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 : http://ibm.biz/covid19-mol

该研究提出了一种名为 CogMol 的自动生成新药的框架，它结合了SMILES VAE嵌入的蛋白-分子结合亲和力预测、分子控制生成方案等技术，可以高效地生成具有高亲和力和低毒性的新药，不需要针对目标进行微调和目标结构信息。

CogMol:使用深度生成模型进行针对性和选择性的COVID-19药物设计