Version 1
: Received: 9 May 2023 / Approved: 10 May 2023 / Online: 10 May 2023 (08:09:11 CEST)
Version 2
: Received: 28 June 2023 / Approved: 29 June 2023 / Online: 30 June 2023 (08:50:24 CEST)
Ohue, M.; Kojima, Y.; Kosugi, T. Generating Potential Protein-Protein Interaction Inhibitor Molecules Based on Physicochemical Properties. Molecules 2023, 28, 5652, doi:10.3390/molecules28155652.
Ohue, M.; Kojima, Y.; Kosugi, T. Generating Potential Protein-Protein Interaction Inhibitor Molecules Based on Physicochemical Properties. Molecules 2023, 28, 5652, doi:10.3390/molecules28155652.
Ohue, M.; Kojima, Y.; Kosugi, T. Generating Potential Protein-Protein Interaction Inhibitor Molecules Based on Physicochemical Properties. Molecules 2023, 28, 5652, doi:10.3390/molecules28155652.
Ohue, M.; Kojima, Y.; Kosugi, T. Generating Potential Protein-Protein Interaction Inhibitor Molecules Based on Physicochemical Properties. Molecules 2023, 28, 5652, doi:10.3390/molecules28155652.
Abstract
Protein-protein interactions (PPIs) are associated with various diseases; hence, they are important targets in drug discovery. However, the physicochemical empirical properties of PPI-targeted drugs are distinct from those of conventional small molecule oral pharmaceuticals, which adhere to the ”rule of five (RO5).” Therefore, developing PPI-targeted drugs using conventional methods, such as molecular generation models, is difficult. In this study, we propose a molecule generation model based on deep reinforcement learning, which is specialized for generating PPI inhibitor candidates. We successfully generated potential PPI inhibitor compounds by modifying the scoring functions of the existing small molecule generation model and constructed a virtual library of generated PPI inhibitor compounds.
Keywords
protein-protein interaction inhibitor; rule of five; rule of four; QEPPI; molecular generation; virtual chemical library
Subject
Chemistry and Materials Science, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
