Tingyang Xu
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View article: De novo design of epitope-specific antibodies via a structure-driven computational workflow
De novo design of epitope-specific antibodies via a structure-driven computational workflow Open
Accurate modeling of antibody-antigen complex structures holds significant potential for advancing biomedical research and the design of therapeutic antibodies. Compared to general proteins, progress in antibody structure prediction and de…
View article: VL-Cogito: Progressive Curriculum Reinforcement Learning for Advanced Multimodal Reasoning
VL-Cogito: Progressive Curriculum Reinforcement Learning for Advanced Multimodal Reasoning Open
Reinforcement learning has proven its effectiveness in enhancing the reasoning capabilities of large language models. Recent research efforts have progressively extended this paradigm to multimodal reasoning tasks. Due to the inherent comp…
View article: DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models
DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models Open
Molecular structure elucidation from spectra is a fundamental challenge in molecular science. Conventional approaches rely heavily on expert interpretation and lack scalability, while retrieval-based machine learning approaches remain cons…
View article: ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning
ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning Open
Reasoning-based large language models have excelled in mathematics and programming, yet their potential in knowledge-intensive medical question answering remains underexplored and insufficiently validated in clinical contexts. To bridge th…
View article: A survey of geometric graph neural networks: data structures, models and applications
A survey of geometric graph neural networks: data structures, models and applications Open
Geometric graphs are a special kind of graph with geometric features, which are vital to model many scientific problems. Unlike generic graphs, geometric graphs often exhibit physical symmetries of translations, rotations, and reflections,…
View article: STAR-R1: Spatial TrAnsformation Reasoning by Reinforcing Multimodal LLMs
STAR-R1: Spatial TrAnsformation Reasoning by Reinforcing Multimodal LLMs Open
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across diverse tasks, yet they lag significantly behind humans in spatial reasoning. We investigate this gap through Transformation-Driven Visual Reasoning …
View article: InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization
InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization Open
Exploring chemical space to find novel molecules that simultaneously satisfy multiple properties is crucial in drug discovery. However, existing methods often struggle with trading off multiple properties due to the conflicting or correlat…
View article: Large Language-Geometry Model: When LLM meets Equivariance
Large Language-Geometry Model: When LLM meets Equivariance Open
Accurately predicting 3D structures and dynamics of physical systems is crucial in scientific applications. Existing approaches that rely on geometric Graph Neural Networks (GNNs) effectively enforce $\mathrm{E}(3)$-equivariance, but they …
View article: Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions
Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions Open
Rapid and accurate state of health (SOH) estimation of retired batteries is a crucial pretreatment for reuse and recycling. However, data-driven methods require exhaustive data curation under random SOH and state of charge (SOC) retirement…
View article: An adaptive autoregressive diffusion approach to design active humanized antibody and nanobody
An adaptive autoregressive diffusion approach to design active humanized antibody and nanobody Open
Humanization is a critical process for designing efficiently specific antibodies and nanobodies prior to clinical trials. Developing widely recognized deep learning techniques or frameworks for humanizing conventional antibodies and nanobo…
View article: Advancing Generalization Across a Variety of Abstract Visual Reasoning Tasks
Advancing Generalization Across a Variety of Abstract Visual Reasoning Tasks Open
The abstract visual reasoning (AVR) domain presents a diverse suite of analogy-based tasks devoted to studying model generalization. Recent years have brought dynamic progress in the field, particularly in i.i.d. scenarios, in which models…
View article: Development of a deep learning survival model to predict the individual risks of peritoneal metastasis of colorectal cancer
Development of a deep learning survival model to predict the individual risks of peritoneal metastasis of colorectal cancer Open
Background : Peritoneal metastasis (PM) has been considered to be the terminal stage of colorectal cancer (CRC) due to poor prognosis. We purposed to construct an AI model of clinicopathological parameters to predict the survival prognosis…
View article: A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications Open
Geometric graphs are a special kind of graph with geometric features, which are vital to model many scientific problems. Unlike generic graphs, geometric graphs often exhibit physical symmetries of translations, rotations, and reflections,…
View article: Fast and accurate modeling and design of antibody-antigen complex using tFold
Fast and accurate modeling and design of antibody-antigen complex using tFold Open
Accurate prediction of antibody-antigen complex structures holds significant potential for advancing biomedical research and the design of therapeutic antibodies. Currently, structure prediction for protein monomers has achieved considerab…
