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The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and
Briefings in Bioinformatics
|February 14, 2016
Summary
Proteochemometric (PCM) modeling predicts interactions between multiple molecules and targets. Advances in target and cross-term descriptors have significantly expanded its applications beyond protein-ligand relationships.
Area of Science:
- Computational chemistry
- Cheminformatics
- Bioinformatics
Background:
- Quantitative structure-activity relationship (QSAR) models traditionally focus on single ligand-target pairs.
- Proteochemometric (PCM) modeling extends this by analyzing relationships across multiple ligands and targets.
- PCM modeling relies on descriptors, bioactivity data, and learning functions.
Purpose of the Study:
- To review the advancements in target and cross-term descriptors in PCM modeling.
- To summarize the expanded application scope of PCM modeling.
- To explore future directions for PCM modeling.
Main Methods:
- Review of literature on PCM modeling techniques.
- Analysis of the impact of novel descriptors (target and cross-term).
- Examination of machine learning advancements in PCM.
Main Results:
- Novel target and cross-term descriptors have substantially improved PCM model performance.
- PCM modeling applications have broadened to include protein-peptide, protein-DNA, and protein-protein interactions.
- The increasing availability of bioactivity data fuels PCM development.
Conclusions:
- PCM modeling is a rapidly evolving field with significant potential.
- Further development of descriptors and machine learning will expand PCM applications.
- Future applications may include Target-Catalyst-Ligand systems.
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