Molecule Design by Latent Prompt Transformer
- URL: http://arxiv.org/abs/2310.03253v2
- Date: Mon, 5 Feb 2024 20:51:25 GMT
- Title: Molecule Design by Latent Prompt Transformer
- Authors: Deqian Kong, Yuhao Huang, Jianwen Xie, Ying Nian Wu
- Abstract summary: This paper proposes a latent prompt Transformer model for solving challenging optimization problems such as molecule design.
The goal is to find molecules with optimal values of a target chemical or biological property that can be computed by an existing software.
- Score: 61.68502207071992
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper proposes a latent prompt Transformer model for solving challenging
optimization problems such as molecule design, where the goal is to find
molecules with optimal values of a target chemical or biological property that
can be computed by an existing software. Our proposed model consists of three
components. (1) A latent vector whose prior distribution is modeled by a Unet
transformation of a Gaussian white noise vector. (2) A molecule generation
model that generates the string-based representation of molecule conditional on
the latent vector in (1). We adopt the causal Transformer model that takes the
latent vector in (1) as prompt. (3) A property prediction model that predicts
the value of the target property of a molecule based on a non-linear regression
on the latent vector in (1). We call the proposed model the latent prompt
Transformer model. After initial training of the model on existing molecules
and their property values, we then gradually shift the model distribution
towards the region that supports desired values of the target property for the
purpose of molecule design. Our experiments show that our proposed model
achieves state of the art performances on several benchmark molecule design
tasks.
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