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Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (M pro ) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity. Structure-based generative chemistry is crucial in computer-aided drug discovery. Here, authors propose PMDM, a conditional generative model for 3D molecule generation tailored to specific targets. Extensive experiments demonstrate that PMDM can effectively generate rational bioactive molecules

Single-cell ATAC-seq technology advances our understanding of single-cell heterogeneity in gene regulation by enabling exploration of epigenetic landscapes and regulatory elements. However, low sequencing depth per cell leads to data sparsity and high dimensionality, limiting the characterization of gene regulatory elements. Here, we develop scAGDE, a single-cell chromatin accessibility model-based deep graph representation learning method that simultaneously learns representation and clustering through explicit modeling of data generation. Our evaluations demonstrated that scAGDE outperforms existing methods in cell segregation, key marker identification, and visualization across diverse datasets while mitigating dropout events and unveiling hidden chromatin-accessible regions. We find that scAGDE preferentially identifies enhancer-like regions and elucidates complex regulatory landscapes, pinpointing putative enhancers regulating the constitutive expression of CTLA4 and the transcriptional dynamics of CD8A in immune cells. When applied to human brain tissue, scAGDE successfully annotated cis-regulatory element-specified cell types and revealed functional diversity and regulatory mechanisms of glutamatergic neurons. Single-cell ATAC-seq reveals gene regulation at individual cell levels but struggles with data sparsity. Here, authors introduce scAGDE, a deep graph learning framework that improves cell embedding and clustering, outperforming existing methods and uncovering key regulatory mechanisms.

Polymethyl methacrylate (PMMA) frequently features in dental restorative materials due to its favorable properties. However, its surface exhibits a propensity for bacterial colonization, and the material can fracture under masticatory pressure. This study incorporated commercially available RHA-1F-II nano-silver loaded zirconium phosphate (Ag-ZrP) into room-temperature cured PMMA at varying mass fractions. Various methods were employed to characterize Ag-ZrP. Subsequently, an examination of the effects of Ag-ZrP on the antimicrobial properties, biosafety, and mechanical properties of PMMA materials was conducted. The results indicated that the antibacterial rate against Streptococcus mutans was enhanced at Ag-ZrP additions of 0%wt, 0.5%wt, 1.0%wt, 1.5%wt, 2.0%wt, 2.5%wt, and 3.0%wt, achieving respective rates of 53.53%, 67.08%, 83.23%, 93.38%, 95.85%, and 98.00%. Similarly, the antibacterial rate against Escherichia coli registered at 31.62%, 50.14%, 64.00%, 75.09%, 86.30%, 92.98%. When Ag-ZrP was introduced at amounts ranging from 1.0% to 1.5%, PMMA materials exhibited peak mechanical properties. However, mechanical strength diminished beyond additions of 2.5%wt to 3.0%wt, relative to the 0%wt group, while PMMA demonstrated no notable cytotoxicity below a 3.0%wt dosage. Thus, it is inferred that optimal antimicrobial and mechanical properties of PMMA materials are achieved with nano-Ag-ZrP (RHA-1F-II) additions of 1.5%wt to 2.0%wt, without eliciting cytotoxicity.

Objective Mutations in GJB2, SLC26A4, and mitochondrial (mt)DNA 12S rRNA genes are the main cause of nonsyndromic hearing impairment. The present study analyzed these mutations in ethnic minority and Han Chinese patients with nonsyndromic hearing impairment from Qinghai, China. Methods The SNPscan assay was used to analyze mutation spectra and frequencies in the two patient groups. Results GJB2 mutations were detected in 9.5% (20/210) of minority patients and 20.88% (48/230) of Han Chinese patients. The most common Han Chinese GJB2 variants were c.235delC and c.299_300delAT, whereas c.235delC and c.109G > A were the most prevalent in minority patients. SLC26A4 mutations were detected in 5.71% (12/210) of minority patients and 14.35% (33/230) of Han Chinese patients, and mtDNA 12S rRNA mutations were detected in 4.28% (9/210) of minority patients and 9.13% (21/230) of Han Chinese patients. Conclusions These data indicate that the mutation frequencies of three deafness-associated genes were significantly higher in Han Chinese patients than in minority patients. Moreover, the GJB2 mutation spectrum was shown to differ between these two patient groups.

