Inhibitor_Mol_VAE: a variational autoencoder approach for generating corrosion inhibitor molecules

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.