PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding

• URL: http://arxiv.org/abs/2302.07120v1
• Date: Tue, 14 Feb 2023 15:27:47 GMT
• Title: PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding
• Authors: Zhangyang Gao, Yuqi Hu, Cheng Tan, Stan Z. Li
• Abstract summary: We develop a novel generative model that considers both the targeted pocket's circumstances and a variety of chemical properties. Experiments show that our model exhibits good controllability in both single and multi-conditional molecular generation.
• Score: 34.27649279751879
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Is there a unified model for generating molecules considering different conditions, such as binding pockets and chemical properties? Although target-aware generative models have made significant advances in drug design, they do not consider chemistry conditions and cannot guarantee the desired chemical properties. Unfortunately, merging the target-aware and chemical-aware models into a unified model to meet customized requirements may lead to the problem of negative transfer. Inspired by the success of multi-task learning in the NLP area, we use prefix embeddings to provide a novel generative model that considers both the targeted pocket's circumstances and a variety of chemical properties. All conditional information is represented as learnable features, which the generative model subsequently employs as a contextual prompt. Experiments show that our model exhibits good controllability in both single and multi-conditional molecular generation. The controllability enables us to outperform previous structure-based drug design methods. More interestingly, we open up the attention mechanism and reveal coupling relationships between conditions, providing guidance for multi-conditional molecule generation.

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