Drug screening with the Autodock Vina on a set of kinases without experimentally established structures

Abstract

Virtual drug screening is one of the most widely used approaches for finding new drugs candidates. The process consists in selecting one or more chemical compounds with the highest binding free energy to target proteins. Given that the empirical space of chemical compounds is extremely large and estimated to has over 50 millions of them, finding the most effective drug becomes computationally challenging. Furthermore, the vast majority of proteins still lack the experimentally obtained 3D structures required for most of the molecular computer tools available, making it impossible to calculate their binding free energies with chemical compounds. In view of this, the aim of our study is to asses the effectiveness of the Autodock Vina tool in a large environments with unstructured proteins, those without defined 3D structure. The ultimate goal is to enable a fast and efficient virtual drug screening in such an environments, and to apply it for discovery of a new drug candidates.

Novel prediction methods for virtual drug screening

“…Another example of research that is utilizing biological networks for DTI is an in silico model of cancer called VINI. Tomić et al [54] have developed a virtual screening framework which transforms the metabolic pathways of cancer from Kyoto Encyclopedia of Genes and Genomes [51] into the binding energy matrices. VINI is able to search libraries containing 3D structures and FASTA drug sequences of small molecules and protein-based drugs, to identify those with the highest binding energy to target molecules.…”