AMSG AI-Lab (beta)
We develop ML algorithms to accelerate the chemical research. The AI-LAB currently includes user-friendly AI modules for retrosynthesis and reaction prediction of organic molecules, and synthesizability prediction of inorganic crystal structures. More modules with GUI will be updated in due course.
|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.|
|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.|
|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.).|
|The results of any AI model can be downloaded and used by each user under CC-BY license|