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Jun Xia

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29 papers

2 author rows

AAAI Conference 2026 Conference Paper

Departures: Distributional Transport for Single-Cell Perturbation Prediction with Neural Schrödinger Bridges

Changxi Chi
Yufei Huang
Jun Xia
Jiangbin Zheng
Yunfan Liu
Zelin Zang
Stan Z. Li

Predicting single-cell perturbation outcomes directly advances gene function analysis and facilitates drug candidate selection, making it a key driver of both basic and translational biomedical research. However, a major bottleneck in this task is the unpaired nature of single-cell data, as the same cell cannot be observed both before and after perturbation due to the destructive nature of sequencing. Although some neural generative transport models attempt to tackle unpaired single-cell perturbation data, they either lack explicit conditioning or depend on prior spaces for indirect distribution alignment, limiting precise perturbation modeling. In this work, we approximate Schrödinger Bridge (SB), which defines stochastic dynamic mappings recovering the entropy-regularized optimal transport (OT), to directly align the distributions of control and perturbed single-cell populations across different perturbation conditions. Unlike prior SB approximations that rely on bidirectional modeling to infer optimal source-target sample coupling, we leverage Minibatch-OT based pairing to avoid such bidirectional inference and the associated ill-posedness of defining the reverse process. This pairing directly guides bridge learning, yielding a scalable approximation to the SB. We approximate two SB models, one modeling discrete gene activation states and the other continuous expression distributions. Joint training enables accurate perturbation modeling and captures single-cell heterogeneity. Experiments on public genetic and drug perturbation datasets show that our model effectively captures heterogeneous single-cell responses and achieves state-of-the-art performance.

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AAAI Conference 2026 Conference Paper

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control

Shaorong Chen
Jingbo Zhou
Jun Xia

The discovery of novel proteins relies on sensitive protein identification, for which de novo peptide sequencing (DNPS) from mass spectra is a crucial approach. While deep learning has advanced DNPS, existing models inadequately enforce the fundamental mass consistency constraint—that a predicted peptide's mass must match the experimental measured precursor mass. Previous DNPS methods often treat this critical information as a simple input feature or use it in post-processing, leading to numerous implausible predictions that do not adhere to this fundamental physical property. To address this limitation, we introduce DiffuNovo, a novel regressor-guided diffusion model for de novo peptide sequencing that provides explicit peptide-level mass control. Our approach integrates the mass constraint at two critical stages: during training, a novel peptide-level mass loss guides model optimization, while at inference, regressor-based guidance from gradient-based updates in the latent space steers the generation to compel the predicted peptide adheres to the mass constraint. Comprehensive evaluations on established benchmarks demonstrate that DiffuNovo surpasses state-of-the-art methods in DNPS accuracy. Additionally, as the first DNPS model to employ a diffusion model as its core backbone, DiffuNovo leverages the powerful controllability of diffusion architecture and achieves a significant reduction in mass error, thereby producing much more physically plausible peptides. These innovations represent a substantial advancement toward robust and broadly applicable DNPS. The source code is available in the supplementary material.

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EAAI Journal 2026 Journal Article

Solver-free artificial intelligence framework for stress-strain prediction and rapid field visualisation of fibre-reinforced polymer-confined recycled aggregate concrete

Temitope E. Dada
Guobin Gong
Jun Xia
Luigi Di Sarno

Accurately predicting the stress-strain behaviour of fibre-reinforced polymer (FRP)-confined recycled aggregate concrete (FRCRAC) remains challenging due to the complex mechanics introduced by recycled aggregate. This study presents a novel two-stage machine-learning (ML) framework with a mechanics-inspired visualisation module (MIVM) to predict and visualise the stress-strain behaviour of FRCRAC. First, Optuna-optimised Categorical Boosting (CATO) models are used to predict ultimate axial strength, axial strain, and hoop strain. These predictions are then integrated into Long Short-Term Memory (LSTMO) models to construct full axial and hoop stress-strain curves, forming a CATO-LSTMO framework. Trained via ten-fold cross-validation on a combination of 194 experimental and 600 synthetic datasets, CATO-LSTMO significantly outperforms conventional analytical and hybrid models with a coefficient of determination, R2, above 98 %. Secondly, a solver-free MIVM that replicates finite element (FE) behaviour is proposed to enhance the physical interpretation of ML models. Three dedicated Categorical Boosting regressors are trained on over 350, 000 nodal field outputs from Abaqus simulations to predict the three-dimensional stress, strain, and displacement distributions across 21 loading frames. These predictions are then scaled using the previously ML-generated stress-strain curves to reconstruct the frame-wise three-dimensional contour plots. The MIVM visualisations achieve results comparable to Abaqus outputs, while being about 500 times faster. This study contributes to artificial intelligence through novel hybrid ML frameworks and solver-free surrogate visualisation. The integrated CATO-LSTMO-MIVM framework is deployed as an interactive web application, and it offers practical use in engineering applications for rapidly estimating and visualising the stress-strain behaviour of FRCRAC.

