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Advances and Challenges in Computational Target Prediction.

Dominique Sydow¹, Lindsey Burggraaff², Angelika Szengel¹

¹In silico Toxicology, Institute of Physiology , Charité - Universitätsmedizin Berlin , Charitéplatz 1 , 10117 Berlin , Germany.

Journal of Chemical Information and Modeling

|March 1, 2019

Summary

This summary is machine-generated.

Computational target prediction aids preclinical drug development by identifying potential drug targets. This review explores ligand-based, target-based, and hybrid methods for predicting molecular targets and discusses future research directions.

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Area of Science:

Pharmacology and Cheminformatics
Computational Drug Discovery

Background:

Target deconvolution is crucial for preclinical drug development, guiding research strategy.
Computational target prediction identifies likely targets for orphan ligands or similar proteins.

Purpose of the Study:

To review existing computational approaches for target prediction.
To highlight limitations and future directions in the field.

Main Methods:

Review of published literature on computational target prediction.
Categorization of methods into ligand-based, target-based, and hybrid approaches.

Main Results:

Summary of diverse computational target prediction strategies.
Identification of applications including mode-of-action, polypharmacology, and drug repositioning.

Conclusions:

Computational target prediction is essential for efficient drug discovery.
Further development is needed to overcome current limitations and advance the field.