Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set. File list (3) download file view on ChemRxiv manuscript.pdf (4.23 MiB) download file view on ChemRxiv supplementary.pdf (0.92 MiB) download file view on ChemRxiv tables.zip (5.99 KiB)

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

Identifying local similarities in ligand binding sites from distant proteins is still a major hurdle to rational drug design approaches. We herewith present a novel method, borrowed from computer vision, particularly adapted to mine fragment subpockets and compare them to entire ligand-binding sites.Pockets are represented by pharmacophore-annotated point clouds mimicking ideal ligands or fragments. Point cloud registration is used to find the transformation enabling an optimal overlap of points sharing similar topological neighborhoods and pharmacophoric features. Importantly, the local environment of randomly sampled cavity points is used to generate a preliminary alignment that is next refined by an iterative closest point algorithm. A scoring function has been tuned to quantify the degree of shape and pharmacophoric overlap, and assess pocket pairwise similarity. The method (ProCare) was first calibrated on a large set of known similar and dissimilar druggable cavities, shown to be insensitive to moderate variations in atomic coordinates, and then applied to the specific problem of comparing fragment subpockets with entire cavities. Starting from a unique set of cavity points, point cloud registration outperformed a state-of-the-art computational method (shape-based similarity search) in detecting local similarities between fragment subsites and entire cavities. A collection of 33,953 subpockets annotated with their bound fragments was screened for local similarity to cavities from three recently described protein X-ray structures. ProCare was able to detect local similarities between remote pockets and transfer the corresponding fragments to the query cavity space. Interestingly, fragments selected from totally unrelated proteins, nicely overlap substructures of the masked original ligand co-crystallized with the target query, thereby proposing an automated first step to an automated fragment-based design approach targeting ligand-orphan cavities.

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>