Predicting drug-perturbed transcriptional responses using multi-conditional diffusion transformer

Drug-perturbed transcriptomes are important for personalized medicine and drug discovery. Nevertheless, the existing high-throughput screening and sequencing techniques for drug-perturbed transcriptomes remain expensive and time-consuming. In this study, we propose a novel multi-condition diffusion transformer model, designated as perturbation diffusion transformer (PertDiT), which is tailored for conditionally generating the perturbed transcriptomes based on drug text information. PertDiT combines the potent transformer architecture with the text representation of pre-trained large language models and utilizes a novel perturbation and transcriptome fusion modules. We have designed two network structures, namely, CrossDiT and CatCrossDiT, applicable to drug discovery and personalized medicine scenarios, respectively. Through a comprehensive set of metrics and an effective data splitting strategy, our model outperforms existing methods, demonstrating a superior ability in post-perturbation transcriptome reconstruction and the prediction of perturbation-induced transcriptional changes. The rationality and effectiveness of the model structure have also been meticulously validated.