Interactive Molecular Discovery with Natural Language
Zheni Zeng Bangchen Yin Shipeng Wang Jiarui Liu Cheng Yang Haishen Yao Xingzhi Sun Maosong Sun Guotong Xie Zhiyuan Liu
Natural language is expected to be a key medium for various human-machine
interactions in the era of large language models. When it comes to the
biochemistry field, a series of tasks around molecules (e.g., property
prediction, molecule mining, etc.) are of great significance while having a
high technical threshold. Bridging the molecule expressions in natural language
and chemical language can not only hugely improve the interpretability and
reduce the operation difficulty of these tasks, but also fuse the chemical
knowledge scattered in complementary materials for a deeper comprehension of
molecules. Based on these benefits, we propose the conversational molecular
design, a novel task adopting natural language for describing and editing
target molecules. To better accomplish this task, we design ChatMol, a
knowledgeable and versatile generative pre-trained model, enhanced by injecting
experimental property information, molecular spatial knowledge, and the
associations between natural and chemical languages into it. Several typical
solutions including large language models (e.g., ChatGPT) are evaluated,
proving the challenge of conversational molecular design and the effectiveness
of our knowledge enhancement method. Case observations and analysis are
conducted to provide directions for further exploration of natural-language
interaction in molecular discovery.