FedGT: Federated Node Classification with Scalable Graph Transformer

Graphs are widely used to model relational data. As graphs are getting larger and larger in real-world scenarios, there is a trend to store and compute subgraphs in multiple local systems. For example, recently proposed \emph{subgraph federated learning} methods train Graph Neural Networks (GNNs) distributively on local subgraphs and aggregate GNN parameters with a central server. However, existing methods have the following limitations: (1) The links between local subgraphs are missing in subgraph federated learning. This could severely damage the performance of GNNs that follow message-passing paradigms to update node/edge features. (2) Most existing methods overlook the subgraph heterogeneity issue, brought by subgraphs being from different parts of the whole graph. To address the aforementioned challenges, we propose a scalable \textbf{Fed}erated \textbf{G}raph \textbf{T}ransformer (\textbf{FedGT}) in the paper. Firstly, we design a hybrid attention scheme to reduce the complexity of the Graph Transformer to linear while ensuring a global receptive field with theoretical bounds. Specifically, each node attends to the sampled local neighbors and a set of curated global nodes to learn both local and global information and be robust to missing links. The global nodes are dynamically updated during training with an online clustering algorithm to capture the data distribution of the corresponding local subgraph. Secondly, FedGT computes clients' similarity based on the aligned global nodes with optimal transport. The similarity is then used to perform weighted averaging for personalized aggregation, which well addresses the data heterogeneity problem. Moreover, local differential privacy is applied to further protect the privacy of clients. Finally, extensive experimental results on 6 datasets and 2 subgraph settings demonstrate the superiority of FedGT.

Multi-scale Iterative Refinement towards Robust and Versatile Molecular Docking

Molecular docking is a key computational tool utilized to predict the binding conformations of small molecules to protein targets, which is fundamental in the design of novel drugs. Despite recent advancements in geometric deep learning-based approaches leading to improvements in blind docking efficiency, these methods have encountered notable challenges, such as limited generalization performance on unseen proteins, the inability to concurrently address the settings of blind docking and site-specific docking, and the frequent occurrence of physical implausibilities such as inter-molecular steric clash. In this study, we introduce DeltaDock, a robust and versatile framework designed for efficient molecular docking to overcome these challenges. DeltaDock operates in a two-step process: rapid initial complex structures sampling followed by multi-scale iterative refinement of the initial structures. In the initial stage, to sample accurate structures with high efficiency, we develop a ligand-dependent binding site prediction model founded on large protein models and graph neural networks. This model is then paired with GPU-accelerated sampling algorithms. The sampled structures are updated using a multi-scale iterative refinement module that captures both protein-ligand atom-atom interactions and residue-atom interactions in the following stage. Distinct from previous geometric deep learning methods that are conditioned on the blind docking setting, DeltaDock demonstrates superior performance in both blind docking and site-specific docking settings. Comprehensive experimental results reveal that DeltaDock consistently surpasses baseline methods in terms of docking accuracy. Furthermore, it displays remarkable generalization capabilities and proficiency for predicting physically valid structures, thereby attesting to its robustness and reliability in various scenarios.

Sparse Attention-Based Neural Networks for Code Classification

Categorizing source codes accurately and efficiently is a challenging problem in real-world programming education platform management. In recent years, model-based approaches utilizing abstract syntax trees (ASTs) have been widely applied to code classification tasks. We introduce an approach named the Sparse Attention-based neural network for Code Classification (SACC) in this paper. The approach involves two main steps: In the first step, source code undergoes syntax parsing and preprocessing. The generated abstract syntax tree is split into sequences of subtrees and then encoded using a recursive neural network to obtain a high-dimensional representation. This step simultaneously considers both the logical structure and lexical level information contained within the code. In the second step, the encoded sequences of subtrees are fed into a Transformer model that incorporates sparse attention mechanisms for the purpose of classification. This method efficiently reduces the computational cost of the self-attention mechanisms, thus improving the training speed while preserving effectiveness. Our work introduces a carefully designed sparse attention pattern that is specifically designed to meet the unique needs of code classification tasks. This design helps reduce the influence of redundant information and enhances the overall performance of the model. Finally, we also deal with problems in previous related research, which include issues like incomplete classification labels and a small dataset size. We annotated the CodeNet dataset with algorithm-related labeling categories, which contains a significantly large amount of data. Extensive comparative experimental results demonstrate the effectiveness and efficiency of SACC for the code classification tasks.

AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking

User modeling, which aims to capture users' characteristics or interests, heavily relies on task-specific labeled data and suffers from the data sparsity issue. Several recent studies tackled this problem by pre-training the user model on massive user behavior sequences with a contrastive learning task. Generally, these methods assume different views of the same behavior sequence constructed via data augmentation are semantically consistent, i.e., reflecting similar characteristics or interests of the user, and thus maximizing their agreement in the feature space. However, due to the diverse interests and heavy noise in user behaviors, existing augmentation methods tend to lose certain characteristics of the user or introduce noisy behaviors. Thus, forcing the user model to directly maximize the similarity between the augmented views may result in a negative transfer. To this end, we propose to replace the contrastive learning task with a new pretext task: Augmentation-Adaptive SelfSupervised Ranking (AdaptSSR), which alleviates the requirement of semantic consistency between the augmented views while pre-training a discriminative user model. Specifically, we adopt a multiple pairwise ranking loss which trains the user model to capture the similarity orders between the implicitly augmented view, the explicitly augmented view, and views from other users. We further employ an in-batch hard negative sampling strategy to facilitate model training. Moreover, considering the distinct impacts of data augmentation on different behavior sequences, we design an augmentation-adaptive fusion mechanism to automatically adjust the similarity order constraint applied to each sample based on the estimated similarity between the augmented views. Extensive experiments on both public and industrial datasets with six downstream tasks verify the effectiveness of AdaptSSR.

A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design

Structure-based drug design (SBDD), which utilizes the three-dimensional geometry of proteins to identify potential drug candidates, is becoming increasingly vital in drug discovery. However, traditional methods based on physiochemical modeling and experts' domain knowledge are time-consuming and laborious. The recent advancements in geometric deep learning, which integrates and processes 3D geometric data, coupled with the availability of accurate protein 3D structure predictions from tools like AlphaFold, have significantly propelled progress in structure-based drug design. In this paper, we systematically review the recent progress of geometric deep learning for structure-based drug design. We start with a brief discussion of the mainstream tasks in structure-based drug design, commonly used 3D protein representations and representative predictive/generative models. Then we delve into detailed reviews for each task (binding site prediction, binding pose generation, \emph{de novo} molecule generation, linker design, and binding affinity prediction), including the problem setup, representative methods, datasets, and evaluation metrics. Finally, we conclude this survey with the current challenges and highlight potential opportunities of geometric deep learning for structure-based drug design.We curate a GitHub repo containing the related papers \url{https://github.com/zaixizhang/Awesome-SBDD}.

Learning Subpocket Prototypes for Generalizable Structure-based Drug Design

Generating molecules with high binding affinities to target proteins (a.k.a. structure-based drug design) is a fundamental and challenging task in drug discovery. Recently, deep generative models have achieved remarkable success in generating 3D molecules conditioned on the protein pocket. However, most existing methods consider molecular generation for protein pockets independently while neglecting the underlying connections such as subpocket-level similarities. Subpockets are the local protein environments of ligand fragments and pockets with similar subpockets may bind the same molecular fragment (motif) even though their overall structures are different. Therefore, the trained models can hardly generalize to unseen protein pockets in real-world applications. In this paper, we propose a novel method DrugGPS for generalizable structure-based drug design. With the biochemical priors, we propose to learn subpocket prototypes and construct a global interaction graph to model the interactions between subpocket prototypes and molecular motifs. Moreover, a hierarchical graph transformer encoder and motif-based 3D molecule generation scheme are used to improve the model's performance. The experimental results show that our model consistently outperforms baselines in generating realistic drug candidates with high affinities in challenging out-of-distribution settings.

An Equivariant Generative Framework for Molecular Graph-Structure Co-Design

Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine learning-based generative models have emerged as promising approaches for \emph{de novo} molecule design. However, further refinement of methodology is highly desired as most existing methods lack unified modeling of 2D topology and 3D geometry information and fail to effectively learn the structure-property relationship for molecule design. Here we present MolCode, a roto-translation equivariant generative framework for \underline{Mol}ecular graph-structure \underline{Co-de}sign. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Extensive experimental results show that MolCode outperforms previous methods on a series of challenging tasks including \emph{de novo} molecule design, targeted molecule discovery, and structure-based drug design. Particularly, MolCode not only consistently generates valid (99.95$\%$ Validity) and diverse (98.75$\%$ Uniqueness) molecular graphs/structures with desirable properties, but also generate drug-like molecules with high affinity to target proteins (61.8$\%$ high-affinity ratio), which demonstrates MolCode's potential applications in material design and drug discovery. Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation.