Reaction condition- and functional group-specific knowledge discovery: Data- and computation-based analysis on transition-metal-free transformation of organoborons

Abstract

Gaining insights into overarching trends in chemical reaction systems is crucial for refining reaction conditions and developing novel reactions. These knowledgements include preferences for certain reagents, solvents, and functional group tolerance rules. Traditionally, synthetic chemists have relied on extensive literature searching to acquire the knowledge, a process that is both time-consuming and laborious. To streamline this process, we construct a standardized dataset and knowledge graph on an emerging domain, transition-metal-free transformations with organoborons. The dataset, compiled from organic reaction literature, includes comprehensive details of reaction scopes and conditions. The subsequent construction of a knowledge graph offers a visual representation of the reactions and their interrelationships. Through knowledge graph-based hierarchical analysis and density functional theory (DFT) calculations, we revealed the currently most frequently used reactants, synthetic conditions, and functional group rules in this field. We anticipate this knowledge graph-based approach will accelerate the acquisition and transfer of chemical reaction knowledge, catalyzing the discovery of new reactions. This work provides an automatic and adaptive framework for extracting key insights from reaction datasets to inform the design of novel reactions.

Keywords

• Data-driven knowledge discovery
• Knowledge graph
• Organoboron chemistry
• DFT
• Calculation