Automatic and efficient decomposition of two-dimensional structures of small molecules for fragment-based...

Area of Science:

• Computational Chemistry
• Cheminformatics
• Drug Discovery

Background:

• Fragment-based drug discovery (FBDD) relies on analyzing and utilizing molecular fragments.
• Efficient computational tools are needed to process large chemical libraries for FBDD.

Purpose of the Study:

• To evaluate the DAIM (Decomposition and Identification of Molecules) program for fragment selection in docking and library analysis.
• To assess DAIM's effectiveness in identifying key molecular fragments for drug design.

Main Methods:

• DAIM was used to decompose 130 ligands from known protein-ligand complexes.
• A fingerprint-based identification technique was employed to select anchor fragments for docking.
• DAIM was applied to analyze a library of 1.85 million commercially available compounds.

Main Results:

• DAIM-selected fragments yielded superior docking results compared to size-based or random selection.
• The most frequent fragments identified by DAIM in a large library significantly overlapped with fragments found in known drugs.
• DAIM facilitated successful in silico screening for beta-secretase and EphB4 kinase inhibitors.

Conclusions:

• DAIM is a valuable tool for fragment-based docking and large-scale database analysis in drug discovery.
• The program effectively identifies relevant fragments for drug design and screening.
• DAIM shows promise for future applications in de novo ligand design.