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IJCAI Conference 2025 Conference Paper

A Comprehensive and Systematic Review for Deep Learning-Based De Novo Peptide Sequencing

Jun Xia
Jingbo Zhou
Shaorong Chen
Tianze Ling
Stan Z. Li

Tandem mass spectrometry (MS/MS) has revolutionized the field of proteomics, enabling the high-throughput identification of proteins. However, one of the central challenges in mass spectrometry-based proteomics remains peptide identification, especially in the absence of a comprehensive peptide database. While traditional database search methods compare observed mass spectra to pre-existing protein databases, they are limited by the availability and completeness of these databases. \emph{De novo} peptide sequencing, which derives peptide sequences directly from mass spectra, has emerged as a crucial approach in such cases. In recent years, deep learning has made significant strides in this domain. These methods train deep neural networks for translating mass spectra into peptide sequences without relying on any pre-constructed databases. Despite significant progress, this field still lacks a comprehensive and systematic review. In this paper, we provide the first review of deep learning-based \emph{de novo} peptide sequencing techniques from the perspectives of data types, model architectures, decoding strategies, applications and evaluation metrics. We also identify key challenges and highlight promising avenues for future research, providing a valuable resource for the AI and scientific communities.

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NeurIPS Conference 2025 Conference Paper

EDBench: Large-Scale Electron Density Data for Molecular Modeling

Hongxin Xiang
Ke Li
Mingquan Liu
Zhixiang Cheng
Bin Yao
Wenjie Du
Jun Xia
Li Zeng

Existing molecular machine learning force fields (MLFFs) generally focus on the learning of atoms, molecules, and simple quantum chemical properties (such as energy and force), but ignore the importance of electron density (ED) $\rho(r)$ in accurately understanding molecular force fields (MFFs). ED describes the probability of finding electrons at specific locations around atoms or molecules, which uniquely determines all ground state properties (such as energy, molecular structure, etc. ) of interactive multi-particle systems according to the Hohenberg-Kohn theorem. However, the calculation of ED relies on the time-consuming first-principles density functional theory (DFT), which leads to the lack of large-scale ED data and limits its application in MLFFs. In this paper, we introduce EDBench, a large-scale, high-quality dataset of ED designed to advance learning-based research at the electronic scale. Built upon the PCQM4Mv2, EDBench provides accurate ED data, covering 3. 3 million molecules. To comprehensively evaluate the ability of models to understand and utilize electronic information, we design a suite of ED-centric benchmark tasks spanning prediction, retrieval, and generation. Our evaluation of several state-of-the-art methods demonstrates that learning from EDBench is not only feasible but also achieves high accuracy. Moreover, we show that learning-based methods can efficiently calculate ED with comparable precision while significantly reducing the computational cost relative to traditional DFT calculations. All data and benchmarks from EDBench will be freely available, laying a robust foundation for ED-driven drug discovery and materials science.

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IJCAI Conference 2025 Conference Paper

Electron Density-enhanced Molecular Geometry Learning

Hongxin Xiang
Jun Xia
Xin Jin
Wenjie Du
Li Zeng
Xiangxiang Zeng

Electron density (ED), which describes the probability distribution of electrons in space, is crucial for accurately understanding the energy and force distribution in molecular force fields (MFF). Existing machine learning force fields (MLFF) focus on mining appropriate physical quantities from the atom-level conformation to enhance the molecular geometry representation while ignoring the unique information from microscopic electrons. In this work, we propose an efficient Electronic Density representation framework to enhance molecular Geometric learning (called EDG), which leverages images rendered from ED to boost molecular geometric representations in MLFF. Specifically, we construct a novel image-based ED representation, which consists of 2 million 6-view images with RGB-D channels, and design an ED representation learning model, called ImageED, to learn ED-related knowledge from these images. We further propose an efficient ED-aware teacher and introduce a cross-modal distillation strategy to transfer knowledge from the image-based teacher to the geometry-based students. Extensive experiments on QM9 and rMD17 demonstrate that EDG can be directly integrated into existing geometry-based models and significantly improves the capabilities of these models (e. g. , SchNet, EGNN, SphereNet, ViSNet) for geometry representation learning in MLFF with a maximum average performance increase of 33. 7%. Code and appendix are available at https: //github. com/HongxinXiang/EDG

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IJCAI Conference 2025 Conference Paper

GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype

Changxi Chi
Jun Xia
Jingbo Zhou
Jiabei Cheng
Chang Yu
Stan Z. Li

Predicting genetic perturbations enables the identification of potentially crucial genes prior to wet-lab experiments, significantly improving overall experimental efficiency. Since genes are the foundation of cellular life, building gene regulatory networks (GRN) is essential to understand and predict the effects of genetic perturbations. However, current methods fail to fully leverage gene-related information, and solely rely on simple evaluation metrics to construct coarse-grained GRN. More importantly, they ignore functional differences between biotypes, limiting the ability to capture potential gene interactions. In this work, we leverage pre-trained large language model and DNA sequence model to extract features from gene descriptions and DNA sequence data, respectively, which serve as the initialization for gene representations. Additionally, we introduce gene biotype information for the first time in genetic perturbation, simulating the distinct roles of genes with different biotypes in regulating cellular processes, while capturing implicit gene relationships through graph structure learning (GSL). We propose GRAPE, a heterogeneous graph neural network (HGNN) that leverages gene representations initialized with features from descriptions and sequences, models the distinct roles of genes with different biotypes, and dynamically refines the GRN through GSL. The results on publicly available datasets show that our method achieves state-of-the-art performance. The code for reproducing the results can be seen at the link: https: //github. com/ChangxiChi/GRAPE.

