In silico fragment-based drug design using a PASS approach

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Drug discovery

Background:

• Fragment-based drug design (FBDD) is crucial for novel ligand development.
• Experimental methods (X-ray, NMR, SPR) and molecular modeling (docking) have limitations in fragment library preparation.
• Quantitative Structure-Activity Relationship (QSAR) studies require large, homogeneous datasets.

Purpose of the Study:

• To propose a ligand-based approach for selecting fragments with a positive contribution to biological activity.
• To leverage the Prediction of Activity Spectra for Substances (PASS) algorithm for fragment selection.
• To develop a method for creating fragment libraries applicable to multiple biological activities.

Main Methods:

• Utilized the PASS algorithm, known for its robustness with heterogeneous datasets.
• Applied PASS to predict biological activity spectra for fragment selection.
• Validated the fragment selection algorithm using intermolecular interaction data from Protein Data Bank (PDB) enzyme inhibitors.

Main Results:

• The PASS algorithm facilitates the preparation of fragment libraries meeting multiple criteria.
• Validation demonstrated that calculated intermolecular interaction fractions correlate with predicted fragment contributions to enzyme inhibition.
• The method identified fragments with statistically significant positive and negative contributions to enzyme inhibition.

Conclusions:

• The proposed ligand-based approach offers an effective strategy for fragment selection in drug design.
• PASS algorithm provides a robust foundation for building versatile fragment libraries.
• This method enhances the efficiency and applicability of fragment-based drug design.