Text-guided Diffusion Model for 3D Molecule Generation
Yanchen Luo, Junfeng Fang, Sihang Li, Zhiyuan Liu, Jiancan Wu, An, Zhang, Wenjie Du, Xiang Wang
TL;DR
This paper introduces TextSMOG, a novel text-guided 3D molecule generation method that leverages language and diffusion models to produce diverse molecular structures based on complex textual descriptions, advancing drug discovery tools.
Contribution
The paper presents a new approach combining language and diffusion models for text-guided small molecule generation, addressing limitations of previous models in handling detailed human language instructions.
Findings
TextSMOG effectively captures information from textual descriptions.
The method produces diverse and stable 3D molecular structures.
Experimental results demonstrate improved guidance and customization capabilities.
Abstract
The de novo generation of molecules with targeted properties is crucial in biology, chemistry, and drug discovery. Current generative models are limited to using single property values as conditions, struggling with complex customizations described in detailed human language. To address this, we propose the text guidance instead, and introduce TextSMOG, a new Text-guided Small Molecule Generation Approach via 3D Diffusion Model which integrates language and diffusion models for text-guided small molecule generation. This method uses textual conditions to guide molecule generation, enhancing both stability and diversity. Experimental results show TextSMOG's proficiency in capturing and utilizing information from textual descriptions, making it a powerful tool for generating 3D molecular structures in response to complex textual customizations.
Peer Reviews
Decision·Submitted to ICLR 2024
(S1): This work explores an important topic of molecule generation. While 2D-based generative models have long been adopted in the pharma industry, models operating in 3D directly are a newer frontier, which many practitioners are excited about, and so developing such models is worthwhile. (S2): The high-level design seems sensible, and it makes use of relatively modern DL components. The text conditioning idea is interesting from an ML point of view (even if I'm not sure about its practicalit
(W1): Many aspects of this work are not clear to me. - (a) Many existing diffusion-based models for generating molecules (or point clouds more generally) have a caveat around number of atoms (points), which has to be fixed beforehand. Is it also the case here? When sampling from the model, do you sample the number of atoms separately? Is that conditioned on the text? - (b) How does $\Gamma$ work? As I understand, it is a model mapping from text to a molecular conformation, i.e. the output is
- This is a novel application of diffusion models on text-guided 3D molecule design. Text can indeed naturally combine multiple conditions to control the generation of molecules that humans want, so this task makes sense. - The paper is well organized.
- This paper is not the first molecule translation task, but the author does not compare with the former baseline [1] which also includes single-objective and multi-objective molecule generation. - [1] Liu, Shengchao, et al. "Multi-modal molecule structure-text model for text-based retrieval and editing." *arXiv preprint arXiv:2212.10789* (2022). - The equivariant diffusion model, iterative latent variable refinement, and multi-modal conversion module are all from existing works , making the
* The results of multiple conditions in Section 4.2 convince the readers about the benefit of the text conditioning. * The examples in Section 4.3 show the flexible conditioning ability, which previous work cannot. * Even the single conditioning is empirically better or competitive to the baseline methods.
* Since the text dataset used in Section 4 is not publicly available, it is hard to reproduce their results in the subsequent research. * Although the direct use of C_p is not recommended in the main text, the empirical evaluation of it is not available. Since the proposed method is complex, the readers would want to see more supporting evidence of the current design choice.
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Taxonomy
TopicsGene expression and cancer classification · Innovative Microfluidic and Catalytic Techniques Innovation · Chemical Synthesis and Analysis
MethodsDiffusion