The RosettaVS platform designed in this study fully utilizes the advantages of both physics-based potentials and machine learning approaches. A physics-based force field treats protein-ligand interactions more similarly to reality while the screening is done using machine learning to minimize the screening time through pattern recognition of biological data. The platform also takes into account receptor flexibility, – this aspect is very important in many drug targets considered for protein-drug design.
The construction of the RosettaVS platform has contributed to improved performance in several aspects of drug development processes. It can quickly search and find potential drug candidates in massive libraries by uniting two techniques of RosettaVS: computational force fields and neural network-based learning. The drug candidates identified through the platform are expected to be developed in a shorter time than standard manual drug discovery processes would take.
RosettaVS is within Rosetta software and is available freely for non-commercial users on GitHub