The factors determining a drug's success are manifold, making de novo drug design an
inherently multi-objective optimisation (MOO) problem. With the advent of machine learning …

Molecular discovery is a multi-objective optimization problem that requires identifying a
molecule or set of molecules that balance multiple, often competing, properties. Multi …

The discovery of new drugs has important implications for human health. Traditional
methods for drug discovery rely on experiments to optimize the structure of lead molecules …

de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting
objectives. Since several desired properties can be considered in the optimization process …

Understanding structure‐activity landscapes is essential in drug discovery. Similarly, it has
been shown that the presence of activity cliffs in compound data sets can have a substantial …

The discovery of transition metal complexes (TMCs) with optimal properties requires large
ligand libraries and efficient multiobjective optimization algorithms. Here we provide the …

Drug discovery is an expensive and risky process. To combat the challenges in drug
discovery, an increasing number of researchers and pharmaceutical companies recognize …

Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse
molecular design. However, GAs have multiple hyperparameters that have not been …

The quantification of molecular similarity has been present since the beginning of
cheminformatics. Although several similarity indices and molecular representations have …

Innovative hybrid distillation/crystallization processes offer significant energy saving and
cost reduction for the separation of isomer mixtures. Despite of the enormous potential of …