A newly developed method based on AI-oriented amino acid interaction mapping (AI-AAM) for efficient virtual scaffold hopping

Abstract

When we acquired a seed or lead compound in drug discovery, the candidate often dropped out during in vivo/vitro testing. We needed to have multiple scaffolds to increase the rate of success of drug development. For scaffold hopping, we needed a descriptor that could represent the binding affinities of a ligand to its target protein. In order to find the descriptor, we assumed that protein-ligand binding could be described as the set of interactions between a ligand and amino acids. By computing the interaction energy between a ligand and amino acids, we created a new descriptor, Amino Acid Mapping (AAM). Based on AAM, we developed the machine learning system (AI-AAM) both for searching and creating seed or lead compounds from a biologically active template compound. We were now ready to obtain multiple candidate compounds in drug discovery.