The single‐cell RNA sequencing (scRNA‐seq) quantifies the gene expression of individual cells, while the bulk RNA sequencing (bulk RNA‐seq) characterizes the mixed transcriptome of cells. The inference of drug sensitivities for individual cells can provide new insights to understand the mechanism of anti‐cancer response heterogeneity and drug resistance at the cellular resolution. However, pharmacogenomic information related to their corresponding scRNA‐Seq is often limited. Therefore, a transfer learning model is proposed to infer the drug sensitivities at single‐cell level. This framework learns bulk transcriptome profiles and pharmacogenomics information from population cell lines in a large public dataset and transfers the knowledge to infer drug efficacy of individual cells. The results suggest that it is suitable to learn knowledge from pre‐clinical cell lines to infer pre‐existing cell subpopulations with different drug sensitivities prior to drug exposure. In addition, the model offers a new perspective on drug combinations. It is observed that drug‐resistant subpopulation can be sensitive to other drugs (e.g., a subset of JHU006 is Vorinostat‐resistant while Gefitinib‐sensitive); such finding corroborates the previously reported drug combination (Gefitinib + Vorinostat) strategy in several cancer types. The identified drug sensitivity biomarkers reveal insights into the tumor heterogeneity and treatment at cellular resolution. A transfer learning framework for single cell drug response prediction. This framework integrates domain adaptation to learn cell line bulk pharmacogenomics and transfers the knowledge to infer drug sensitivities at single cells before treatment. This ranking‐based framework for drug sensitivity inference provides new strategies to account for intratumoral heterogeneity; providing new perspectives for biomarker discovery and drug combination applications.

Recent advances in spatial transcriptomics have enabled simultaneous preservation of high‐throughput gene expression profiles and the spatial context, enabling high‐resolution exploration of distinct regional characterization in tissue. To effectively understand the underlying biological mechanisms within tissue microenvironments, there is a requisite for methods that can accurately capture external spatial heterogeneity and interpret internal gene regulation from spatial transcriptomics data. However, current methods for region identification often lack the simultaneous characterizing of spatial structure and gene regulation, thereby limiting the ability of spatial dissection and gene interpretation. Here, stDCL is developed, a dual graph contrastive learning method to identify spatial domains and interpret gene regulation in spatial transcriptomics data. stDCL adaptively incorporates gene expression data and spatial information via a graph embedding autoencoder, thereby preserving critical information within the latent embedding representations. In addition, dual graph contrastive learning is proposed to train the model, ensuring that the latent embedding representation closely resembles the actual spatial distribution and exhibits cluster similarity. Benchmarking stDCL against other state‐of‐the‐art clustering methods using complex cortex datasets demonstrates its superior accuracy and effectiveness in identifying spatial domains. Our analysis of the imputation matrices generated by stDCL reveals its capability to reconstruct spatial hierarchical structures and refine differential expression assessment. Furthermore, it is demonstrated that the versatility of stDCL in interpretability of gene regulation, spatial heterogeneity at high resolution, and embryonic developmental patterns. In addition, it is also showed that stDCL can successfully annotate disease‐associated astrocyte subtypes in Alzheimer's disease and unravel multiple relevant pathways and regulatory mechanisms. stDCL, a dual graph contrastive learning method, captures spatial heterogeneity and interprets gene regulation in spatial transcriptomics data. Integrating spatial and gene expression data through graph embeddings, stDCL provides robust spatial characterization and accurate region identification, reconstructs spatial hierarchies, and identifies disease‐associated cell subtypes, unveiling new insights into tissue microenvironments and disease mechanisms.

With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.