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IJCAI Conference 2025 Conference Paper

MTGIB-UNet: A Multi-Task Graph Information Bottleneck and Uncertainty Weighted Network for ADMET Prediction

Xuqiang Li
Wenjie Du
Jun Xia
Jianmin Wang
Xiaoqi Wang
Yang Yang
Yang Wang

Accurate prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties is crucial in drug development, as these properties directly impact a drug's efficacy and safety. However, existing multi-task learning models often face challenges related to noise interference and task conflicts when dealing with complex molecular structures. To address these issues, we propose a novel multi-task Graph Neural Network (GNN) model, \textbf{MTGIB-UNet}. The model begins by encoding molecular graphs to capture intricate molecular structure information. Subsequently, based on the Graph Information Bottleneck (GIB) principle, the model compresses the information flow by extracting subgraphs, retaining task-relevant features while removing noise for each task. These embeddings are then fused through a gated network that dynamically adjusts the contribution weights of auxiliary tasks to the primary task. Specifically, an uncertainty weighting (UW) strategy is applied, with additional emphasis placed on the primary task, allowing dynamic adjustment of task weights while strengthening the influence of the primary task on model training. Experiments on standard ADMET datasets demonstrate that our model outperforms existing methods. Additionally, the model shows good interpretability by identifying key molecular substructures related to specific ADMET endpoints.

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NeurIPS Conference 2025 Conference Paper

PRESCRIBE: Predicting Single-Cell Responses with Bayesian Estimation

Jiabei Cheng
Changxi Chi
Jingbo Zhou
Hongyi Xin
Jun Xia

In single-cell perturbation prediction, a central task is to forecast the effects of perturbing a gene unseen in the training data. The efficacy of such predictions depends on two factors: (1) the similarity of the target gene to those covered in the training data, which informs model (epistemic) uncertainty, and (2) the quality of the corresponding training data, which reflects data (aleatoric) uncertainty. Both factors are critical for determining the reliability of a prediction, particularly as gene perturbation is an inherently stochastic biochemical process. In this paper, we propose PRESCRIBE (PREdicting Single-Cell Response wIth Bayesian Estimation), a multivariate deep evidential regression framework designed to measure both sources of uncertainty jointly. Our analysis demonstrates that PRESCRIBE effectively estimates a confidence score for each prediction, which strongly correlates with its empirical accuracy. This capability enables the filtering of untrustworthy results, and in our experiments, it achieves steady accuracy improvements of over 3% compared to comparable baselines.

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NeurIPS Conference 2024 Conference Paper

AdaNovo: Towards Robust \emph{De Novo} Peptide Sequencing in Proteomics against Data Biases

Jun Xia
Shaorong Chen
Jingbo Zhou
Xiaojun Shan
Wenjie Du
Zhangyang Gao
Cheng Tan
Bozhen Hu

Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the high-throughput analysis of protein composition in biological tissues. Despite the development of several deep learning methods for predicting amino acid sequences (peptides) responsible for generating the observed mass spectra, training data biases hinder further advancements of \emph{de novo} peptide sequencing. Firstly, prior methods struggle to identify amino acids with Post-Translational Modifications (PTMs) due to their lower frequency in training data compared to canonical amino acids, further resulting in unsatisfactory peptide sequencing performance. Secondly, various noise and missing peaks in mass spectra reduce the reliability of training data (Peptide-Spectrum Matches, PSMs). To address these challenges, we propose AdaNovo, a novel and domain knowledge-inspired framework that calculates Conditional Mutual Information (CMI) between the mass spectra and amino acids or peptides, using CMI for robust training against above biases. Extensive experiments indicate that AdaNovo outperforms previous competitors on the widely-used 9-species benchmark, meanwhile yielding 3. 6\% - 9. 4\% improvements in PTMs identification. The supplements contain the code.

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IJCAI Conference 2024 Conference Paper

An Image-enhanced Molecular Graph Representation Learning Framework

Hongxin Xiang
Shuting Jin
Jun Xia
Man Zhou
Jianmin Wang
Li Zeng
Xiangxiang Zeng

Extracting rich molecular representation is a crucial prerequisite for accurate drug discovery. Recent molecular representation learning methods achieve impressive progress, but the paradigm of learning from a single modality gradually encounters the bottleneck of limited representation capabilities. In this work, we fully consider the rich visual information contained in 3D conformation molecular images (i. e. , texture, shadow, color and planar spatial information) and distill graph-based models for more discriminative drug discovery. Specifically, we propose an image-enhanced molecular graph representation learning framework that leverages multi-view molecular images rendered from 3D conformations to boost molecular graph representations. To extract useful auxiliary knowledge from multi-view images, we design a teacher, which is pre-trained on 2 million molecules with conformations through five meticulously designed pre-training tasks. To transfer knowledge from teacher to graph-based students, we pose an efficient cross-modal knowledge distillation strategy with knowledge enhancer and task enhancer. It is worth noting that the distillation architecture of IEM can be directly integrated into existing graph-based models, and significantly improves the capabilities of these models (e. g. GIN, EdgePred, GraphMVP, MoleBERT) for molecular representation learning. In particular, GraphMVP and MoleBERT equipped with IEM achieve new state-of-the-art performance on MoleculeNet benchmark, achieving average 73. 89% and 73. 81% ROC-AUC, respectively. Code is available at https: //github. com/HongxinXiang/IEM.

