Online Continual Learning (OCL) learns from non-independently and identically distributed streaming data with unknown task boundaries during training and testing. Previous methods suffer from the shortcut feature trap and limited plasticity, leading to two requirements: attribute invariance and structure invariance. The former requires to capture the attributes of objects which maintain invariance during all sessions of OCL, while the latter requires to capture the relation of different attributes during OCL. From the causal invariant representation perspective, we propose Quadruplet Augmentation (QuadAug) by preserving attribute and structure invariance via data and channel augmentation with four types of augmentation strategies. First, we build a fine-grained causal graph of OCL to isolate the session-invariant attributes from confounders. Then, by observing different roles of amplitude and phase components of Fourier domain during knowledge transfer, QuadAug preserves attribute invariance by an Amplitude-Phase augmentation (AP-aug) module via a bidirectional data augmentation strategy, to intervene subtle confounders: the single-session class factor and the class-irrelevant factor. Finally, by decomposing the structure invariance into two necessary conditions: channel independence and channel sufficiency, QuadAug preserves structure invariance by an Independence-Sufficiency augmentation (IS-aug) module, which preserves the channel independence property with an inter-channel discrepancy constraint, and the channel sufficiency property with an adversarial augmentation constraint. QuadAug produces significant improvement on four sequential datasets and three blurry datasets for OCL.

Antibodies defend our health by binding to antigens with high specificity and potentiality, primarily relying on the Complementarity-Determining Region (CDR). Yet, current experimental methods of discovering new antibody CDRs are heavily time-consuming. Computational design could alleviate this burden; especially, protein language models have proven quite beneficial in many recent studies. However, most existing models solely focus on antibody potentiality and struggle to encapsulate the diverse range of plausible CDR candidates, limiting their effectiveness in real-world scenarios as binding is only one factor in the multitude of drug-forming criteria. In this paper, we introduce PG-AbD, a framework uniting Generative Flow Networks (GFlowNets) and pretrained Protein Language Models (PLMs) to successfully generate highly potent, diverse and novel antibody candidates. We innovatively construct a Products of Experts (PoE) composed by the global-distribution-modeling PLM and the local-distribution-modeling Potts Model to serve as the reward function of GFlowNet. The joint training paradigm is introduced, where PoE is trained by contrastive divergence with the negative samples generated by GFlowNet, and then guides GFlowNet to sample diverse antibody candidates. We evaluate PG-AbD on extensive antibody design benchmarks. It significantly outperforms existing methods in diversity (13.5% on RabDab, 31.1% on SabDab) while maintaining optimal potential and novelty. Generated antibodies are also found to form stable, regular 3D structures with their corresponding antigens, demonstrating the great potential of PG-AbD to accelerate real-world antibody discovery.

BACKGROUND: The emergence of single-cell (SC) and spatial transcriptomics (ST) has revolutionized our understanding of gene expression dynamics in complex tissues. However, it also presents challenges for data analysis and visualization, particularly due to the complexity of ST data and the diversity of analysis platforms. The SCNT (Single-Cell, Single-Nucleus, and Spatial Transcriptomics Analysis and Visualization Tools) package was developed to address these challenges by providing an efficient and user-friendly tool for processing, analyzing, and visualizing SC and ST data. RESULTS: SCNT is an R-based package that integrates widely used tools such as Seurat and ggplot2, enabling seamless conversion between Seurat and H5ad formats. The package supports high-resolution spatial visualization, including customizable gene expression and clustering plots. SCNT also simplifies key data analysis steps, such as quality control, dimensionality reduction, and doublet detection, significantly enhancing workflow efficiency. We tested SCNT on publicly available PBMC dataset, Visum and Visium HD human kidney tissue data, demonstrating its effectiveness. CONCLUSIONS: SCNT offers a valuable tool for researchers exploring SC and ST data. Its simplicity, flexibility, and powerful visualization capabilities provide a streamlined workflow for both novice and advanced users. Future developments will focus on expanding support for additional ST platforms and enhancing multi-omics data integration.

