There is a perceived dichotomy between structure-based and descriptor-based molecular
representations used for predictive chemistry tasks. Here, we study the performance …

There is a lack of scalable quantitative measures of reactivity that cover the full range of
functional groups in organic chemistry, ranging from highly unreactive C–C bonds to highly …

Deep graph neural networks are extensively utilized to predict chemical reactivity and
molecular properties. However, because of the complexity of chemical space, such models …

Conspectus Machine-readable chemical structure representations are foundational in all
attempts to harness machine learning for the prediction of reactivities, selectivities, and …

Machine learning (ML) and quantum mechanical (QM) methods can be used in two-way
synergy to build chemical reaction expert systems. The proposed ML approach identifies …

The reliable prediction of chemical reactivity remains in the realm of knowledgeable
synthetic chemists. Automating this process by using artificial intelligence could accelerate …

In a departure from conventional chemical approaches, data-driven models of chemical
reactions have recently been shown to be statistically successful using machine learning …

The estimation of chemical reaction properties such as activation energies, rates, or yields is
a central topic of computational chemistry. In contrast to molecular properties, where …

Machine learning (ML) approaches have demonstrated the ability to predict molecular
spectra at a fraction of the computational cost of traditional theoretical chemistry methods …

Physics-based representations constructed using only atomic positions and nuclear charges
(also known as quantum machine learning, QML) allow for the reliable and efficient …