Yeonjoon Kim, Jin Woo Kim, Zeehyo Kim
Kyunghoon Lee, Hyeongwoo Kim, Seonghwan Kim, and Woo Youn Kim
Accurate reaction prediction based on quantum calculations requires a lot of effort, as those calculations are performed manually. Here, we aim to develop a fast and efficient method which automatically performs quantum calculations for an accurate reaction prediction, minimizing the manual efforts.
Our team already developed a software called “ACE-Reaction” which automatically and efficiently explores plausible reaction mechanisms for given reactants and products based on basic chemical heuristics and quantum calculations. We are further trying to improve the software by implementing automated TS search, and using Artificial Intelligence (AI) which predicts chemical reactivity.
Synthesis Planning (Retro-synthesis)
One of the main goals of chemistry is to synthesize desired molecules. For such synthesis, planning synthetic pathway is essential which often takes a lot of effort requiring knowledge of chemistry. To reduce such efforts in synthetic chemistry, our team is currently developing a CASP (Computer-Aided Synthesis Planning) for automated synthesis planning.
We are adopting machine learning technique to train neural networks to learn chemical knowledge from reported reaction database, searching synthetic pathways quickly and efficiently.
Requirements for Joining our Team
 Efficient prediction of reaction paths through molecular graph and reaction network analysis, Y. Kim, J. W. Kim, Z. Kim, W. Y. Kim, Chem. Sci. 2018, 9, 825–835
 Performance of ACE-Reaction on 26 Organic Reactions for Fully Automated Reaction Network Construction and Microkinetic Analysis, J. W. Kim, Y. Kim, K. Y. Baek, K. Lee, W. Y. Kim, J. Phys. Chem. A 2019, 123, 4796–4805
 Efficient construction of chemical reaction network guided by Monte Carlo tree search, K. Lee, J. W. Kim, Y. W. Kim, ChemSystemsChem, 2020, 2,