As a continual learning paradigm where non-stationary data arrive in the form of streams and training occurs whenever a small batch of samples is accumulated, general continual learning (GCL) suffers from both inter-task bias and intra-task bias. Existing GCL methods can hardly simultaneously handle two issues since it requires models to avoid from lying into the spurious correlation trap of GCL. From a causal perspective, we formalize a structural causality model of GCL and conclude that spurious correlation exists not only between confounders and input, but also within multiple causal variables. Inspired by frequency transformation techniques which harbor intricate patterns of image comprehension, we propose a plug-and-play module: the Dual-Domain Division Multiplex (D3M) unit, which intervenes confounders and multiple causal factors over frequency and spatial domains with a two-stage pseudo causal intervention strategy. Typically, D3M consists of a frequency division multiplexer (FDM) module and a spatial division multiplexer (SDM) module, each of which prioritizes target-relevant causal features by dividing and multiplexing features over frequency domain and spatial domain, respectively. As a lightweight and model-agonistic unit, D3M can be seamlessly integrated into most current GCL methods. Extensive experiments on four popular datasets demonstrate that D3M significantly enhances accuracy and diminishes catastrophic forgetting compared to current methods. The code is available at https://github.com/wangsfan/D3M.

Inverse protein folding is a fundamental task in computational protein design, which aims to design protein sequences that fold into the desired backbone structures. While the development of machine learning algorithms for this task has seen significant success, the prevailing approaches, which predominantly employ a discriminative formulation, frequently encounter the error accumulation issue and often fail to capture the extensive variety of plausible sequences. To fill these gaps, we propose Bridge-IF, a generative diffusion bridge model for inverse folding, which is designed to learn the probabilistic dependency between the distributions of backbone structures and protein sequences. Specifically, we harness an expressive structure encoder to propose a discrete, informative prior derived from structures, and establish a Markov bridge to connect this prior with native sequences. During the inference stage, Bridge-IF progressively refines the prior sequence, culminating in a more plausible design. Moreover, we introduce a reparameterization perspective on Markov bridge models, from which we derive a simplified loss function that facilitates more effective training. We also modulate protein language models (PLMs) with structural conditions to precisely approximate the Markov bridge process, thereby significantly enhancing generation performance while maintaining parameter-efficient training. Extensive experiments on well-established benchmarks demonstrate that Bridge-IF predominantly surpasses existing baselines in sequence recovery and excels in the design of plausible proteins with high foldability. The code is available at https://github.com/violet-sto/Bridge-IF.

Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field.

Molecular de novo design is a critical yet challenging task in scientific fields, aiming to design novel molecular structures with desired property profiles. Significant progress has been made by resorting to generative models for graphs. However, limited attention is paid to hierarchical generative models, which can exploit the inherent hierarchical structure (with rich semantic information) of the molecular graphs and generate complex molecules of larger size that we shall demonstrate to be difficult for most existing models. The primary challenge to hierarchical generation is the non-differentiable issue caused by the generation of intermediate discrete coarsened graph structures. To sidestep this issue, we cast the tricky hierarchical generation problem over discrete spaces as the reverse process of hierarchical representation learning and propose MolHF, a new hierarchical flow-based model that generates molecular graphs in a coarse-to-fine manner. Specifically, MolHF first generates bonds through a multi-scale architecture, then generates atoms based on the coarsened graph structure at each scale. We demonstrate that MolHF achieves state-of-the-art performance in random generation and property optimization, implying its high capacity to model data distribution. Furthermore, MolHF is the first flow-based model that can be applied to model larger molecules (polymer) with more than 100 heavy atoms. The code and models are available at https://github.com/violet-sto/MolHF.

Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as a black-box optimization problem over the *discrete* chemical space. In practice, multiple conflicting objectives and costly evaluations (e.g., wet-lab experiments) make the *diversity* of candidates paramount. Computational methods have achieved initial success but still struggle with considering diversity in both objective and search space. To fill this gap, we propose a multi-objective Bayesian optimization (MOBO) algorithm leveraging the hypernetwork-based GFlowNets (HN-GFN) as an acquisition function optimizer, with the purpose of sampling a diverse batch of candidate molecular graphs from an approximate Pareto front. Using a single preference-conditioned hypernetwork, HN-GFN learns to explore various trade-offs between objectives. We further propose a hindsight-like off-policy strategy to share high-performing molecules among different preferences in order to speed up learning for HN-GFN. We empirically illustrate that HN-GFN has adequate capacity to generalize over preferences. Moreover, experiments in various real-world MOBO settings demonstrate that our framework predominantly outperforms existing methods in terms of candidate quality and sample efficiency. The code is available at https://github.com/violet-sto/HN-GFN.