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

The large accumulation of genomic and proteomic data has clearly established the need for accurate prediction of protein structure, especially with a view to enabling structurebased drug design. There are numerous ways to do this on computers these days, but one that distinctly stands out is homology modeling since it is fast, cheap, and consistent. It forecasts three-dimensional protein structures with unrecognized geometries by examining how they have evolved to date. This article provides an in-depth and up-to-date review of homology modeling. It addresses important steps, like discovering templates, sequence alignment, model building, loop refinement, side chain modeling, and structure validation. We have examined in detail the widely used tools, such as BLAST, MODELLER, Swiss-Model, SCWRL, PROCHECK, ProSA, and VERIFY3D. Also, it has been covered how artificial intelligence and machine learning can enhance these processes to be more precise, where little homology exists. Currently, a lot of focus is being placed on loop modeling problems, choosing a template, and structural validation procedures, like root mean square deviation (RMSD), Z-score, and Ramachandran plot statistics. In addition, we have examined how homology modeling is used in ligand docking and in lead optimization at the system level. The review offers great advice on how to speed up the drug-development process, especially for proteins that prove hard to crystallize or solve by X-ray crystallography. It does so by integrating concepts from bioinformatics, computational genomics, and structural biology into its design. We herein bridge traditional techniques and current computer technology to offer drug scientists a useful tool.