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Abstract
Deep learning-based generative modeling demonstrates proven advantages as an effective approach in molecular discovery. This study introduces a generative-network based method called Inhibitor_Mol_VAE, which uses a variational autoencoder model to generate corrosion inhibitor molecules with targeted inhibition efficiency. We first evaluate the model’s ability to reconstruct molecules. Then, we assess the model’s ability to generate new inhibitor molecules using physiochemical properties (including MolWt, LogP, Vdw_volume, and Electronegativity). New molecules with high inhibition efficiencies at low concentrations, such as [ethoxy(methoxy)phosphoryl]-phenylmethanol and (alpha-methylamino-benzyl)-phosphonsaeure-monoaethylester are successfully discovered.
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Details
1 University of Science and Technology Beijing, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, Beijing, China (GRID:grid.69775.3a) (ISNI:0000 0004 0369 0705); University of Science and Technology Beijing, Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing, China (GRID:grid.69775.3a) (ISNI:0000 0004 0369 0705); University of Science and Technology Beijing, Shunde Innovation School, Foshan, China (GRID:grid.69775.3a) (ISNI:0000 0004 0369 0705)
2 University of Science and Technology Beijing, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, Beijing, China (GRID:grid.69775.3a) (ISNI:0000 0004 0369 0705); Liaoning Academy of Materials, Institute of Materials Intelligent Technology, Shenyang, China (GRID:grid.69775.3a)
3 University of Science and Technology Beijing, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, Beijing, China (GRID:grid.69775.3a) (ISNI:0000 0004 0369 0705); University of Science and Technology Beijing, Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing, China (GRID:grid.69775.3a) (ISNI:0000 0004 0369 0705); University of Science and Technology Beijing, Shunde Innovation School, Foshan, China (GRID:grid.69775.3a) (ISNI:0000 0004 0369 0705); Liaoning Academy of Materials, Institute of Materials Intelligent Technology, Shenyang, China (GRID:grid.69775.3a)