Abstract
Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously.
Highlights
Proteochemometrics (PCM) modelling describes methods where a computational description from the ligand side of the system is combined with a description of the biological side being studied and both are related to a particular readout of interest.[15,16]
PCM relates to personalized medicine as it can predict the effect of a ligand on a complex biological system, e.g. cell line, from genotypic information.[17]
In addition to traditional therapeutic targets, which continue to Review be well represented in recent PCM studies, other applications and techniques are gaining ground steadily, namely: (i) the modelling of the selectivity of viral protein mutants, mainly HIV; (ii) the inclusion of bioactivity information from mammal orthologues; (iii) the usage of 3-dimensional target information; and (iv) toxicogenomics and pharmacogenomics
Summary
The term chemogenomics comprises techniques capable to capitalize on this huge amount of bioactivity data by considering compound and target information, in order to nd unknown interactions between (new) compounds and their (new) targets.[13,14] Proteochemometrics (PCM) modelling describes methods where a computational description from the ligand side of the system is combined with a description of the biological side being studied and both are related to a particular readout of interest.[15,16]. In this context, ligands are typically small molecules biologics have been explored. PCM relates to personalized medicine as it can predict the effect of a ligand on a complex biological system, e.g. cell line, from genotypic information.[17]
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