A Comprehensive Review on Homology Modelling in Drug Development: Methodologies, Tools, and Evaluation

Area of Science:

• Bioinformatics
• Computational Genomics
• Structural Biology

Background:

• Genomic and proteomic data necessitate accurate protein structure prediction for structure-based drug design.
• Homology modeling offers a fast, cost-effective, and consistent approach to predicting three-dimensional protein structures.

Purpose of the Study:

• To provide an in-depth and up-to-date review of homology modeling techniques and tools.
• To explore the integration of artificial intelligence and machine learning in enhancing protein structure prediction.
• To discuss the application of homology modeling in drug discovery and lead optimization.

Main Methods:

• Review of key steps: template discovery, sequence alignment, model building, loop refinement, side chain modeling, and structure validation.
• Examination of widely used tools: BLAST, MODELLER, Swiss-Model, SCWRL, PROCHECK, ProSA, and VERIFY3D.
• Analysis of current challenges and focus areas: loop modeling, template selection, and validation metrics (RMSD, Z-score, Ramachandran plot).

Main Results:

• Homology modeling integrates bioinformatics, computational genomics, and structural biology for efficient drug development.
• AI and machine learning show potential to improve precision in homology modeling, especially with limited homology.
• The review provides guidance for accelerating drug development for proteins challenging to crystallize.

Conclusions:

• Homology modeling is a valuable tool for drug scientists, bridging traditional methods with modern computational technology.
• The review highlights the importance of homology modeling in overcoming challenges in protein structure determination.
• Effective application of homology modeling can significantly speed up the drug development pipeline.