Crossing New Frontiers: Knowledge-Augmented Large Language Model Prompting for Zero-Shot Text-Based De Novo Molecule Design
<|reference_start|>Crossing New Frontiers: Knowledge-Augmented Large Language Model Prompting for Zero-Shot Text-Based De Novo Molecule Design: Molecule design is a multifaceted approach that leverages computational methods and experiments to optimize molecular properties, fast-tracking new drug discoveries, innovative material development, and more efficient chemical processes. Recently, text-based molecule design has emerged, inspired by next-generation AI tasks analogous to foundational vision-language models. Our study explores the use of knowledge-augmented prompting of large language models (LLMs) for the zero-shot text-conditional de novo molecular generation task. Our approach uses task-specific instructions and a few demonstrations to address distributional shift challenges when constructing augmented prompts for querying LLMs to generate molecules consistent with technical descriptions. Our framework proves effective, outperforming state-of-the-art (SOTA) baseline models on benchmark datasets.<|reference_end|>
@article{srinivas2024crossing,
    title={Crossing New Frontiers: Knowledge-Augmented Large Language Model
  Prompting for Zero-Shot Text-Based De Novo Molecule Design},
    author={Sakhinana Sagar Srinivas, Venkataramana Runkana},
    journal={arXiv preprint arXiv:2408.11866},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11866},
    primaryClass={cs.CL cs.AI cs.LG q-bio.BM}
}