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FAME: Fragment-based Conditional Molecular Generation for Phenotypic Drug Discovery
Thai-Hoang Pham1, Lei Xie2, Ping Zhang3
1Department of Computer Science and Engineering, The Ohio State University, Columbus, USA.
Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining
|May 5, 2022
Summary
We developed FAME, a novel deep graph generative model for phenotypic molecular design using gene expression data. FAME generates valid molecules with desired activity by denoising gene expression profiles and using a fragment-based approach.
Area of Science:
- Computational chemistry and cheminformatics
- Machine learning in drug discovery
- Genomics and bioinformatics
Background:
- De novo molecular design is crucial but challenging in drug discovery due to complex chemical spaces.
- Existing deep generative models often focus on molecular distribution learning or target-based design, limiting real-world applications.
- Phenotypic molecular design, especially gene expression-based, offers advantages for discovering first-in-class drugs.
Purpose of the Study:
- To propose the first deep graph generative model (FAME) for phenotypic molecular design, specifically using gene expression profiles.
- To address the challenges of learning the molecular generation distribution from noisy gene expression data.
- To generate novel molecules with desired biological activity through a gene expression-driven approach.
Main Methods:
- Developed a gene expression denoising (GED) model using a contrastive objective function to reduce noise in gene expression data.
- Designed FAME, a conditional variational autoencoder, to generate molecules from denoised gene expression profiles.
- Employed a fragment-based autoregressive generation strategy within FAME to enhance molecular generation.
Main Results:
- FAME successfully generates novel molecules with high validity and desired biological activity from gene expression data.
- The proposed GED model effectively reduces noise in gene expression data, improving downstream molecular generation.
- FAME outperforms existing SMILES-based and graph-based deep generative models in phenotypic molecular design tasks.
Conclusions:
- FAME represents a significant advancement in deep graph generative models for phenotypic molecular design.
- The fragment-based generation strategy and denoising approach are key to FAME's success.
- The developed noise reduction technique for omics data has broader implications for phenotypic drug discovery.
