Summary of Molecule Design by Latent Prompt Transformer, By Deqian Kong et al.
Molecule Design by Latent Prompt Transformer
by Deqian Kong, Yuhao Huang, Jianwen Xie, Edouardo Honig, Ming Xu, Shuanghong Xue, Pei Lin, Sanping Zhou, Sheng Zhong, Nanning Zheng, Ying Nian Wu
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel generative model called Latent Prompt Transformer (LPT) for molecule design, which frames the problem as a conditional generative modeling task. LPT consists of three components: a latent vector with a learnable prior distribution, a molecule generation model based on a causal Transformer, and a property prediction model that predicts target properties and/or constraint values using the latent prompt. The model can be learned by maximum likelihood estimation on molecule-property pairs, and during property optimization, the latent prompt is inferred from target properties and constraints through posterior sampling and used to guide autoregressive molecule generation. The authors demonstrate the effectiveness of LPT in discovering useful molecules across various optimization tasks, including single-objective, multi-objective, and structure-constrained optimization, with strong sample efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to design molecules that have specific properties or meet certain requirements. It’s like teaching a computer to draw pictures based on what you want the picture to look like. The computer uses something called a “latent prompt” to generate the molecule. This prompt is learned from existing molecules and their properties, and then used to guide the creation of new molecules that have the desired properties or meet certain constraints. The authors show that this method can successfully design useful molecules with specific properties, and it does so efficiently. |
Keywords
* Artificial intelligence * Autoregressive * Generative model * Likelihood * Optimization * Prompt * Transformer
