IDCLP: A Deep Learning Framework for Predicting Chemical-Induced Gene Expression Profiles Through Multisource Data Integration

Phenotypic Drug Discovery enables the exploration and identification of new compounds with potential therapeutic value without the need to predefine the molecular targets of drug action or hypothesize their mechanisms in pathology. Chemicalinduced transcriptional profiles offer a comprehensive view of phenotypic responses to drugs and serve as a key tool in phenotype-based compound screening. Hence, it is necessary to develop an algorithm for predicting chemical-induced transcription profiles. Existing works tried to predict the transcription profile based on the structure of a compound, but the results are not yet satisfactory. Transcriptional profiles during the induction process are influenced not only by the structural features of compounds, cellular context, and dosages but also by the complex interactions between compounds and biological entities (e.g. target, diseases, and side effects) and the physicochemical properties of compounds. Here, we propose a deep learning model called IDCLP to predict gene expression profiles induced by de novo compounds. It utilizes a heterogeneous graph attention mechanism for extracting drug embedding and integrates the similarity network fusion algorithm to construct a drug similarity network. Moreover, IDCLP utilizes attention mechanisms to model the associations between drugs and cell lines. Experimental results show that IDCLP outperforms state-of-the-art methods, especially with unseen drugs that are dissimilar from the drugs in the supervised learning. Our implementation of IDCLP is available at https://github.com/sdesignates/IDCLP.git.