Models can codify our understanding of chemical reactivity and serve a useful purpose in
the development of new synthetic processes via, for example, evaluating hypothetical …

Machine Learning (ML) techniques face significant challenges when predicting advanced
chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction …

Abstract Large Language Models (LLMs) with strong abilities in natural language
processing tasks have emerged and have been applied in various kinds of areas such as …

There is significant interest and importance to develop robust machine learning models to
assist organic chemistry synthesis. Typically, task-specific machine learning models for …

Late-stage functionalization is an economical approach to optimize the properties of drug
candidates. However, the chemical complexity of drug molecules often makes late-stage …

Molecular representation learning (MRL) is a key step to build the connection between
machine learning and chemical science. In particular, it encodes molecules as numerical …

The application of computational methods in enantioselective catalysis has evolved from the
rationalization of the observed stereochemical outcome to their prediction and application to …

Transfer and active learning have the potential to accelerate the development of new
chemical reactions, using prior data and new experiments to inform models that adapt to the …

Burgeoning developments in machine learning (ML) and its rapidly growing adaptations in
chemistry are noteworthy. Motivated by the successful deployments of ML in the realm of …

Accurate prediction of reaction yield is the holy grail for computer-assisted synthesis
prediction, but current models have failed to generalize to large literature datasets. To …