Before revision:  We augment this generative framework with an in silico screening process that accounts for toxicity, to lower the failure rate of the generated drug candidates in later stages of the drug development pipeline. We apply this framework to three relevant proteins of the SARS-CoV-2 , the virus responsible for COVID-19, namely non-structural protein 9 (NSP9) replicase, main protease, and the receptor-binding domain (RBD) of the S protein.
After revision:  CogMol combines adaptive pre-training of a molecular SMILES Variational Autoencoder (VAE) and an efficient multi-attribute controlled sampling scheme that uses guidance from attribute predictors trained on latent features. To generate novel and optimal drug-like molecules for unseen viral targets, CogMol leverages a protein-molecule binding affinity predictor that is trained using SMILES VAE embeddings and protein sequence embeddings learned unsupervised from a large corpus. CogMol framework is applied to three SARS-CoV-2 , the virus responsible for COVID-19, namely non-structural protein 9 (NSP9) replicase, main protease, and the receptor-binding domain (RBD) of the S protein.