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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