A GPU Accelerated Fragment-based De Novo Ligand Design by a Bayesian Optimization Algorithm

Abstract

De Novo ligand design is an automatic fragment-based design of molecules within a protein binding site of a known structure. A Bayesian Optimization Algorithm (BOA), a meta-heuristic algorithm, is introduced to join predocked fragments with a user-supplied list of fragments. A novel feature proposed is the simultaneous optimization of force field energy and a term enforcing 3D-overlap to known binding mode(s). The performance of the algorithm is tested on Liver X receptors (LXRs) using a library of about 14, 000 fragments and the binding mode of a known heterocyclic phenyl acetic acid to bias the design. We further introduce the use of GPU (Graphics Processing Unit) to overcome the excessive time required in evaluating each possible fragment combination. We show how the GPU utilization enables experimenting larger fragment sets and target receptors for more complex instances. The results show how the nVidia's Tesla C2050 GPU was utilized to enable the generation of complex agonists effectively. In fact, eight of the 1, 809 molecules designed for LXRs are found in the ZINC database of commercially available compounds.