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Drug Discovery: Overview

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

GPCR Homology Model Generation for Lead Optimization.

Christofer S Tautermann¹

¹Department for Medicinal Chemistry, Boehringer Ingelheim Pharma, GmbH & Co KG, Birkendorfer Straße 65, 88397, Biberach an der Riss, Germany. christofer.tautermann@boehringer-ingelheim.com.

Methods in Molecular Biology (Clifton, N.J.)

|December 1, 2017

Summary

This summary is machine-generated.

GPCR homology modeling, using new X-ray structures, aids drug design. A workflow guides accurate modeling and ligand placement for different receptor states, improving GPCR-ligand optimization.

Keywords:

Docking GPCR Homology models Lead optimization

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Area of Science:

Structural Biology
Computational Chemistry
Pharmacology

Background:

Recent advancements in solving G protein-coupled receptor (GPCR) X-ray structures have enabled high-accuracy homology modeling.
GPCR homology models are increasingly utilized in drug design for ligand optimization.
Understanding GPCR conformational states is crucial for accurate modeling.

Purpose of the Study:

To present a comprehensive workflow for GPCR homology modeling.
To guide the selection of appropriate templates based on sequence and activation state.
To address challenges in ligand placement and pose validation.

Main Methods:

Target sequence selection for GPCR homology modeling.
Template selection considering sequence homology and receptor activation state.
Ligand placement strategies accounting for novel binding site characteristics.
Pose validation techniques for GPCR-ligand complexes.

Main Results:

A validated workflow for constructing accurate GPCR homology models.
Methodology for selecting templates reflecting specific GPCR activation states.
Improved approaches for placing ligands in diverse and complex binding pockets.
Enhanced accuracy in predicting GPCR-ligand interactions.

Conclusions:

The presented workflow facilitates accurate GPCR homology modeling for drug discovery.
Consideration of activation states and unusual binding sites is key for successful modeling.
This approach enhances the utility of GPCR models in optimizing drug candidates.