FragVScreen

FragVScreen is developed to combine small molecule virtual screening with data mining and machine learning algorithms. Given the binding pocket for a target of interest, the method will search for similar pockets in a database of native protein-ligand complexes. The search is based on 3D-geometric and chemical similarity of the atoms forming the binding pocket. For each similar pocket identified, the ligand’s sub-structures (fragments) corresponding to that pocket are extracted, thus, forming a virtual library of fragments. The fragments are used along with machine learning algorithms and specialized virtual screening platform in order to find larger molecules that contain these fragments and are still predicted to bind. The method relies on efficient algorithms to facilitate the pocket similarity search and virtual screening processes, and it can be used for structure-based drug design tools such as fragment-based lead discovery.