Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data (2023)

Abstract: Chemical reactions, as a core entity in the realm of chemistry, hold crucial implications in diverse areas ranging from hands-on laboratory research to advanced computational drug design. Despite a burgeoning interest in employing NLP techniques to extract these reactions, aligning this task with the real-world requirements of chemistry practitioners remains an ongoing challenge. In this paper, we present Reaction Miner, a system specifically designed to interact with raw scientific literature, delivering precise and more informative chemical reactions. Going beyond mere extraction, Reaction Miner integrates a holistic workflow: it accepts PDF files as input, bypassing the need for pre-processing and bolstering user accessibility. Subsequently, a text segmentation module ensures that the refined text encapsulates complete chemical reactions, augmenting the accuracy of extraction. Moreover, Reaction Miner broadens the scope of existing pre-defined reaction roles, including vital attributes previously neglected, thereby offering a more comprehensive depiction of chemical reactions. Evaluations conducted by chemistry domain users highlight the efficacy of each module in our system, demonstrating Reaction Miner as a powerful tool in this field.

## introduction chemical reactions lie at the heart of chemistry, representing the transformative processes that give birth to new substances. the structured format of these reactions paves the way for diverse applications, including synthesis planning @xcite , reaction prediction @xcite , and reaction condition recommendation @xcite 1 code, data, and models can be found at: https:// github.com/maszhongming/reactionminer . the link to the video that introduces our system is at: https://youtu. be/q7p6nwdkcxw ## method in this section, we start with the task formulation to provide an overarching perspective of reaction miner, subsequently delving into each module. scientific paper step 1: pdf-to-text step 2: text segmentation raw text localization boundary detection step 3: role enrichment description • product • reactants • time • temperature • solvent • yield • speed • inhibitor • light condition • ph • cooling condition pre-defined roles enriched roles training step 4: unified reaction extraction unified data llama keyword topics ## experiments in this section, we evaluate the performance of re-action miner by testing its three core modules. ## conclusion in our exploration, we present reaction miner, an integrated system adept at extracting chemical reactions directly from raw scientific pdfs. beyond mere extraction, it offers enhanced accuracy by broadening the scope of reaction roles and eliminating prior gaps. feedback from chemistry experts marks it as a powerful tool for the field.