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IROS Conference 2024 Conference Paper

An Online Rcm Adjusting System for Robot-Assisted Retinal Surgeries

Jun Xia
Ting Wang 0028
Huanqi Ni
Yanlin Li 0006
Ruoxi Chen
M. Ali Nasseri
Haotian Lin 0001
Kai Huang 0001

In robot-assisted retinal surgery, a Remote Center of Motion (Rcm) allows the surgical instrument to rotate around a distal fixed point without any lateral translations. The Rcm point should be perfectly aligned inside the trocar. Otherwise, unexpected tool translations at the expected remote center will enlarge the force applied to the trocar and consequently result in post-operative complications. Due to the narrow size of the trocar and the lack of real-time detection equipment, the Rcm point is hard to be perfectly located inside the trocar. Even if the Rcm is perfectly aligned, the movement of the tissue around the eyeball could make it inappropriate again. In this paper, inspired by the control strategy of surgeons, an online Rcm adjusting strategy is proposed. Instead of only using one fixed Rcm point, to restrict the force between the surgical tool and the trocar, the proposed strategy adjusts the position of the Rcm point during the motion. The results show our approach significantly reduces the force between the robot end-effector and surgical port by 64. 2%. In addition, the results also demonstrate that our approach complies the Rcm trajectories without deforming or spoiling the working space, which is significantly important for obeying surgeon’s instructions in practice.

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AAAI Conference 2024 Conference Paper

Cross-Gate MLP with Protein Complex Invariant Embedding Is a One-Shot Antibody Designer

Cheng Tan
Zhangyang Gao
Lirong Wu
Jun Xia
Jiangbin Zheng
Xihong Yang
Yue Liu
Bozhen Hu

Antibodies are crucial proteins produced by the immune system in response to foreign substances or antigens. The specificity of an antibody is determined by its complementarity-determining regions (CDRs), which are located in the variable domains of the antibody chains and form the antigen-binding site. Previous studies have utilized complex techniques to generate CDRs, but they suffer from inadequate geometric modeling. Moreover, the common iterative refinement strategies lead to an inefficient inference. In this paper, we propose a simple yet effective model that can co-design 1D sequences and 3D structures of CDRs in a one-shot manner. To achieve this, we decouple the antibody CDR design problem into two stages: (i) geometric modeling of protein complex structures and (ii) sequence-structure co-learning. We develop a novel macromolecular structure invariant embedding, typically for protein complexes, that captures both intra- and inter-component interactions among the backbone atoms, including Calpha, N, C, and O atoms, to achieve comprehensive geometric modeling. Then, we introduce a simple cross-gate MLP for sequence-structure co-learning, allowing sequence and structure representations to implicitly refine each other. This enables our model to design desired sequences and structures in a one-shot manner. Extensive experiments are conducted to evaluate our results at both the sequence and structure level, which demonstrate that our model achieves superior performance compared to the state-of-the-art antibody CDR design methods.

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NeurIPS Conference 2024 Conference Paper

Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptative Residual Module

Jingbo Zhou
Yixuan Du
Ruqiong Zhang
Jun Xia
Zhizhi Yu
Zelin Zang
Di Jin
Carl Yang

Graph Neural Networks (GNNs), a type of neural network that can learn from graph-structured data through neighborhood information aggregation, have shown superior performance in various downstream tasks. However, as the number of layers increases, node representations becomes indistinguishable, which is known as over-smoothing. To address this issue, many residual methods have emerged. In this paper, we focus on the over-smoothing issue and related residual methods. Firstly, we revisit over-smoothing from the perspective of overlapping neighborhood subgraphs, and based on this, we explain how residual methods can alleviate over-smoothing by integrating multiple orders neighborhood subgraphs to avoid the indistinguishability of the single high-order neighborhood subgraphs. Additionally, we reveal the drawbacks of previous residual methods, such as the lack of node adaptability and severe loss of high-order neighborhood subgraph information, and propose a \textbf{Posterior-Sampling-based, Node-Adaptive Residual module (PSNR)}. We theoretically demonstrate that PSNR can alleviate the drawbacks of previous residual methods. Furthermore, extensive experiments verify the superiority of the PSNR module in fully observed node classification and missing feature scenarios. Our codeis available at \href{https: //github. com/jingbo02/PSNR-GNN}{https: //github. com/jingbo02/PSNR-GNN}.

