these models also holds profound implications for the design of stimulus-responsive materials used in drug delivery over the long term. To optimize large models for extensive dataset processing and comprehensive learning akin to a chemist’s intuition, integrating deeper chemical insights is imperative. Our study initially contrasted the performance of Bigbrid, Gemma, GPT NeoX, etc., specifically focusing on designing photoresponsive drug delivery molecules. We gathered excitation energy data through computational chemistry tools and further explored light-driven isomerisation reaction as a critical mechanism in drug delivery. Our study explored the effectiveness of incorporating human feedback into reinforcement learning to imbue large models with chemical intuition, improving their understanding of relationships involving -N=N- groups in photoisomerisa-tion transitions of light-responsive molecules. Despite progress, the limited availability of specialized domain datasets continues to be a significant challenge in maximizing the performance of large models.
