Search Results for author:
Found 29 papers, 19 papers with code
FoldMark: Protecting Protein Generative Models with Watermarking
Protein structure is key to understanding protein function and is essential for progress in bioengineering, drug discovery, and molecular biology.
DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking
Despite these advancements, current methods are often tailored for specific docking settings, and limitations such as the neglect of protein side-chain structures, difficulties in handling large binding pockets, and challenges in predicting physically valid structures exist.
Generalized Protein Pocket Generation with Prior-Informed Flow Matching
One critical step in this process involves designing protein pockets, the protein interface binding with the ligand.
FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling
Structure-based drug design (SBDD), which aims to generate 3D ligand molecules binding to target proteins, is a fundamental task in drug discovery.
Towards Few-shot Self-explaining Graph Neural Networks
Subsequently, the predictor mimics the decision-making process, which makes predictions based on the generated explanation.
Model Inversion Attacks Through Target-Specific Conditional Diffusion Models
To alleviate these issues, leveraging on diffusion models' remarkable synthesis capabilities, we propose Diffusion-based Model Inversion (Diff-MI) attacks.
What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding
Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD).
Structure-based Drug Design Benchmark: Do 3D Methods Really Dominate?
Currently, the field of structure-based drug design is dominated by three main types of algorithms: search-based algorithms, deep generative models, and reinforcement learning.
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation
Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets.
FedGT: Federated Node Classification with Scalable Graph Transformer
However, existing methods have the following limitations: (1) The links between local subgraphs are missing in subgraph federated learning.
Binding-Adaptive Diffusion Models for Structure-Based Drug Design
Then the selected protein-ligand subcomplex is processed with SE(3)-equivariant neural networks, and transmitted back to each atom of the complex for augmenting the target-aware 3D molecule diffusion generation with binding interaction information.
Multi-scale Iterative Refinement towards Robust and Versatile Molecular Docking
This model is then paired with GPU-accelerated sampling algorithms.
Sparse Attention-Based Neural Networks for Code Classification
We introduce an approach named the Sparse Attention-based neural network for Code Classification (SACC) in this paper.
AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking
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.
Full-Atom Protein Pocket Design via Iterative Refinement
In the initial stage, the residue types and backbone coordinates are refined using a hierarchical context encoder, complemented by two structure refinement modules that capture both inter-residue and pocket-ligand interactions.
Geometric Deep Learning for Structure-Based Drug Design: A Survey
Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates.
Learning Subpocket Prototypes for Generalizable Structure-based Drug Design
Generating molecules with high binding affinities to target proteins (a. k. a.
An Equivariant Generative Framework for Molecular Graph-Structure Co-Design
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.
Backdoor Defense via Deconfounded Representation Learning
The other clean model dedicates to capturing the desired causal effects by minimizing the mutual information with the confounding representations from the backdoored model and employing a sample-wise re-weighting scheme.
Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense
Experiments on two public datasets show that ClusterAttack can effectively degrade the performance of FedRec systems while circumventing many defense methods, and UNION can improve the resistance of the system against various untargeted attacks, including our ClusterAttack.
FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information
Existing defenses focus on preventing a small number of malicious clients from poisoning the global model via robust federated learning methods and detecting malicious clients when there are a large number of them.
Hierarchical Graph Transformer with Adaptive Node Sampling
The Transformer architecture has achieved remarkable success in a number of domains including natural language processing and computer vision.
FLCert: Provably Secure Federated Learning against Poisoning Attacks
Our key idea is to divide the clients into groups, learn a global model for each group of clients using any existing federated learning method, and take a majority vote among the global models to classify a test input.
Model Inversion Attacks against Graph Neural Networks
One famous privacy attack against data analysis models is the model inversion attack, which aims to infer sensitive data in the training dataset and leads to great privacy concerns.
FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients
FLDetector aims to detect and remove the majority of the malicious clients such that a Byzantine-robust FL method can learn an accurate global model using the remaining clients.
ProtGNN: Towards Self-Explaining Graph Neural Networks
In this work, we propose Prototype Graph Neural Network (ProtGNN), which combines prototype learning with GNNs and provides a new perspective on the explanations of GNNs.
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction
To bridge this gap, we propose Motif-based Graph Self-supervised Learning (MGSSL) by introducing a novel self-supervised motif generation framework for GNNs.
GraphMI: Extracting Private Graph Data from Graph Neural Networks
Then we design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.
Backdoor Attacks to Graph Neural Networks
Specifically, we propose a \emph{subgraph based backdoor attack} to GNN for graph classification.