Modeling drug-induced transcriptional responses at the single-cell level is essential for advancing human healthcare, particularly in understanding disease mechanisms, assessing therapeutic efficacy, and anticipating adverse effects. However, existing approaches often impose a rigid constraint by enforcing pointwise alignment of latent representations to a standard normal prior, which limits expressiveness and results in biologically uninformative embeddings, especially in complex biological systems. Additionally, many methods inadequately address the challenges of unpaired data, typically relying on naive averaging strategies that ignore cell-type specificity and intercellular heterogeneity. To overcome these limitations, we propose scOTM, a deep learning framework designed to predict single-cell perturbation responses from unpaired data, focusing on generalization to unseen cell types. scOTM integrates prior biological knowledge of perturbations and cellular states, derived from large language models specialized for molecular and single-cell corpora. These informative representations are incorporated into a variational autoencoder with maximum mean discrepancy regularization, allowing flexible modeling of transcriptional shifts without imposing a strict constraint of alignment to a standard normal prior. scOTM further employs optimal transport to establish an efficient and interpretable mapping between control and perturbed distributions, effectively capturing the transcriptional shifts underlying response variation. Extensive experiments demonstrate that scOTM outperforms existing methods in predicting whole-transcriptome responses and identifying top differentially expressed genes. Furthermore, scOTM exhibits superior robustness in data-limited settings and strong generalization capabilities across cell types.

Single‐cell Hi‐C (scHi‐C) has made it possible to analyze chromatin organization at the single‐cell level. However, scHi‐C experiments generate inherently sparse data, which poses a challenge for loop calling methods. The existing approach performs significance tests across the imputed dense contact maps, leading to substantial computational overhead and loss of information at the single‐cell level. To overcome this limitation, a lightweight framework called scGSLoop is proposed, which sets a new paradigm for scHi‐C loop calling by adapting the training and inferencing strategies of graph‐based deep learning to leverage the sequence features and 1D positional information of genomic loci. With this framework, sparsity is no longer a challenge, but rather an advantage that the model leverages to achieve unprecedented computational efficiency. Compared to existing methods, scGSLoop makes more accurate predictions and is able to identify more loops that have the potential to play regulatory roles in genome functioning. Moreover, scGSLoop preserves single‐cell information by identifying a distinct group of loops for each individual cell, which not only enables an understanding of the variability of chromatin looping states between cells, but also allows scGSLoop to be extended for the investigation of multi‐connected hubs and their underlying mechanisms. A lightweight framework called scGSLoop is introduced to detect chromatin loops on single‐cell Hi‐C data. Leveraging graph‐based deep learning, scGSLoop operates on sparse matrices, avoiding resource‐intensive data densification. This model not only achieves computational efficiency, but also accurately predicts loops genome‐wide. It preserves single‐cell information, enabling the investigation of looping variability and multi‐connected hubs.

Background MYO15A is one of the common genes of severe‐to‐profound sensorineural deafness. Mutations in this gene can cause both pre‐ and post‐lingual hearing losses. In this study, a novel MYO15A variant (c.2482C>T) was identified to be associated with autosomal recessive non‐syndromic hearing loss (ARNSHL) in a Chinese Uighur family. Methods To examine the effects of the MYO15A mutation on the morphology and function of the derived hair cell‐like cells, two iPSCs were generated separately from the proband and a mutation‐negative family member and those were then induced to hair cell‐like cells. Results Results showed that this homozygous MYO15A mutation (PVS1 + PM2 + PP1 + PP3), which is located in the N‐terminal domain, displayed significant differences in the morphology and function of hair cell‐like cells between the proband and the normal control, although it had no effect on the totipotency of iPSCs. Conclusion Our study demonstrates that the novel variant c.2482C>T in the MYO15A gene may cause inner ear hair cell dysfunction and audiological disorders in this family. Two iPSCs were generated separately from the proband and a mutation‐negative family member, and those were then induced to hair cell‐like cells to examine the effects of the MYO15A mutation (c.2482C>T) on the morphology and function of those cells. Results demonstrate that the novel mutation may cause inner ear hair cell dysfunction and audiological disorders.