  Drug discovery is vitally important for protecting human against disease.
Target-based screening is one of the most popular methods to develop new drugs
in the past several decades. This method efficiently screens candidate drugs
inhibiting target protein in vitro, but it often fails due to inadequate
activity of the selected drugs in vivo. Accurate computational methods are
needed to bridge this gap. Here, we propose a novel graph multi task deep
learning model to identify compounds carrying both target inhibitory and cell
active (MATIC) properties. On a carefully curated SARS-CoV-2 dataset, the
proposed MATIC model shows advantages comparing with traditional method in
screening effective compounds in vivo. Next, we explored the model
interpretability and found that the learned features for target inhibition (in
vitro) or cell active (in vivo) tasks are different with molecular property
correlations and atom functional attentions. Based on these findings, we
utilized a monte carlo based reinforcement learning generative model to
generate novel multi-property compounds with both in vitro and in vivo
efficacy, thus bridging the gap between target-based and cell-based drug
discovery.
