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 this https URL

PRESTO 是一种新的框架，通过集成全面的预训练策略和数据集配置，逐步改进多模式 LLM，并通过跨模态对齐和多图理解来弥合分子 - 文本形态差距，从而在合成化学任务中提供了竞争性的结果。