Efficient Binding Affinity Estimation for Fragment-Based Compounds Using a Separated Topologies Approach.

Area of Science:

• Computational Chemistry
• Drug Discovery
• Biophysics

Background:

• Fragment-based drug discovery (FBDD) is crucial for early-stage drug development.
• Predicting binding affinities of fragments and analogs presents computational challenges due to weak interactions and structural diversity.
• Existing free-energy methods are often not optimized for FBDD's specific needs.

Purpose of the Study:

• To evaluate the Separated Topologies (SepTop) approach for modeling fragment-based transformations in drug discovery.
• To assess SepTop's accuracy in predicting binding affinities for fragment merging and linking.
• To determine SepTop's suitability for fragment optimization in early drug development.

Main Methods:

• Retrospective analysis using established FBDD datasets (Cyclophilin D, SARS-CoV-2 Macrodomain 1).
• Application of the Separated Topologies (SepTop) computational method.
• Validation of binding free-energy calculations for fragment and lead-like compounds.

Main Results:

• The SepTop approach accurately recovered experimental binding affinities.
• Good predictive accuracy was observed across both fragment and lead-like compounds.
• SepTop demonstrated effectiveness in modeling fragment merging and linking transformations.

Conclusions:

• SepTop is a suitable method for fragment optimization in FBDD.
• SepTop can extend the application of binding free-energy calculations to earlier drug discovery stages.
• This method enhances computational strategies for developing novel therapeutics.