Journal Article

Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders

Zhebrak, Alexander, Shayakhmetov, Rim, Aliper, Alexander, Kuznetsov, Maksim, Polykovskiy, Daniil, Kadurin, Artur, Nikolenko, Sergey

2020

Overview

Gene expression profiles are useful for assessing the efficacy and side effects of drugs. In this paper, we propose a new generative model that infers drug molecules that could induce a desired change in gene expression. Our model-the Bidirectional Adversarial Autoencoder-explicitly separates cellular processes captured in gene expression changes into two feature sets: those and to the drug incubation. The model uses features to produce a drug hypothesis. We have validated our model on the LINCS L1000 dataset by generating molecular structures in the SMILES format for the desired transcriptional response. In the experiments, we have shown that the proposed model can generate novel molecular structures that could induce a given gene expression change or predict a gene expression difference after incubation of a given molecular structure. The code of the model is available at https://github.com/insilicomedicine/BiAAE.

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: adversarial autoencoders / conditional generation / Datasets / Deep learning / Drug development / drug discovery / Gene expression / generative models / Machine learning / Neural networks / Pharmacology / Proteins / representation learning / Transcriptomes