PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes
This work addresses synthetic chemistry tasks for researchers and practitioners, but it appears incremental as it builds on existing multimodal LLM approaches with specific enhancements.
The study tackled the problem of suboptimal performance in synthetic chemistry tasks by multimodal large language models (MLLMs) due to neglecting molecule graph interactions, and introduced PRESTO, a framework that integrates pretraining strategies and dataset configurations to achieve competitive results in these tasks.
Multimodal Large Language Models (MLLMs) have seen growing adoption across various scientific disciplines. These advancements encourage the investigation of molecule-text modeling within synthetic chemistry, a field dedicated to designing and conducting chemical reactions to synthesize new compounds with desired properties and applications. Current approaches, however, often neglect the critical role of multiple molecule graph interaction in understanding chemical reactions, leading to suboptimal performance in synthetic chemistry tasks. This study introduces PRESTO(Progressive Pretraining Enhances Synthetic Chemistry Outcomes), a new framework that bridges the molecule-text modality gap by integrating a comprehensive benchmark of pretraining strategies and dataset configurations. It progressively improves multimodal LLMs through cross-modal alignment and multi-graph understanding. Our extensive experiments demonstrate that PRESTO offers competitive results in downstream synthetic chemistry tasks. The code can be found at https://github.com/IDEA-XL/PRESTO.