Summary of Multimodal Large Language Models For Inverse Molecular Design with Retrosynthetic Planning, by Gang Liu et al.
Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning
by Gang Liu, Michael Sun, Wojciech Matusik, Meng Jiang, Jie Chen
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Chemical Physics (physics.chem-ph); Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Llamole, a multimodal large language model that can generate text and graphs simultaneously. The primary challenge is adapting image-based models to graph-based structures, which hinders applications in materials and drug design. To overcome this limitation, Llamole combines a base language model with the Graph Diffusion Transformer and Graph Neural Networks for multi-conditional molecular generation and reaction inference within texts. Additionally, it integrates A* search with LLM-based cost functions for efficient retrosynthetic planning. The authors create benchmarking datasets and conduct extensive experiments to evaluate Llamole against in-context learning and supervised fine-tuning. Llamole outperforms 14 adapted language models across 12 metrics for controllable molecular design and retrosynthetic planning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a super smart computer that can help scientists design new materials or drugs by understanding how they work. Right now, computers are great at working with words, but struggle to understand complex structures like molecules. The researchers in this paper created a new kind of computer program called Llamole that can work with both text and these molecular structures. This is important because it could help us design new materials or drugs more efficiently. They tested Llamole against other programs and found that it did better at designing new molecules and predicting how they would react. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Inference » Language model » Large language model » Supervised » Transformer
