In early drug discovery, virtual screening based on deep learning, virtual screening based on molecular docking, and molecular dynamics are three widely used computational strategies, but they always face a trade-off between throughput, search stability, and physical fidelity. This article discusses how quantum computing can be integrated into these processes under the constraints of Noisy Intermediate-Scale Quantum (NISQ). At present, the most realistic role of quantum computing is not the complete replacement of classical processes, but modular coprocessing for selected decision-sensitive subroutines. In the screening of deep learning, quantum modules are mainly inserted into selected components of the model. In predictive models, they are used to enhance representation learning or feature extraction. In generative models, they serve as priors or generators. In docking screening, quantum integration is suitable for specific substeps such as site recognition, pose search, and flexible docking. In molecular dynamics, representative examples include ground state ab initio molecular dynamics, annealer-based trajectory propagation, and excited state molecular dynamics, while most large-scale sampling is still done by classical methods. The actual problem in these scenarios is not whether the quantum module can be inserted, but whether it can provide repeatable gains related to decision-making under the constraints of actual running time and resources. Therefore, we emphasize strong classical baselines, reliable ranking and calibration, transparent resource reporting, and evaluation at downstream decision points as key criteria for assessing progress in the near term.

The precise delineation of nasopharyngeal carcinoma (NPC) is a critical prerequisite for radiation therapy, but manual methods are inefficient and inconsistent. Current automated segmentation techniques are challenged by the complexity of multi-modal inputs and multi-target outputs, including Organs at Risk (OARs), Gross Tumor Volume of the Primary Tumor (GTVp), Gross Tumor Volume of the Nodal Metastases (GTVn), clinical target volume prescribed with 70 Gy (CTV70), and clinical target volume prescribed with 63 Gy (CTV63). This process is frequently hindered by gradient conflicts during multitask optimization. To resolve these issues, we propose Grad-MTSeg, a novel deep learning framework. Our approach introduces two core innovations: Unidirectional Anatomic Guidance (UAG) to leverage CT structural priors for improved MRI-based segmentation, and Hierarchical Gradient Optimization (HGO) to alleviate destructive gradient interference among tasks. Our framework improves segmentation accuracy for relevant NPC tasks by effectively resolving conflicts across OARs, GTVs, and CTVs. Validation on three external datasets confirms that Grad-MTSeg provides an efficient and precise solution for complex multimodal segmentation, advancing the automation of NPC radiotherapy planning.

Accurately deconvoluting cellular composition from bulk RNA-seq data is pivotal for understanding the tumor microenvironment and advancing precision medicine. Existing methods often struggle to consistently and accurately quantify cell types across heterogeneous RNA-seq datasets, particularly when ground truths are unavailable. In this study, we introduce DECEPTICON, a deconvolution strategy inspired by the Anna Karenina principle, which postulates that successful outcomes share common traits, while failures are more varied. DECEPTICON selects top-performing methods by leveraging correlations between different strategies and combines them dynamically to enhance performance. Our approach demonstrates superior accuracy in predicting cell-type proportions across multiple tumor datasets, improving correlation by 23.9% and reducing root mean square error by 73.5% compared to the best of 50 analyzed strategies. Applied to The Cancer Genome Atlas (TCGA) datasets for breast carcinoma, cervical squamous cell carcinoma, and lung adenocarcinoma, DECEPTICON-based predictions showed improved differentiation between patient prognoses. This correlation-based strategy offers a reliable, flexible tool for deconvoluting complex transcriptomic data and highlights its potential in refining prognostic assessments in oncology and advancing cancer biology.

Single-cell sequencing has advanced our understanding of cellular heterogeneity and disease pathology, offering insights into cellular behavior and immune mechanisms. However, extracting meaningful phenotype-related features is challenging due to noise, batch effects, and irrelevant biological signals. To address this, we introduce Deep scSTAR (DscSTAR), a deep learning-based tool designed to enhance phenotype-associated features. DscSTAR identified HSP+ FKBP4+ T cells in CD8+ T cells, which linked to immune dysfunction and resistance to immune checkpoint blockade in non-small cell lung cancer. It has also enhanced spatial transcriptomics analysis of renal cell carcinoma, revealing interactions between cancer cells, CD8+ T cells, and tumor-associated macrophages that may promote immune suppression and affect outcomes. In hepatocellular carcinoma, it highlighted the role of S100A12+ neutrophils and cancer-associated fibroblasts in forming tumor immune barriers and potentially contributing to immunotherapy resistance. These findings demonstrate DscSTAR's capacity to model and extract phenotype-specific information, advancing our understanding of disease mechanisms and therapy resistance.

Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.

The accurate prediction of the effect of amino acid mutations for protein-protein interactions (PPI $\Delta \Delta G$) is a crucial task in protein engineering, as it provides insight into the relevant biological processes underpinning protein binding and provides a basis for further drug discovery. In this study, we propose MpbPPI, a novel multi-task pre-training-based geometric equivariance-preserving framework to predict PPI $\Delta \Delta G$. Pre-training on a strictly screened pre-training dataset is employed to address the scarcity of protein-protein complex structures annotated with PPI $\Delta \Delta G$ values. MpbPPI employs a multi-task pre-training technique, forcing the framework to learn comprehensive backbone and side chain geometric regulations of protein-protein complexes at different scales. After pre-training, MpbPPI can generate high-quality representations capturing the effective geometric characteristics of labeled protein-protein complexes for downstream $\Delta \Delta G$ predictions. MpbPPI serves as a scalable framework supporting different sources of mutant-type (MT) protein-protein complexes for flexible application. Experimental results on four benchmark datasets demonstrate that MpbPPI is a state-of-the-art framework for PPI $\Delta \Delta G$ predictions. The data and source code are available at https://github.com/arantir123/MpbPPI.