Despite considerable progress in genome-and proteome-based high-throughput screening
methods and in rational drug design, the increase in approved drugs in the past decade did …

Introduction: Neural networks are becoming a very popular method for solving machine
learning and artificial intelligence problems. The variety of neural network types and their …

Discovering and accurately locating drug targets is of great significance for the research and
development of new drugs. As a different approach to traditional drug development, the …

One of the most extended subfields of chemometrics, at least by considering the number of
publications and interested researchers, is QSAR/QSPR. During the time, various improved …

The majority of computational methods for predicting toxicity of chemicals are typically based
on “nonmechanistic” cheminformatics solutions, relying on an arsenal of QSAR descriptors …

Hepatic toxicity is a key concern for novel pharmaceutical drugs since it is difficult to
anticipate in preclinical models, and it can originate from pharmacologically unrelated drug …

The chemical modification of histones at specific DNA regulatory elements is linked to the
activation, inactivation and poising of genes. A number of tools exist to predict enhancers …

In silico methods like molecular docking and pharmacophore modeling are established
strategies in lead identification. Their successful application for finding new active molecules …

Quantitative structure–property/activity relationships (QSPRs/QSARs) are a tool (in silico) to
rapidly predict various endpoints in general, and drug toxicity in particular. However, this …

Genetic algorithms (GAs) are a helpful tool in optimization, simulation, modelling, design,
and prediction purposes in various domains of science including materials science …