  High-throughput reaction condition (RC) screening is fundamental to chemical
synthesis. However, current RC screening suffers from laborious and costly
trial-and-error workflows. Traditional computer-aided synthesis planning (CASP)
tools fail to find suitable RCs due to data sparsity and inadequate reaction
representations. Nowadays, large language models (LLMs) are capable of tackling
chemistry-related problems, such as molecule design, and chemical logic Q\&A
tasks. However, LLMs have not yet achieved accurate predictions of chemical
reaction conditions. Here, we present MM-RCR, a text-augmented multimodal LLM
that learns a unified reaction representation from SMILES, reaction graphs, and
textual corpus for chemical reaction recommendation (RCR). To train MM-RCR, we
construct 1.2 million pair-wised Q\&A instruction datasets. Our experimental
results demonstrate that MM-RCR achieves state-of-the-art performance on two
open benchmark datasets and exhibits strong generalization capabilities on
out-of-domain (OOD) and High-Throughput Experimentation (HTE) datasets. MM-RCR
has the potential to accelerate high-throughput condition screening in chemical
synthesis.
