In Silico Drug-Target Profiling.

Area of Science:

• Pharmacological Science
• Computational Chemistry
• Drug Discovery

Background:

• Drug discovery relies on understanding chemical-target interactions and their link to disease phenotypes.
• Advancements in chemical proteomics and structural biology have generated extensive data for target profiling.
• The target-based approach has been central to drug discovery, generating valuable in silico data.

Purpose of the Study:

• To review various in silico target profiling techniques.
• To highlight applications of these techniques in drug discovery, such as predicting off-target effects and prioritizing compounds.
• To discuss methods for inferring drug-target interactions and facilitating drug repurposing.

Main Methods:

• Ligand-based chemical similarity searches and machine learning approaches.
• Structure-based 3D techniques using descriptors and energy scoring for binding affinity prediction.
• Novel methods employing compound set metrics to compare query compounds with target-associated compound libraries.

Main Results:

• In silico target profiling enables prediction of chemical-target interactions based on similarity or biophysical principles.
• Different computational methods, including ligand-based, structure-based, and set-metric approaches, are available.
• These techniques are crucial for identifying unwanted targets, repurposing existing drugs, and prioritizing compounds from screening.

Conclusions:

• In silico target profiling is a powerful tool in modern pharmacology and drug discovery.
• The reviewed computational methods offer diverse strategies for understanding chemical-target interactions.
• These approaches significantly contribute to advancing drug discovery pipelines and therapeutic strategies.