In silico fragment-mapping method: a new tool for fragment-based/structure-based drug discovery

Area of Science:

• Computational Chemistry
• Drug Discovery
• Structural Biology

Background:

• Fragment-based drug discovery (FBDD) and structure-based drug discovery (SBDD) are crucial for identifying novel therapeutics.
• Existing methods often require extensive experimental data or complex computational resources.
• There is a need for efficient in silico tools to support FBDD/SBDD workflows.

Purpose of the Study:

• To propose and validate a novel in silico fragment-mapping method for FBDD/SBDD.
• To introduce the Canonical Subsite-Fragment DataBase (CSFDB) and the Fsubsite program.
• To demonstrate the method's utility in identifying potential drug candidates and guiding fragment growing.

Main Methods:

• Development of the Canonical Subsite-Fragment DataBase (CSFDB) containing subsite-fragment pairs from protein-ligand complexes.
• Creation of the Fsubsite program for knowledge-based fragment mapping using 3D similarity matching.
• Application of the method to cyclin-dependent kinase 2 (CDK2), tRNA-guanine transglycosylase, and heat shock protein 90-α (HSP90α) targets.

Main Results:

• Successful identification of known CDK2 inhibitors through fragment mapping.
• Demonstration of fragment-mapping utility for fragment growing around a ligand in tRNA-guanine transglycosylase.
• Generation of a 3D-pharmacophore model for HSP90α, leading to the identification of similar known ligands via virtual screening.

Conclusions:

• The proposed in silico fragment-mapping method is a valuable computational tool for FBDD and SBDD.
• CSFDB and Fsubsite provide an efficient approach for identifying and growing fragments in drug discovery.
• The method shows promise for accelerating the identification of novel therapeutic agents.