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NeurIPS Conference 2024 Conference Paper

End-to-end Learnable Clustering for Intent Learning in Recommendation

Yue Liu
Shihao Zhu
Jun Xia
Yingwei Ma
Jian Ma
Xinwang Liu
Shengju Yu
Kejun Zhang

Intent learning, which aims to learn users' intents for user understanding and item recommendation, has become a hot research spot in recent years. However, existing methods suffer from complex and cumbersome alternating optimization, limiting performance and scalability. To this end, we propose a novel intent learning method termed \underline{ELCRec}, by unifying behavior representation learning into an \underline{E}nd-to-end \underline{L}earnable \underline{C}lustering framework, for effective and efficient \underline{Rec}ommendation. Concretely, we encode user behavior sequences and initialize the cluster centers (latent intents) as learnable neurons. Then, we design a novel learnable clustering module to separate different cluster centers, thus decoupling users' complex intents. Meanwhile, it guides the network to learn intents from behaviors by forcing behavior embeddings close to cluster centers. This allows simultaneous optimization of recommendation and clustering via mini-batch data. Moreover, we propose intent-assisted contrastive learning by using cluster centers as self-supervision signals, further enhancing mutual promotion. Both experimental results and theoretical analyses demonstrate the superiority of ELCRec from six perspectives. Compared to the runner-up, ELCRec improves NDCG@5 by 8. 9\% and reduces computational costs by 22. 5\% on the Beauty dataset. Furthermore, due to the scalability and universal applicability, we deploy this method on the industrial recommendation system with 130 million page views and achieve promising results. The codes are available on GitHub\footnote{https: //github. com/yueliu1999/ELCRec}. A collection (papers, codes, datasets) of deep group recommendation/intent learning methods is available on GitHub\footnote{https: //github. com/yueliu1999/Awesome-Deep-Group-Recommendation}.

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NeurIPS Conference 2024 Conference Paper

FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning

Sizhe Liu
Jun Xia
Lecheng Zhang
Yuchen Liu
Yue Liu
Wenjie Du
Zhangyang Gao
Bozhen Hu

Molecular relational learning (MRL) is crucial for understanding the interaction behaviors between molecular pairs, a critical aspect of drug discovery and development. However, the large feasible model space of MRL poses significant challenges to benchmarking, and existing MRL frameworks face limitations in flexibility and scope. To address these challenges, avoid repetitive coding efforts, and ensure fair comparison of models, we introduce FlexMol, a comprehensive toolkit designed to facilitate the construction and evaluation of diverse model architectures across various datasets and performance metrics. FlexMol offers a robust suite of preset model components, including 16 drug encoders, 13 protein sequence encoders, 9 protein structure encoders, and 7 interaction layers. With its easy-to-use API and flexibility, FlexMol supports the dynamic construction of over 70, 000 distinct combinations of model architectures. Additionally, we provide detailed benchmark results and code examples to demonstrate FlexMol’s effectiveness in simplifying and standardizing MRL model development and comparison. FlexMol is open-sourced and available at https: //github. com/Steven51516/FlexMol.

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NeurIPS Conference 2024 Conference Paper

Learning Complete Protein Representation by Dynamically Coupling of Sequence and Structure

Bozhen Hu
Cheng Tan
Jun Xia
Yue Liu
Lirong Wu
Jiangbin Zheng
Yongjie Xu
Yufei Huang

Learning effective representations is imperative for comprehending proteins and deciphering their biological functions. Recent strides in language models and graph neural networks have empowered protein models to harness primary or tertiary structure information for representation learning. Nevertheless, the absence of practical methodologies to appropriately model intricate inter-dependencies between protein sequences and structures has resulted in embeddings that exhibit low performance on tasks such as protein function prediction. In this study, we introduce CoupleNet, a novel framework designed to interlink protein sequences and structures to derive informative protein representations. CoupleNet integrates multiple levels and scales of features in proteins, encompassing residue identities and positions for sequences, as well as geometric representations for tertiary structures from both local and global perspectives. A two-type dynamic graph is constructed to capture adjacent and distant sequential features and structural geometries, achieving completeness at the amino acid and backbone levels. Additionally, convolutions are executed on nodes and edges simultaneously to generate comprehensive protein embeddings. Experimental results on benchmark datasets showcase that CoupleNet outperforms state-of-the-art methods, exhibiting particularly superior performance in low-sequence similarities scenarios, adeptly identifying infrequently encountered functions and effectively capturing remote homology relationships in proteins.

