The primary goal of rational drug discovery is the identification of selective ligands which act
on single or multiple drug targets to achieve the desired clinical outcome through the …

Proteochemometric (PCM) modelling is a computational method to model the bioactivity of
multiple ligands against multiple related protein targets simultaneously. Hence it has been …

Background Molecular descriptors and fingerprints have been routinely used in QSAR/SAR
analysis, virtual drug screening, compound search/ranking, drug ADME/T prediction and …

Proteochemometrics is a machine learning based modeling approach relying on a
combination of ligand and protein descriptors. With ongoing developments in machine …

Extensive kinase profiling data, covering more than half of the human kinome, are available
nowadays and allow the construction of activity prediction models of high practical utility …

As an extension of the conventional quantitative structure activity relationship models,
proteochemometric (PCM) modelling is a computational method that can predict the …

Serine proteases, implicated in important physiological functions, have a high intra-family
similarity, which leads to unwanted off-target effects of inhibitors with insufficient selectivity …

Nuclear receptors (NRs) are a class of essential proteins that regulate the expression of
specific genes and are associated with multiple diseases. In silico methods for prescreening …

Proteochemometrics (PCM) is an approach for bioactivity predictive modeling which models
the relationship between protein and chemical information. Gaussian Processes (GP) …

The bromodomain-containing proteins are a ligandable family of epigenetic readers, which
play important roles in oncological, cardiovascular, and inflammatory diseases. Achieving …