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. 2023 Jul 26;28(15):5652.
doi: 10.3390/molecules28155652.

Generating Potential Protein-Protein Interaction Inhibitor Molecules Based on Physicochemical Properties

Masahito Ohue  1 , Yuki Kojima  1 , Takatsugu Kosugi  1
Affiliations

Generating Potential Protein-Protein Interaction Inhibitor Molecules Based on Physicochemical Properties

Masahito Ohue et al. Molecules. .

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 challenging. In this study, we propose a molecular generation model based on deep reinforcement learning that is specialized for the production of PPI inhibitors. By introducing a scoring function that can represent the properties of PPI inhibitors, we successfully generated potential PPI inhibitor compounds. These newly constructed virtual compounds possess the desired properties for PPI inhibitors, and they show similarity to commercially available PPI libraries. The virtual compounds are freely available as a virtual library.

Keywords: QEPPI; molecular generation; protein-protein interaction inhibitor; rule of five; rule of four; virtual chemical library.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Distribution of QED scores of the compounds generated between the REINVENT steps 2901 and 3000.
Figure 2
Figure 2
Distribution of RO4 equivalent scores of the compounds generated between the REINVENT steps 2901 and 3000.
Figure 3
Figure 3
Distribution of QEPPI scores of the compounds generated between the REINVENT steps 2901 and 3000.
Figure 4
Figure 4
Distribution of the generated compounds: (a) distribution of molecular weight, (b) distribution of lipophilicity (LogP).
Figure 5
Figure 5
Molecular weight–LogP scatter plots of the RO4- and QEPPI-generated compounds.
Figure 6
Figure 6
Examples of compounds that have been generated and included in the virtual library.
Figure 7
Figure 7
Distribution of scores (number of steps: 100) for the compounds generated by the QEPPI and obtained from the Enamine PPI library: (a) QED and (b) QEPPI scores.
Figure 8
Figure 8
Molecular weight–LogP scatterplots of the compounds generated by the QEPPI and obtained from the Enamine PPI library.
See this image and copyright information in PMC

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