Discovering new reactions, optimizing their performance, and extending the synthetically
accessible chemical space are critical drivers for major technological advances and more …

Abstract Machine learning (ML) methods enable computers to address problems by learning
from existing data. Such applications are becoming commonplace in molecular sciences …

Small molecules exhibiting desirable property profiles are often discovered through an
iterative process of designing, synthesizing and testing sets of molecules. The selection of …

Catalytic reactions play a central role in many industrial processes, owing to their ability to
enhance efficiency and sustainability. However, complex interactions between the …

Recently, multi-objective optimization has garnered significant attention in the field of
reaction optimization. Various multi-objective optimization solvers, such as MVMOO …

Bayesian optimization (BO) is an efficient method for solving complex optimization problems,
including those in chemical research, where it is gaining significant popularity. Although …

Retrosynthesis aims to efficiently plan the synthesis of desirable chemicals by strategically
breaking down molecules into readily available building block compounds. Having a long …

Neural network applications, as an emerging machine learning technology, are increasingly
being integrated into pharmaceutical manufacturing technologies, offering significant …

The synthesis of thousands of candidate compounds in drug discovery and development
offers opportunities for computer-aided synthesis planning to simplify the synthesis of …

Generative chemistry, which uses computational approaches to explore large chemical
spaces, has gained considerable popularity in identifying potential lead candidates for drug …