The recent synergy of machine learning (ML) with molecular synthesis has emerged as an
increasingly powerful platform in organic synthesis and catalysis. This merger has set the …

In the development of transition metal catalysis, the process of ligand screening, where an
optimal ligand for a reaction of interest is identified from a large variety of candidate …

The efficient exploration of chemical space to design molecules with intended properties
enables the accelerated discovery of drugs, materials, and catalysts, and is one of the most …

The automated construction of datasets has become increasingly relevant in computational
chemistry. While transition-metal catalysis has greatly benefitted from bottom-up or top-down …

We present a de novo discovery of an efficient catalyst of the Morita–Baylis–Hillman (MBH)
reaction by searching chemical space for molecules that lower the estimated barrier of the …

Chiral ligands are important components in asymmetric homogeneous catalysis, but their
synthesis and screening can be both time-consuming and resource-intensive. Data-driven …

Rising antimicrobial resistance (AMR) and lack of innovation in the antibiotic pipeline
necessitate novel approaches to discovering new drugs. Metal complexes have proven to …

The computation of reaction selectivity represents an appealing complementary route to
experimental studies and a powerful means to refine catalyst design strategies. Accurately …

The prediction of reaction selectivity is a challenging task for computational chemistry, not
only because many molecules adopt multiple conformations but also due to the exponential …

This study introduces a novel approach for the de novo design of transition metal catalysts,
leveraging the power of genetic algorithms and density functional theory calculations. By …