The integration of early ADMET (absorption, distribution, metabolism, excretion and toxicity)
profiling, or simply prediction, of'lead'molecules to speed-up the'lead'selection further for …

Predicting protein subcellular localization is an important and difficult problem, particularly
when query proteins may have the multiplex character, ie, simultaneously exist at, or move …

Quantitative Structure-Activity Relationship (QSAR) models have been used in
Pharmaceutical design and Medicinal Chemistry for the discovery of anti-parasite drugs …

Introduction of graphic representation for biological sequences can provide intuitive overall
pictures as well as useful insights for performing large-scale analysis. Here, a new two …

Quantitative structure–activity relationship (QSAR), a regression modeling methodology that
establishes statistical correlation between structure feature and apparent behavior for a …

Determining the target proteins of new anticancer compounds is a very important task in
Medicinal Chemistry. In this sense, chemists carry out preclinical assays with a high number …

In this work, we developed a general perturbation theory and machine learning method for
data mining of proteomes to discover new B-cell epitopes useful for vaccine design. The …

Many drugs with very different affinity to a large number of receptors are described. Thus, in
this work, we selected drug− target pairs (DTPs/nDTPs) of drugs with high affinity/nonaffinity …

Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and
pharmaceutical chemistry need and consider it a huge goal to define target proteins of new …

Predicting drug–protein interactions (DPIs) for target proteins involved in dopamine
pathways is a very important goal in medicinal chemistry. We can tackle this problem using …