Due to the vast design space of molecules, generating molecules conditioned on a specific sub-structure relevant to a particular function or therapeutic target is a crucial task in computer-aided drug design. Existing works mainly focus on specific tasks, such as linker design or scaffold hopping, each task requires training a model from scratch, and many well-pretrained De Novo molecule generation model parameters are not effectively utilized. To this end, we propose a two-stage training approach, consisting of condition learning and condition optimization. In the condition learning stage, we adopt the idea of ControlNet and design some meaningful adjustments to make the unconditional generative model learn sub-structure conditioned generation. In the condition optimization stage, by using human preference learning, we further enhance the stability and robustness of sub-structure control. In our experiments, only trained on randomly partitioned sub-structure data, the proposed method outperforms previous techniques by generating more valid and diverse molecules. Our method is easy to implement and can be quickly applied to various pre-trained molecule generation models.

Hypergraph-based modeling has gained significant attention for capturing complex higher-order interactions among vertices. While random walks serve as fundamental tools for analyzing hypergraphs, existing approaches either fail to fully leverage edge-dependent vertex weights (EDVWs) or lack sufficient expressiveness to model intricate hypergraph structures. To address these limitations, we propose a unified random walk framework that integrates hyperedge degrees and vertex weights, offering a more robust approach to hypergraph modeling. We establish equivalence conditions between hypergraph and graph random walks, leading to a novel unified random-walk-based hypergraph Laplacian that incorporates EDVWs, ensuring expressiveness and desirable spectral properties. Building on this foundation, we introduce the General Hypergraph Spectral Convolution (GHSC) framework, which extends existing Graph Convolutional Neural Networks (GCNNs) for effective hypergraph learning, supporting both edge-independent and edge-dependent vertex weights. Extensive experiments across diverse datasets, including citation networks, visual objects, and protein modeling tasks, demonstrate state-of-the-art performance, with notable improvements in protein structure modeling using EDVW-hypergraphs. This work advances the theoretical understanding of hypergraph random walks and spectral theory while providing a versatile framework for deep hypergraph learning. Code is available at GHSC_H_GNNs.

Antibody structure prediction is a critical task in immunological research and therapeutic antibody development. Despite advances in prediction methods, contemporary approaches still face formidable challenges, particularly in accurately modeling Complementarity-determining regions (CDRs). Furthermore, current prediction time costs, typically on the order of minutes, preclude large-scale structure prediction and screening. In this work, we present ImmuFold, a novel deep-learning approach that achieves second-level performance in antibody structure prediction. ImmuFold integrates ImmuBERT, a 650M antibody language model pre-trained on hundreds of millions of natural antibody sequences, with a structure prediction network that directly predicts all-atom structure, encompassing both main chain and side chains. ImmuFold outperforms current methods, including IgFold and AlphaFold2, generating higher-quality antibody structures in approximately one second. Comparative analysis of the antibody binding task demonstrates the superior representational capabilities of ImmuBERT relative to existing language models, a crucial factor underpinning the efficacy of ImmuFold.

Antibodies are proteins produced by the immune system that recognize and bind to specific antigens, and their 3D structures are crucial for understanding their binding mechanism and designing therapeutic interventions. The specificity of antibody-antigen binding predominantly depends on the complementarity-determining regions (CDR) within antibodies.Despite recent advancements in antibody structure prediction, the quality of predicted CDRs remains suboptimal.In this paper, we develop a novel antibody structure refinement method termed FlowAB based on energy-guided flow matching. FlowAB adopts the powerful deep generative method SE(3) flow matching and simultaneously incorporates important physical prior knowledge into the flow model to guide the generation process.The extensive experiments demonstrate that FlowAB can significantly improve the antibody CDR structures. It achieves new state-of-the-art performance on the antibody structure prediction task when used in conjunction with an appropriate prior model while incurring only marginal computational overhead. This advantage makes FlowAB a practical tool in antibody engineering.

Molecular representation learning has shown great success in advancing AI-based drug discovery. A key insight of many recent works is that the 3D geometric structure of molecules provides essential information about their physicochemical properties. Recently, denoising diffusion probabilistic models have achieved impressive performance in molecular 3D conformation generation. However, most existing molecular diffusion models treat each atom as an independent entity, overlooking the dependency among atoms within the substructures. This paper introduces a novel approach that enhances molecular representation learning by incorporating substructural information in the diffusion model framework. We propose a novel diffusion model termed SubgDiff for involving the molecular subgraph information in diffusion. Specifically, SubgDiff adopts three vital techniques: i) subgraph prediction, ii) expectation state, and iii) k-step same subgraph diffusion, to enhance the perception of molecular substructure in the denoising network. Experiments on extensive downstream tasks, especially the molecular force predictions, demonstrate the superior performance of our approach.

Nowadays, with the application of 5G, graph-based recommendation algorithms have become a research hotspot. Graph neural networks encode the graph structure information in the node representation through an iterative neighbor aggregation method, which can effectively alleviate the problem of data sparsity. In addition, more and more information graph can be used in collaborative filtering recommendation, such as user social information graph, user or item attributed information graph, etc. In this paper, we propose a novel heterogeneous information fusion based graph collaborative filtering method, which models graph data from different heterogeneous graph, and combines them together to enhance presentation learning. Through information propagation and aggregation, our model can learn the latent embeddings effectively and enhance the performance of recommendation. Experimental results on different datasets validate the outperformance of the proposed framework.

HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph-structured data. However, most existing convolution filters are localized and determined by the pre-defined initial hypergraph topology, neglecting to explore implicit and long-range relations in real-world data. In this paper, we propose the first learning-based method tailored for constructing adaptive hypergraph structure, termed HypERgrAph Laplacian aDaptor (HERALD), which serves as a generic plug-and-play module for improving the representational power of HGCNNs. Specifically, HERALD adaptively optimizes the adjacency relationship between vertices and hyperedges in an end-to-end manner and thus the task-aware hypergraph is learned. Furthermore, HERALD employs the self-attention mechanism to capture the non-local paired-nodes relation. Extensive experiments on various popular hypergraph datasets for node classification and graph classification tasks demonstrate that our approach obtains consistent and considerable performance enhancement, proving its effectiveness and generalization ability.

Recently, the pretrain-finetuning paradigm has attracted tons of attention in graph learning community due to its power of alleviating the lack of labels problem in many real-world applications. Current studies use existing techniques, such as weight constraint, representation constraint, which are derived from images or text data, to transfer the invariant knowledge from the pre-train stage to fine-tuning stage. However, these methods failed to preserve invariances from graph structure and Graph Neural Network (GNN) style models. In this paper, we present a novel optimal transport-based fine-tuning framework called GTOT-Tuning, namely, Graph Topology induced Optimal Transport fine-Tuning, for GNN style backbones. GTOT-Tuning is required to utilize the property of graph data to enhance the preservation of representation produced by fine-tuned networks. Toward this goal, we formulate graph local knowledge transfer as an Optimal Transport (OT) problem with a structural prior and construct the GTOT regularizer to constrain the fine-tuned model behaviors. By using the adjacency relationship amongst nodes, the GTOT regularizer achieves node-level optimal transport procedures and reduces redundant transport procedures, resulting in efficient knowledge transfer from the pre-trained models. We evaluate GTOT-Tuning on eight downstream tasks with various GNN backbones and demonstrate that it achieves state-of-the-art fine-tuning performance for GNNs.