Target-aware Molecular Graph Generation

Cheng Tan; Zhangyang Gao; Stan Z. Li

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arXiv · 2202.04829

Target-aware Molecular Graph Generation

Cheng Tan·Zhangyang Gao·Stan Z. Li

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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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Cheng Tan, Zhangyang Gao, Stan Z. Li. 2022-10-21. Target-aware Molecular Graph Generation. https://arxiv.org/abs/2202.04829

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