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 …

The last few years have seen a steep rise in the use of data‐driven methods in different
scientific fields historically relying on theoretical or empirical approaches. Chemistry is at the …

The spatial optimization method between combinatorial optimization problems and GIS has
many geographical applications. The p-center problem is a classic NP-hard location …

A hyperheuristic homotopy algorithm (HH-HA) and a homotopy-based swarm intelligence
algorithm for parallel stochastic search are proposed to improve the search ergodicity and …

A critical problem in large neural networks is over parameterization with a large number of
weight parameters, which limits their use on edge devices due to prohibitive computational …

Mildew of maize seeds may affect their germination rates and reduce crop quality. It is
crucial to classify maize seeds efficiently and without destroying their original structure. This …

Equifinality and premature convergence can result in considerable errors when
simultaneously characterizing groundwater contamination sources and estimating …

De novo molecular design is the process of learning knowledge from existing data to
propose new chemical structures that satisfy the desired properties. By using de novo …

Hyperparameter optimization is a crucial problem in Evolutionary Computation. In fact, the
values of the hyperparameters directly impact the trajectory taken by the optimization …

Motivation The burgeoning field of target-specific drug design has attracted considerable
attention, focusing on identifying compounds with high binding affinity toward specific target …