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

Fragment-based drug
discovery (FBDD) is a widely used strategy
in early-stage drug development, but accurately predicting the binding
affinities of fragments and their elaborated analogs poses unique
computational challenges. These difficulties arise from weak binding
affinities, diverse chemical scaffolds, and limited structural overlap
between fragments and their optimized derivatives. While several free-energy
methods exist, few are tailored to the specific requirements of FBDD.
In this study, we evaluate the Separated Topologies (SepTop) approach
for modeling fragment-based transformations, including fragment merging
and linking. Using retrospective data sets from Cyclophilin D and
SARS-CoV-2 Macrodomain 1, we demonstrate that SepTop can recover experimental
binding affinities with good accuracy across both fragment and lead-like
compounds. These results support SepTop’s suitability for fragment
optimization and highlight its potential to extend the reach of binding
free-energy calculations into earlier stages of drug discovery.