  Large Language Models (LLMs) have demonstrated great performance in few-shot
In-Context Learning (ICL) for a variety of generative and discriminative
chemical design tasks. The newly expanded context windows of LLMs can further
improve ICL capabilities for molecular inverse design and lead optimization. To
take full advantage of these capabilities we developed a new semi-supervised
learning method that overcomes the lack of experimental data available for
many-shot ICL. Our approach involves iterative inclusion of LLM generated
molecules with high predicted performance, along with experimental data. We
further integrated our method in a multi-modal LLM which allows for the
interactive modification of generated molecular structures using text
instructions. As we show, the new method greatly improves upon existing ICL
methods for molecular design while being accessible and easy to use for
scientists.
