PerturbEnergy：基于条件能量先验的细胞扰动响应预测方法
Learning Perturbation-Specific Representations through Conditional Energy Prior for Cellular Response Prediction
药物研发圈的可以看看，这篇提出了 PerturbEnergy，用能量先验让模型更专注扰动效应，DEG 预测刷到了 SOTA，代码也开源了。
针对细胞扰动预测中扰动特异性信号易被细胞复杂性掩盖的问题，论文提出 PerturbEnergy，对扰动表征引入条件能量先验，使与给定扰动兼容的表征获得更低能量。作者证明其 ELBO 与完整自编码器的 ELBO 等价，且优化该目标可提高表征与扰动之间条件互信息的下界。在四个基准数据集上，该方法将潜在空间中的扰动识别提升最高达 83.2%，并在 top-50/100/200 差异表达基因（DEG）预测上取得当前最优成绩，在高度可变基因（HVG）预测上保持竞争力。代码已开源于 GitHub。
Learning Perturbation-Specific Representations through Conditional Energy Prior for Cellular Response Prediction
AI-based cellular perturbation prediction is essential to accelerate drug discovery and therapeutic identification. However, perturbation-specific signals are generally obscured by cellular complexity, making it difficult for existing methods to correctly capture perturbation effects. To address this problem, we propose PerturbEnergy, which introduces a conditional energy-based prior over perturbation representations. It assigns lower energy (higher probability) to representations compatible with the given perturbation and higher energy to others, thereby explicitly tilting the latent space toward perturbation-preferred regions. Besides, PerturbEnergy can be integrated into autoencoder architectures as we find its Evidence Lower Bound (ELBO) is equivalent to the ELBO of the complete autoencoder. We further prove that optimizing PerturbEnergy increases the lower bound on the conditional mutual information between representation and perturbation. Extensive experiments on four benchmark datasets demonstrate the effectiveness of our PerturbEnergy: it improves perturbation recognition especially in latent space with improvement of up to 83.2\%; It also achieves state-of-the-art performance for predicting top-50/100/200 differentially expressed gene(DEG) response and remaining competitive on highly variable gene(HVG) prediction. The code is provided: https://github.com/BlueWhaleLab/PerturbEnergy