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IJCAI Conference 2024 Conference Paper

MMGNN: A Molecular Merged Graph Neural Network for Explainable Solvation Free Energy Prediction

Wenjie Du
Shuai Zhang
Di Wu
Jun Xia
Ziyuan Zhao
Junfeng Fang
Yang Wang

In this paper, we address the challenge of accurately modeling and predicting Gibbs free energy in solute-solvent interactions, a pivotal yet complex aspect in the field of chemical modeling. Traditional approaches, primarily relying on deep learning models, face limitations in capturing the intricate dynamics of these interactions. To overcome these constraints, we introduce a novel framework, molecular modeling graph neural network (MMGNN), which more closely mirrors real-world chemical processes. Specifically, MMGNN explicitly models atomic interactions such as hydrogen bonds by initially forming indiscriminate connections between intermolecular atoms, which are then refined using an attention-based aggregation method, tailoring to specific solute-solvent pairs. To address the challenges of non-interactive or repulsive atomic interactions, MMGNN incorporates interpreters for nodes and edges in the merged graph, enhancing explainability and reducing redundancy. MMGNN stands as the first framework to explicitly align with real chemical processes, providing a more accurate and scientifically sound approach to modeling solute-solvent interactions. The infusion of explainability allows for the extraction of key subgraphs, which are pivotal for further research in solute-solvent dynamics. Extensive experimental validation confirms the efficacy and enhanced explainability of MMGNN.

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NeurIPS Conference 2024 Conference Paper

NovoBench: Benchmarking Deep Learning-based \emph{De Novo} Sequencing Methods in Proteomics

Jingbo Zhou
Shaorong Chen
Jun Xia
Sizhe Sizhe Liu
Tianze Ling
Wenjie Du
Yue Liu
Jianwei Yin

Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the analysis of protein composition in biological tissues. Many deep learning methods have been developed for \emph{de novo} peptide sequencing task, i. e. , predicting the peptide sequence for the observed mass spectrum. However, two key challenges seriously hinder the further research of this important task. Firstly, since there is no consensus for the evaluation datasets, the empirical results in different research papers are often not comparable, leading to unfair comparison. Secondly, the current methods are usually limited to amino acid-level or peptide-level precision and recall metrics. In this work, we present the first unified benchmark NovoBench for \emph{de novo} peptide sequencing, which comprises diverse mass spectrum data, integrated models, and comprehensive evaluation metrics. Recent impressive methods, including DeepNovo, PointNovo, Casanovo, InstaNovo, AdaNovo and $\pi$-HelixNovo are integrated into our framework. In addition to amino acid-level and peptide-level precision and recall, we also evaluate the models' performance in terms of identifying post-tranlational modifications (PTMs), efficiency and robustness to peptide length, noise peaks and missing fragment ratio, which are important influencing factors while seldom be considered. Leveraging this benchmark, we conduct a large-scale study of current methods, report many insightful findings that open up new possibilities for future development. The benchmark is open-sourced to facilitate future research and application. The code is available at \url{https: //github. com/Westlake-OmicsAI/NovoBench}.

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NeurIPS Conference 2024 Conference Paper

ProtGO: Function-Guided Protein Modeling for Unified Representation Learning

Bozhen Hu
Cheng Tan
Yongjie Xu
Zhangyang Gao
Jun Xia
Lirong Wu
Stan Z. Li

Protein representation learning is indispensable for various downstream applications of artificial intelligence for bio-medicine research, such as drug design and function prediction. However, achieving effective representation learning for proteins poses challenges due to the diversity of data modalities involved, including sequence, structure, and function annotations. Despite the impressive capabilities of large language models in biomedical text modelling, there remains a pressing need for a framework that seamlessly integrates these diverse modalities, particularly focusing on the three critical aspects of protein information: sequence, structure, and function. Moreover, addressing the inherent data scale differences among these modalities is essential. To tackle these challenges, we introduce ProtGO, a unified model that harnesses a teacher network equipped with a customized graph neural network (GNN) and a Gene Ontology (GO) encoder to learn hybrid embeddings. Notably, our approach eliminates the need for additional functions as input for the student network, which shares the same GNN module. Importantly, we utilize a domain adaptation method to facilitate distribution approximation for guiding the training of the teacher-student framework. This approach leverages distributions learned from latent representations to avoid the alignment of individual samples. Benchmark experiments highlight that ProtGO significantly outperforms state-of-the-art baselines, clearly demonstrating the advantages of the proposed unified framework.

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NeurIPS Conference 2024 Conference Paper

WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks

Jun Xia
Zhihao Yue
Yingbo Zhou
Zhiwei Ling
Yiyu Shi
Xian Wei
Mingsong Chen

Due to the increasing popularity of Artificial Intelligence (AI), more and more backdoor attacks are designed to mislead Deep Neural Network (DNN) predictions by manipulating training samples or processes. Although backdoor attacks have been investigated in various scenarios, they still suffer from the problems of both low fidelity of poisoned samples and non-negligible transfer in latent space, which make them easily identified by existing backdoor detection algorithms. To overcome this weakness, this paper proposes a novel frequency-based backdoor attack method named WaveAttack, which obtains high-frequency image features through Discrete Wavelet Transform (DWT) to generate highly stealthy backdoor triggers. By introducing an asymmetric frequency obfuscation method, our approach adds an adaptive residual to the training and inference stages to improve the impact of triggers, thus further enhancing the effectiveness of WaveAttack. Comprehensive experimental results show that, WaveAttack can not only achieve higher effectiveness than state-of-the-art backdoor attack methods, but also outperform them in the fidelity of images (i. e. , by up to 28. 27\% improvement in PSNR, 1. 61\% improvement in SSIM, and 70. 59\% reduction in IS). Our code is available at https: //github. com/BililiCode/WaveAttack.

