TransGEM: a molecule generation model based on Transformer with gene expression data

作者：Yanguang Liu, Hailong Yu, Xinya Duan, Xiaomin Zhang, Ting Cheng, Feng Jiang, Hao Tang, Yao Ruan, Miao Zhang, Hongyu Zhang, Qingye Zhang · 发表于：Bioinformatics · 年份：2024 · DOI：10.1093/bioinformatics/btae189 · 被引用次数：13 · 研究领域：Computational Drug Discovery Methods、vaccines and immunoinformatics approaches、Gene Regulatory Network Analysis

MOTIVATION: It is difficult to generate new molecules with desirable bioactivity through ligand-based de novo drug design, and receptor-based de novo drug design is constrained by disease target information availability. The combination of artificial intelligence and phenotype-based de novo drug design can generate new bioactive molecules, independent from disease target information. Gene expression profiles can be used to characterize biological phenotypes. The Transformer model can be utilized to capture the associations between gene expression profiles and molecular structures due to its remarkable ability in processing contextual information. RESULTS: We propose TransGEM (Transformer-based model from gene expression to molecules), which is a phenotype-based de novo drug design model. A specialized gene expression encoder is used to embed gene expression difference values between diseased cell lines and their corresponding normal tissue cells into TransGEM model. The results demonstrate that the TransGEM model can generate molecules with desirable evaluation metrics and property distributions. Case studies illustrate that TransGEM model can generate structurally novel molecules with good binding affinity to disease target proteins. The majority of genes with high attention scores obtained from TransGEM model are associated with the onset of the disease, indicating the potential of these genes as disease targets. Therefore, this study provides a new paradigm for de novo dru...