2025-12-22 北海道大学
化学的知識を機械可読なルールに変換し、反応探索をインテリジェントに導く。
＜関連情報＞
- https://www.hokudai.ac.jp/news/2025/12/post-2151.html
- https://www.hokudai.ac.jp/news/pdf/251222_pr3.pdf
- https://pubs.acs.org/doi/10.1021/acscatal.5c06298
ケムオントロジー：化学者の知識を使って反応経路探索を⾼速化する新⼿法 ChemOntology: A Reusable Explicit Chemical Ontology-Based Method to Expedite Reaction Path Searches
Pinku Nath,Yuriko Ono,Yu Harabuchi,Yasunori Yamamoto,Satoshi Maeda,Tetsuya Taketsugu,and Masaharu Yoshioka
ACS Catalysis Published: December 21, 2025
DOI:https://doi.org/10.1021/acscatal.5c06298
Abstract
ChemOntology is a computational framework developed to extract and apply chemical knowledge from reaction intermediates generated during automated reaction path searches. By integrating chemical and geometric knowledge generated using chemical ontology and topology, ChemOntology identifies chemically relevant reaction paths and geometries to guide the reaction path search. Combined with an automated reaction path search method, Artificial Force Induced Reaction (AFIR) and applied to the Heck reaction, the ChemOntology-AFIR approach efficiently identified all key intermediates and elementary steps, including major and side products, even in high-energy regions where conventional AFIR need much larger computational efforts for extensive conformational sampling. This knowledge-driven approach significantly reduces computational costs by eliminating chemically irrelevant paths and structures. ChemOntology relies on three main inputs: the reaction setup, chemically informed assumptions encoded as Elementary Reaction Process Ontologies (ERPOs), and a set of reaction rules. Together, these enable efficient use of extracted knowledge to accelerate reaction exploration. Unlike machine learning models, it requires no training on data sets and is broadly applicable to a wide range of organometallic systems, offering a robust tool for mechanistic analysis and rational catalyst design, especially in systems where human insight remains indispensable.
