Y. Jung Group @ KAIST

*Organic AI*

1. Retrosynthesis: LocalRetro Paper: Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention. JACS Au, 2021 GitHub source


LocalRetro is an accurate machine learning-based AI for predicting the possible reactans for synthesizing a given moleucle (retrosynthesis) using graph neural networks (GNN) and local reaction tempalte.


2. Reaction prediction: LocalTransform Paper: A generalized-template-based graph neural network for accurate organic reactivity prediction, Nat Mach Intell, 2022 GitHub source


LocalTransform is an accurate machine learning-based AI for predicting possible outcomes of organic chemical reactions using graph neural networks (GNN) and generalized reaction tempalte.


*Inorganic AI*

1. Synthesizability prediction: PU_CGCNN Paper: Structure-based Synthesizability Prediction of Crystals using Partially Supervised Learning. J Am Chem Soc, 2020 GitHub source


PU-CGCNN is a python code for predicting CLscore (crystal-likeness score) which is quantitative synthesizability metric of inorganic crystals. This is a partially supervised machine learning protocol (PU-learning) using CGCNN classifier (by T. Xie et al.).



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Advanced Materials Simulations Group