Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective
Yuheng Ding,
Bo Qiang,
Qixuan Chen
et al.
Abstract:Chemical reactions serve as foundational building blocks for organic chemistry and drug design. In the era of large AI models, data-driven approaches have emerged to innovate the design of novel reactions, optimize existing ones for higher yields, and discover new pathways for synthesizing chemical structures comprehensively. To effectively address these challenges with machine learning models, it is imperative to derive robust and informative representations or engage in feature engineering using extensive da… Show more
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