The recent COVID-19 pandemic has highlighted the need for rapid therapeutic development for infectious diseases. To accelerate this process, we present a deep learning based generative modeling framework, CogMol, to design drug candidates specific to a given target protein sequence with high off-target selectivity. 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. Docking to the target proteins demonstrate the potential of these generated molecules as ligands. Structural similarity analyses further imply novelty of the generated molecules with respect to the training dataset as well as possible biological association of a number of generated molecules that might be of relevance to COVID-19 therapeutic design. While the validation of these molecules is underway, we release ~ 3000 novel COVID-19 drug candidates generated using our framework . URL : URL
The novel nature of SARS-CoV-2 calls for the development of efficient de novo drug design approaches. In this study, we propose an end-to-end framework, named CogMol (Controlled Generation of Molecules), for designing new drug-like small molecules targeting novel viral proteins with high affinity and off-target selectivity. 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 target proteins: main protease, receptor-binding domain of the spike protein, and non-structural protein 9 replicase. The generated candidates are novel at both molecular and chemical scaffold levels when compared to the training data. CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations. Docking reveals favorable binding of generated molecules to the target protein structure, where 87-95 \% of high affinity molecules showed docking free energy < -6 kcal/mol. When compared to approved drugs, the majority of designed compounds show low parent molecule and metabolite toxicity and high synthetic feasibility. In summary, CogMol handles multi-constraint design of synthesizable, low-toxic, drug-like molecules with high target specificity and selectivity, and does not need target-dependent fine-tuning of the framework or target structure information.
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recent COVID-19 pandemic has highlighted the need for rapid therapeutic development for infectious diseases. To accelerate this process, we present a deep learning based generative modeling framework, CogMol, to design drug candidates specific to a given target protein sequence with high
novel nature of SARS-CoV-2 calls for the development of efficient de novo drug design approaches. In this study, we propose an end-to-end framework, named CogMol (Controlled Generation of Molecules), for designing new drug-like small molecules targeting novel viral proteins with high affinity and
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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
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
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, the virus responsible for COVID-19, namely non-structural protein 9 (NSP9) replicase,
target proteins:
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and the
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(RBD) of the S protein. Docking to the target proteins demonstrate the potential of these generated molecules as ligands. Structural similarity analyses further imply novelty of the generated molecules with respect to the training dataset as well as possible biological association of a number of generated molecules that might be of relevance to COVID-19 therapeutic design. While the validation of these molecules is underway, we release ~ 3000 novel COVID-19 drug candidates generated using our framework . URL : URL
of the spike protein, and non-structural protein 9 replicase. The generated candidates are novel at both molecular and chemical scaffold levels when compared to the training data. CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations. Docking reveals favorable binding of generated molecules to the target protein structure, where 87-95 \% of high affinity molecules showed docking free energy < -6 kcal/mol. When compared to approved drugs, the majority of designed compounds show low parent molecule and metabolite toxicity and high synthetic feasibility. In summary, CogMol handles multi-constraint design of synthesizable, low-toxic, drug-like molecules with high target specificity and selectivity, and does not need target-dependent fine-tuning of the framework or target structure information.