3D-Pharma, A Ligand-based Virtual Screening tool using 3D Pharmacophore Fingerprints

Abstract

In this work, we introduced 3D-Pharma, a new Ligand-Based Virtual Screening method that uses fingerprints of pharmacophore triplets at atomic resolutions to build very simple and predictive models. Within 3D-Pharma the molecules are described by multiple representations that comprehend several prototropic species and conformations (multiple species, multiple mode approach). All the multiple representations of a compound are concatenated into a unique fingerprint that accounts for most of its chemical and conformational diversity. The biological activity of an ensemble of active molecules are represented by a single modal fingerprint or model, validated through a new exhaustive 10-fold cross-validation scheme, which improves robustness and internal consistency of the models, as well as its predictive power. We benchmark our method with 10 datasets of active compounds and decoys gathered from DUD database and compare its performance against seven state-of-the-art LBVS methods. To generate the models, we used three external and independent datasets of bioactive compounds (Drugs, PDB Ligands and WOMBAT). We concluded that 3D-Pharma overperforms all other state-of-the-art LBVS tools analyzed, in terms of global accuracy as well as scaffold hopping and early recovery capacities. Furthermore, the models produced by 3D-Pharma are simple, robust, consistent and predictive.