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IJCAI Conference 2023 Conference Paper

A Systematic Survey of Chemical Pre-trained Models

Jun Xia
Yanqiao Zhu
Yuanqi Du
Stan Z. Li

Deep learning has achieved remarkable success in learning representations for molecules, which is crucial for various biochemical applications, ranging from property prediction to drug design. However, training Deep Neural Networks (DNNs) from scratch often requires abundant labeled molecules, which are expensive to acquire in the real world. To alleviate this issue, tremendous efforts have been devoted to Chemical Pre-trained Models (CPMs), where DNNs are pre-trained using large-scale unlabeled molecular databases and then fine-tuned over specific downstream tasks. Despite the prosperity, there lacks a systematic review of this fast-growing field. In this paper, we present the first survey that summarizes the current progress of CPMs. We first highlight the limitations of training molecular representation models from scratch to motivate CPM studies. Next, we systematically review recent advances on this topic from several key perspectives, including molecular descriptors, encoder architectures, pre-training strategies, and applications. We also highlight the challenges and promising avenues for future research, providing a useful resource for both machine learning and scientific communities.

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JBHI Journal 2023 Journal Article

HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis

Xiaodan Xing
Javier Del Ser
Yinzhe Wu
Yang Li
Jun Xia
Lei Xu
David Firmin
Peter Gatehouse

Synthetic digital twins based on medical data accelerate the acquisition, labelling and decision making procedure in digital healthcare. A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing them in reality. Unfortunately, algorithms for cardiac data synthesis have been so far scarcely studied in the literature. An important imaging modality in the cardiac examination is three-directional CINE multi-slice myocardial velocity mapping (3Dir MVM), which provides a quantitative assessment of cardiac motion in three orthogonal directions of the left ventricle. The long acquisition time and complex acquisition produce make it more urgent to produce synthetic digital twins of this imaging modality. In this study, we propose a hybrid deep learning (HDL) network, especially for synthetic 3Dir MVM data. Our algorithm is featured by a hybrid UNet and a Generative Adversarial Network with a foreground-background generation scheme. The experimental results show that from temporally down-sampled magnitude CINE images (six times), our proposed algorithm can still successfully synthesise high temporal resolution 3Dir MVM CMR data (PSNR=42. 32) with precise left ventricle segmentation (DICE=0. 92). These performance scores indicate that our proposed HDL algorithm can be implemented in real-world digital twins for myocardial velocity mapping data simulation. To the best of our knowledge, this work is the first one investigating digital twins of the 3Dir MVM CMR, which has shown great potential for improving the efficiency of clinical studies via synthesised cardiac data.

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YNICL Journal 2023 Journal Article

Morphometric similarity network alterations in COVID-19 survivors correlate with behavioral features and transcriptional signatures

Jia Long
Jiao Li
Bing Xie
Zhuomin Jiao
Guoqiang Shen
Wei Liao
Xiaomin Song
Hongbo Le

OBJECTIVES: To explore the differences in the cortical morphometric similarity network (MSN) between COVID-19 survivors and healthy controls, and the correlation between these differences and behavioralfeatures and transcriptional signatures. MATERIALS & METHODS: 39 COVID-19 survivors and 39 age-, sex- and education years-matched healthy controls (HCs) were included. All participants underwent MRI and behavioral assessments (PCL-17, GAD-7, PHQ-9). MSN analysis was used to compute COVID-19 survivors vs. HCs differences across brain regions. Correlation analysis was used to determine the associations between regional MSN differences and behavioral assessments, and determine the spatial similarities between regional MSN differences and risk genes transcriptional activity. RESULTS: COVID-19 survivors exhibited decreased regional MSN in insula, precuneus, transverse temporal, entorhinal, para-hippocampal, rostral middle frontal and supramarginal cortices, and increased regional MSN in pars triangularis, lateral orbitofrontal, superior frontal, superior parietal, postcentral, and inferior temporal cortices. Regional MSN value of lateral orbitofrontal cortex was positively associated with GAD-7 and PHQ-9 scores, and rostral middle frontal was negatively related to PHQ-9 scores. The analysis of spatial similarities showed that seven risk genes (MFGE8, MOB2, NUP62, PMPCA, SDSL, TMEM178B, and ZBTB11) were related to regional MSN values. CONCLUSION: The MSN differences were associated with behavioral and transcriptional signatures, early psychological counseling or intervention may be required to COVID-19 survivors. Our study provided a new insight into understanding the altered coordination of structure in COVID-19 and may offer a new endophenotype to further investigate the brain substrate.

