Target-aware Molecular Graph Generation
- URL: http://arxiv.org/abs/2202.04829v1
- Date: Thu, 10 Feb 2022 04:31:14 GMT
- Title: Target-aware Molecular Graph Generation
- Authors: Cheng Tan, Zhangyang Gao, Stan Z. Li
- Abstract summary: We propose SiamFlow, which forces the flow to fit the distribution of target sequence embeddings in latent space.
Specifically, we employ an alignment loss and a uniform loss to bring target sequence embeddings and drug graph embeddings into agreements.
Experiments quantitatively show that our proposed method learns meaningful representations in the latent space toward the target-aware molecular graph generation.
- Score: 37.937378787812264
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generating molecules with desired biological activities has attracted growing
attention in drug discovery. Previous molecular generation models are designed
as chemocentric methods that hardly consider the drug-target interaction,
limiting their practical applications. In this paper, we aim to generate
molecular drugs in a target-aware manner that bridges biological activity and
molecular design. To solve this problem, we compile a benchmark dataset from
several publicly available datasets and build baselines in a unified framework.
Building on the recent advantages of flow-based molecular generation models, we
propose SiamFlow, which forces the flow to fit the distribution of target
sequence embeddings in latent space. Specifically, we employ an alignment loss
and a uniform loss to bring target sequence embeddings and drug graph
embeddings into agreements while avoiding collapse. Furthermore, we formulate
the alignment into a one-to-many problem by learning spaces of target sequence
embeddings. Experiments quantitatively show that our proposed method learns
meaningful representations in the latent space toward the target-aware
molecular graph generation and provides an alternative approach to bridge
biology and chemistry in drug discovery.
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