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。

PRESTO：渐进预训练改善合成化学结果

PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes

Molecule-text modeling, which aims to facilitate molecule-relevant tasks with
a textual interface and textual knowledge, is an emerging research direction.
Beyond single molecules, studying reaction-text modeling holds promise for
helping the synthesis of new materials and drugs. However, previous works
mostly neglect reaction-text modeling: they primarily focus on modeling
individual molecule-text pairs or learning chemical reactions without texts in
context. Additionally, one key task of reaction-text modeling -- experimental
procedure prediction -- is less explored due to the absence of an open-source
dataset. The task is to predict step-by-step actions of conducting chemical
experiments and is crucial to automating chemical synthesis. To resolve the
challenges above, we propose a new pretraining method, ReactXT, for
reaction-text modeling, and a new dataset, OpenExp, for experimental procedure
prediction. Specifically, ReactXT features three types of input contexts to
incrementally pretrain LMs. Each of the three input contexts corresponds to a
pretraining task to improve the text-based understanding of either reactions or
single molecules. ReactXT demonstrates consistent improvements in experimental
procedure prediction and molecule captioning and offers competitive results in
retrosynthesis. Our code is available at this https URL

分子 - 文本建模是一个新兴的研究方向，旨在通过文本界面和文本知识促进与分子相关的任务。针对单个分子之外，研究反应 - 文本建模对于帮助合成新材料和药物具有潜力，然而，以往的研究大多忽视了反应 - 文本建模。为了解决这些挑战，我们提出了一种新的预训练方法 ReactXT，用于反应 - 文本建模，并提供了一个新的数据集 OpenExp，用于实验过程预测。我们的方法在实验过程预测、分子字幕生成和逆向合成方面表现出一致的改进，并取得了竞争性的结果。