Multimodal Large Language Models for Inverse Molecular Design with
  Retrosynthetic Planning
Authors: name: Gang Liu, name: Michael Sun, name: Wojciech Matusik, name: Meng Jiang, name: Jie Chen
Organization: University of Notre Dame DM2 Lab

While large language models (LLMs) have integrated images, adapting them to
graphs remains challenging, limiting their applications in materials and drug
design. This difficulty stems from the need for coherent autoregressive
generation across texts and graphs. To address this, we introduce Llamole, the
first multimodal LLM capable of interleaved text and graph generation, enabling
molecular inverse design with retrosynthetic planning. Llamole integrates a
base LLM with the Graph Diffusion Transformer and Graph Neural Networks for
multi-conditional molecular generation and reaction inference within texts,
while the LLM, with enhanced molecular understanding, flexibly controls
activation among the different graph modules. Additionally, Llamole integrates
A* search with LLM-based cost functions for efficient retrosynthetic planning.
We create benchmarking datasets and conduct extensive experiments to evaluate
Llamole against in-context learning and supervised fine-tuning. Llamole
significantly outperforms 14 adapted LLMs across 12 metrics for controllable
molecular design and retrosynthetic planning.