View article: Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale
Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale Open
The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial products. While these exceptional AIGC products are gaining in…
View article: SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases Open
Graph Neural Networks (GNNs) with equivariant properties have emerged as powerful tools for modeling complex dynamics of multi-object physical systems. However, their generalization ability is limited by the inadequate consideration of phy…
View article: Structure-Aware DropEdge Toward Deep Graph Convolutional Networks
Structure-Aware DropEdge Toward Deep Graph Convolutional Networks Open
It has been discovered that graph convolutional networks (GCNs) encounter a remarkable drop in performance when multiple layers are piled up. The main factor that accounts for why deep GCNs fail lies in oversmoothing, which isolates the ne…
View article: DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery – a Focus on Affinity Prediction Problems with Noise Annotations
DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery – a Focus on Affinity Prediction Problems with Noise Annotations Open
AI-aided drug discovery (AIDD) is gaining popularity due to its potential to make the search for new pharmaceuticals faster, less expensive, and more effective. Despite its extensive use in numerous fields (e.g., ADMET prediction, virtual …
View article: Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
Handling Missing Data via Max-Entropy Regularized Graph Autoencoder Open
Graph neural networks (GNNs) are popular weapons for modeling relational data. Existing GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation a burning issue. Until recently, many works notice that GNN…
View article: Human Mobility Modeling during the COVID-19 Pandemic via Deep Graph Diffusion Infomax
Human Mobility Modeling during the COVID-19 Pandemic via Deep Graph Diffusion Infomax Open
Non-Pharmaceutical Interventions (NPIs), such as social gathering restrictions, have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of people. To support policy-makers, multiple studies have first modelled…
View article: MDM: Molecular Diffusion Model for 3D Molecule Generation
MDM: Molecular Diffusion Model for 3D Molecule Generation Open
Molecule generation, especially generating 3D molecular geometries from scratch (i.e., 3D de novo generation), has become a fundamental task in drug design. Existing diffusion based 3D molecule generation methods could suffer from unsatisf…
View article: Towards Controllable Diffusion Models via Reward-Guided Exploration
Towards Controllable Diffusion Models via Reward-Guided Exploration Open
By formulating data samples' formation as a Markov denoising process, diffusion models achieve state-of-the-art performances in a collection of tasks. Recently, many variants of diffusion models have been proposed to enable controlled samp…
View article: Decision Support System for Chronic Diseases Based on Drug-Drug Interactions
Decision Support System for Chronic Diseases Based on Drug-Drug Interactions Open
Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-drug antagonism can lead to serious adverse effects or even d…
View article: Accelerated Discovery of Macrocyclic CDK2 Inhibitor QR-6401 by Generative Models and Structure-Based Drug Design
Accelerated Discovery of Macrocyclic CDK2 Inhibitor QR-6401 by Generative Models and Structure-Based Drug Design Open
Selective CDK2 inhibitors have the potential to provide effective therapeutics for CDK2-dependent cancers and for combating drug resistance due to high cyclin E1 (CCNE1) expression intrinsically or CCNE1 amplification induced by treatment …
View article: A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets
A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets Open
Structure-based generative chemistry aims to explore much bigger chemical space to design a ligand with high binding affinity to the target proteins; it is a critical step in de novo computer-aided drug discovery. Traditional in silico met…
View article: Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax
Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax Open
Non-Pharmaceutical Interventions (NPIs), such as social gathering restrictions, have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of people. To support policy-makers, multiple studies have first modeled …
View article: Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
Handling Missing Data via Max-Entropy Regularized Graph Autoencoder Open
Graph neural networks (GNNs) are popular weapons for modeling relational data. Existing GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation a burning issue. Until recently, many works notice that GNN…
View article: Towards Complete-View and High-Level Pose-based Gait Recognition
Towards Complete-View and High-Level Pose-based Gait Recognition Open
The model-based gait recognition methods usually adopt the pedestrian walking postures to identify human beings. However, existing methods did not explicitly resolve the large intra-class variance of human pose due to camera views changing…
View article: MDM: Molecular Diffusion Model for 3D Molecule Generation
MDM: Molecular Diffusion Model for 3D Molecule Generation Open
Molecule generation, especially generating 3D molecular geometries from scratch (i.e., 3D \textit{de novo} generation), has become a fundamental task in drug designs. Existing diffusion-based 3D molecule generation methods could suffer fro…