The optimal design of compounds through manipulating properties at the molecular level is
often the key to considerable scientific advances and improved process systems …

Computational Chemistry is currently a synergistic assembly between ab initio calculations,
simulation, machine learning (ML) and optimization strategies for describing, solving and …

Computer-aided design of novel molecules and compounds is a challenging task that can
be addressed with quantum computing (QC) owing to its notable advances in optimization …

De novo design seeks to generate molecules with required property profiles by virtual
design-make-test cycles. With the emergence of deep learning and neural generative …

Machine learning enables computers to address problems by learning from data. Deep
learning is a type of machine learning that uses a hierarchical recombination of features to …

Conspectus Computer-aided synthesis planning (CASP) is focused on the goal of
accelerating the process by which chemists decide how to synthesize small molecule …

Computer-assisted design of small molecules has experienced a resurgence in academic
and industrial interest due to the widespread use of data-driven techniques such as deep …

Recent years have witnessed a boom of machine learning (ML) applications in chemistry,
which reveals the potential of data‐driven prediction of synthesis performance. Digitalization …

Chemical Product Engineering (CPE) is marked by numerous challenges, such as the
complexity of the properties–structure–ingredients–process relationship of the different …

Deep learning has become a powerful and frequently employed tool for the prediction of
molecular properties, thus creating a need for open-source and versatile software solutions …