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NeurIPS Conference 2023 Conference Paper

Understanding the Limitations of Deep Models for Molecular property prediction: Insights and Solutions

Jun Xia
Lecheng Zhang
Xiao Zhu
Yue Liu
Zhangyang Gao
Bozhen Hu
Cheng Tan
Jiangbin Zheng

Molecular Property Prediction (MPP) is a crucial task in the AI-driven Drug Discovery (AIDD) pipeline, which has recently gained considerable attention thanks to advancements in deep learning. However, recent research has revealed that deep models struggle to beat traditional non-deep ones on MPP. In this study, we benchmark 12 representative models (3 non-deep models and 9 deep models) on 15 molecule datasets. Through the most comprehensive study to date, we make the following key observations: \textbf{(\romannumeral 1)} Deep models are generally unable to outperform non-deep ones; \textbf{(\romannumeral 2)} The failure of deep models on MPP cannot be solely attributed to the small size of molecular datasets; \textbf{(\romannumeral 3)} In particular, some traditional models including XGB and RF that use molecular fingerprints as inputs tend to perform better than other competitors. Furthermore, we conduct extensive empirical investigations into the unique patterns of molecule data and inductive biases of various models underlying these phenomena. These findings stimulate us to develop a simple-yet-effective feature mapping method for molecule data prior to feeding them into deep models. Empirically, deep models equipped with this mapping method can beat non-deep ones in most MoleculeNet datasets. Notably, the effectiveness is further corroborated by extensive experiments on cutting-edge dataset related to COVID-19 and activity cliff datasets.

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IJCAI Conference 2022 Conference Paper

Eliminating Backdoor Triggers for Deep Neural Networks Using Attention Relation Graph Distillation

Jun Xia
Ting Wang
Jiepin Ding
Xian Wei
Mingsong Chen

Due to the prosperity of Artificial Intelligence (AI) techniques, more and more backdoors are designed by adversaries to attack Deep Neural Networks (DNNs). Although the state-of-the-art method Neural Attention Distillation (NAD) can effectively erase backdoor triggers from DNNs, it still suffers from non-negligible Attack Success Rate (ASR) together with lowered classification ACCuracy (ACC), since NAD focuses on backdoor defense using attention features (i. e. , attention maps) of the same order. In this paper, we introduce a novel backdoor defense framework named Attention Relation Graph Distillation (ARGD), which fully explores the correlation among attention features with different orders using our proposed Attention Relation Graphs (ARGs). Based on the alignment of ARGs between teacher and student models during knowledge distillation, ARGD can more effectively eradicate backdoors than NAD. Comprehensive experimental results show that, against six latest backdoor attacks, ARGD outperforms NAD by up to 94. 85% reduction in ASR, while ACC can be improved by up to 3. 23%.

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ICRA Conference 2020 Conference Paper

Microscope-Guided Autonomous Clear Corneal Incision

Jun Xia
Sean J. Bergunder
Duoru Lin
Ying Yan
Shengzhi Lin
M. Ali Nasseri
Mingchuan Zhou
Haotian Lin 0001

Clear Corneal Incision, a challenging step in cataract surgery, and important to the overall quality of the surgery. New surgeons usually spend one full year trying to perfect their incision, but even after such rigorous training deficient incisions can still occur. This paper proposes an autonomous robotic system for this self-sealing incision. A conventional ophthalmic microscope system with a monocular camera is utilized to capture the surgical scene, ascertain the robot's position, and estimate depth information. Kinematics with a remote centre of motion (RCM) is designed for a multi-axes robot to perform the incision route. The experimental results on ex-vivo porcine eyes show the autonomous Clear Corneal Incision has a stricter three-plane structure than a surgeon-made incision, which is closer to the ideal incision.

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YNIMG Journal 2013 Journal Article

Noninvasive photoacoustic computed tomography of mouse brain metabolism in vivo

Junjie Yao
Jun Xia
Konstantin I. Maslov
Mohammadreza Nasiriavanaki
Vassiliy Tsytsarev
Alexei V. Demchenko
Lihong V. Wang

We have demonstrated the feasibility of imaging mouse brain metabolism using photoacoustic computed tomography (PACT), a fast, noninvasive and functional imaging modality with optical contrast and acoustic resolution. Brain responses to forepaw stimulations were imaged transdermally and transcranially. 2-NBDG, which diffuses well across the blood–brain-barrier, provided exogenous contrast for photoacoustic imaging of glucose response. Concurrently, hemoglobin provided endogenous contrast for photoacoustic imaging of hemodynamic response. Glucose and hemodynamic responses were quantitatively decoupled by using two-wavelength measurements. We found that glucose uptake and blood perfusion around the somatosensory region of the contralateral hemisphere were both increased by stimulations, indicating elevated neuron activity. While the glucose response area was more homogenous and confined within the somatosensory region, the hemodynamic response area had a clear vascular pattern and spread wider than the somatosensory region. Our results demonstrate that 2-NBDG-enhanced PACT is a promising tool for noninvasive studies of brain metabolism.

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ICRA Conference 2001 Conference Paper

An Exact Representation of Effective Cutting Shapes of 5-axis CNC Machining Using Rational Bezier and B-spline Tool Motions

Jun Xia
Qiaode Jeffrey Ge

Presents an approach to 5-axis CNC tool path generation for sculptured surface machining with a flat-end cutter. Rational Bezier and B-spline motions are used to plan cutter motions so that an exact representation of the effective cutting shape can be obtained. The exact representation leads to an accurate computation of the scallop curve generated by two adjacent tool paths. Two examples are given to show how this result can be used to accurately plan and verify tool paths for 5-axis CNC milling of sculptured surfaces.

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