arXiv:2607. 23447v1 Announce Type: new Abstract: Predicting cellular responses to unseen chemical perturbations is challenging due to unknown targets and mechanisms, high-dimensional expression responses, and limited experimental coverage of the large small-molecule design space.
PerturbRx is a treatment‑conditioned representation learning framework that learns latent transitions induced by drug interventions. It trains a drug‑ and dose‑conditioned transition predictor using control and treated single‑cell populations, then applies this predictor to pretreatment patient profiles to generate response features without needing post‑treatment data. On TCGA and patient‑derived xenograft benchmarks, PerturbRx outperforms other methods, demonstrating the value of perturbation‑pretrained latent transitions for patient‑level drug‑response prediction.
arXiv:2608. 01734v1 Announce Type: new Abstract: Predicting transcriptomic responses to small-molecule perturbations across cell lines is central to drug discovery, but exhaustive profiling of drug-cell combinations is infeasible.
arXiv:2606. 01042v1 Announce Type: cross Abstract: Perturbation experiments are central to understanding cellular mechanisms, but remain costly and sparse, motivating prediction of gene expression responses for unobserved conditions.
The paper introduces PT‑RAG, a retrieval‑augmented generation framework that enhances deep learning models for predicting cellular transcriptional responses to gene perturbations. PT‑RAG adds a two‑stage retrieval module—semantic retrieval followed by a Gumbel‑Softmax selector—to supply contextually relevant perturbation information, improving predictive accuracy across cell types and perturbations. Experiments on STATE‑style and scGPT backbones show consistent gains, such as a 5.9% reduction in 2‑Wasserstein distance for cross‑cell‑type predictions.
The paper introduces PT‑RAG, a retrieval‑augmented generation framework that enhances cellular perturbation response prediction by incorporating context from related perturbations. PT‑RAG uses a two‑stage retrieval process—semantic retrieval to shortlist candidate perturbations followed by a differentiable Gumbel‑Softmax selector that adapts the selection based on cell state and query perturbation. Experiments on two backbones, a frozen STATE‑style generator and a fully trained scGPT, show that PT‑RAG improves distributional similarity and predictive quality, achieving a 5.9% reduction in 2‑Wasserstein distance on cross‑cell‑type tasks.
arXiv:2606. 11508v1 Announce Type: new Abstract: Accurate prediction of absorption, distribution, metabolism, and excretion (ADME) properties is critical to drug discovery, but remains challenging because ADME endpoints are noisy, interdependent, and often data-limited.
Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical response labels and post-treatment molecular profiles. Preclinical transfer-learning models can simulate drug-induced expression changes but are often hard to interpret and unstable, whereas knowledge-graph methods provide mechanistic context yet remain static and fail to capture drug-induced transcriptomic perturbation dynamics.
The paper introduces a method that uses orthogonal projection to remove the influence of known biochemical features from protein language model (PLM) embeddings, allowing the authors to assess how much these features contribute to protein fitness predictions. By applying this technique to high‑order and interaction effects, they demonstrate that eliminating these interpretable features reduces downstream classifier performance, indicating that PLM embeddings encode patterns correlated with biochemical properties. The authors also show that these biochemical features explain a substantial portion of the variance in the classifier’s predictions, suggesting that PLM embeddings capture biologically relevant information.
arXiv:2606. 01461v1 Announce Type: new Abstract: Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across cancer subtypes.
arXiv:2607. 04557v1 Announce Type: cross Abstract: Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical response labels and post-treatment molecular profiles.
arXiv:2608. 14293v1 Announce Type: cross Abstract: High-content microscopy enables systematic profiling of cellular responses to chemical perturbations, but the scale of the chemical space makes exhaustive phenotypic characterization experimentally infeasible.