2 research outputs found
Bridging the Gap between Target-Based and Cell-Based Drug Discovery with a Graph Generative Multitask Model
The
development of new drugs is crucial for protecting humans from
disease. In the past several decades, target-based screening has been
one of the most popular methods for developing new drugs. This method
efficiently screens potential inhibitors of a target protein in vitro,
but it frequently fails in vivo due to insufficient activity of the
selected drugs. There is a need for accurate computational methods
to bridge this gap. Here, we present a novel graph multi-task deep learning model to identify compounds with both target inhibitory and cell active
(MATIC) properties. On a carefully curated SARS-CoV-2 data set, the
proposed MATIC model shows advantages compared with the traditional
method in screening effective compounds in vivo. Following this, we
investigated the interpretability of the model and discovered that
the learned features for target inhibition (in vitro) or cell active
(in vivo) tasks are different with molecular property correlations
and atom functional attention. Based on these findings, we utilized
a Monte Carlo-based reinforcement learning generative model to generate
novel multiproperty compounds with both in vitro and in vivo efficacy,
thus bridging the gap between target-based and cell-based drug discovery.
The tool is freely accessible at https://github.com/SIAT-code/MATIC
Bridging the Gap between Target-Based and Cell-Based Drug Discovery with a Graph Generative Multitask Model
The
development of new drugs is crucial for protecting humans from
disease. In the past several decades, target-based screening has been
one of the most popular methods for developing new drugs. This method
efficiently screens potential inhibitors of a target protein in vitro,
but it frequently fails in vivo due to insufficient activity of the
selected drugs. There is a need for accurate computational methods
to bridge this gap. Here, we present a novel graph multi-task deep learning model to identify compounds with both target inhibitory and cell active
(MATIC) properties. On a carefully curated SARS-CoV-2 data set, the
proposed MATIC model shows advantages compared with the traditional
method in screening effective compounds in vivo. Following this, we
investigated the interpretability of the model and discovered that
the learned features for target inhibition (in vitro) or cell active
(in vivo) tasks are different with molecular property correlations
and atom functional attention. Based on these findings, we utilized
a Monte Carlo-based reinforcement learning generative model to generate
novel multiproperty compounds with both in vitro and in vivo efficacy,
thus bridging the gap between target-based and cell-based drug discovery.
The tool is freely accessible at https://github.com/SIAT-code/MATIC