DrugPT: A Flexible Framework for Integrating Gene and Chemical Representations in Perturbation Modeling
Zhu, L.; Luo, Y.
Show abstract
Accurately modeling the transcriptional response of cells to drug perturbations is critical for drug discovery and precision medicine. Here, we propose DrugPT, a novel Transformer-based framework for predicting gene expression changes upon drug treatment. DrugPT consists of three modular components: (1) a gene representation module that encodes pre-treatment expression profiles using GPT-derived embeddings; (2) a drug representation module that captures chemical structure information using a pre-trained GPT2 model; and (3) a Transformer-based prediction module that integrates gene and drug embeddings to predict perturbed gene expression profiles. Unlike previous approaches, DrugPT formulates the prediction task as a sequence modeling problem, leveraging pre-trained language models for both gene and drug modalities. We demonstrate the architecture of DrugPT and highlight its generalizable design for encoding heterogeneous biological and chemical information. This work introduces a unified modeling framework, laying the foundation for future developments in multimodal perturbation prediction tasks.
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