The interest in the application of machine learning techniques (MLT) as drug design tools is
growing in the last decades. The reason for this is related to the fact that the drug design is …

In recent times, there has been a growing interest in artificial neural networks, which are a
rough simulation of the information processing ability of the human brain, as modern and …

Causality has been a subject of study for a long time. Often causality is confused with
correlation. Human intuition has evolved such that it has learned to identify causality through …

Many articles in “in silico” drug design implemented genetic algorithm (GA) for feature
selection, model optimization, conformational search, or docking studies. Some of these …

In this paper, we introduce a new method to analyse HIV using a combination of
autoencoder networks and genetic algorithms. The proposed method is tested on a set of …

The goal of a quantitative structure–activity relationship (QSAR) model is to encode the
relationship between molecular structure and biological activity or physical property. Based …

A linear and non-linear quantitative structure–activity relationship (QSAR) study is presented
for modeling and predicting heparanase inhibitors' activity. A data set that consisted of 92 …

This paper proposes a new method, Adaptive Neuro-Fuzzy Inference System (ANFIS) to
evaluate physicochemical descriptors of certain chemical compounds for their appropriate …

Choosing a set of molecular descriptors (features) that is most relevant to a given biological
response variable is a very important problem in QSAR that has not be solved in an optimal …

Artificial neural networks (ANNs) have been widely used for medicinal chemistry modeling.
In the last two decades, too many reports used MATLAB environment as an adequate …