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MTGGF: A Metabolism Type-Aware graph generative model for Molecular Metabolite Prediction

INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2025年 1-13页

作者： Zhao, Peng-Cheng Wei, Xue-Xin Wang, Qiong Wang, Hao-Yang Du, Bing-Xue Li, Jia-Ning Zhu, Bei Yu, Hui Shi, Jian-Yu Northwestern Polytech Univ Sch Life Sci Xian 710072 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China

Metabolism in vivo turns small molecules (e.g., drugs) into metabolites (new molecules), which brings unexpected safety issues in drug development. However, it is costly to determine metabolites by biological assays. Recent computational methods provide new promising approaches by predicting possible metabolites. Rule-based methods utilize predefined reaction-derived rules to infer metabolites. However, they are powerless to new metabolic reaction patterns. In contrast, rule-free methods leverage sequence-to-sequence machine translation to generate metabolites. Nevertheless, they are insufficient to characterize molecule structures, and bear weak interpretability. To address these issues in rule-free methods, this manuscript proposes a novel metabolism type-aware graph generative framework (MTGGF) for molecular metabolite prediction. It contains a two-stage learning process, including a pre-training on a large general chemical reaction dataset, and a fine-tuning on three smaller type-specific metabolic reaction datasets. Its core, an elaborate graph-to-graph generative model, treats both atoms and bonds as bipartite vertices, and molecules as bipartite graphs, such that it can embed rich information of molecule structures and ensure the integrity of generated metabolite structures. The comparison with state-of-the-art methods demonstrates its superiority. Furthermore, the ablation study validates the contributions of its two graph encoding components and its reaction-type-specific fine-tuning models. More importantly, based on interactive attention between a molecule and its metabolites, the case studies on five approved drugs reveal that there exist crucial substructures specific to metabolism types. It is anticipated that this framework can boost the risk evaluation of drug metabolites. The codes are available at https://***/zpczaizheli/Metabolite.

关键词： Molecular metabolism graph generative model Pretraining Fine-tuning Attention

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A Data-Driven graph generative model for Temporal Interaction Networks 20

A Data-Driven Graph Generative Model for Temporal Interactio...

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26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Zhou, Dawei Zheng, Lecheng Han, Jiawei He, Jingrui Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9781450379984

Deep graph generative models have recently received a surge of attention due to its superiority of modeling realistic graphs in a variety of domains, including biology, chemistry, and social science. Despite the initial success, most, if not all, of the existing works are designed for static networks. Nonetheless, many realistic networks are intrinsically dynamic and presented as a collection of system logs (i.e., timestamped interactions/edges between entities), which pose a new research direction for us: how can we synthesize realistic dynamic networks by directly learning from the system logs? In addition, how can we ensure the generated graphs preserve both the structural and temporal characteristics of the real data? To address these challenges, we propose an end-to-end deep generative framework named TagGen. In particular, we start with a novel sampling strategy for jointly extracting structural and temporal context information from temporal networks. On top of that, TagGen parameterizes a bi-level self-attention mechanism together with a family of local operations to generate temporal random walks. At last, a discriminator gradually selects generated temporal random walks, that are plausible in the input data, and feeds them to an assembling module for generating temporal networks. The experimental results in seven real-world data sets across a variety of metrics demonstrate that (1) TagGen outperforms all baselines in the temporal interaction network generation problem, and (2) TagGen significantly boosts the performance of the prediction models in the tasks of anomaly detection and link prediction.

关键词： graph generative model Temporal Networks Transformer model

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Multi-objective de novo drug design with conditional graph generative model

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JOURNAL OF CHEMINFORMATICS 2018年第1期10卷 33-33页

作者： Li, Yibo Zhang, Liangren Liu, Zhenming Peking Univ Sch Pharmaceut Sci State Key Lab Nat & Biomimet Drugs Xueyuan Rd 38 Beijing 100191 Peoples R China

Recently, deep generative models have revealed itself as a promising way of performing de novo molecule design. However, previous research has focused mainly on generating SMILES strings instead of molecular graphs. Although available, current graph generative models are are often too general and computationally expensive. In this work, a new de novo molecular design framework is proposed based on a type of sequential graph generators that do not use atom level recurrent units. Compared with previous graph generative models, the proposed method is much more tuned for molecule generation and has been scaled up to cover significantly larger molecules in the ChEMBL database. It is shown that the graph-based model outperforms SMILES based models in a variety of metrics, especially in the rate of valid outputs. For the application of drug design tasks, conditional graph generative model is employed. This method offers highe flexibility and is suitable for generation based on multiple objectives. The results have demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold, compounds with specific drug-likeness and synthetic accessibility requirements, as well as dual inhibitors against JNK3 and GSK-3 beta.

关键词： Deep learning De novo drug design graph generative model

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Fast graph Generation via Spectral Diffusion

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第5期46卷 3496-3508页

作者： Luo, Tianze Mo, Zhanfeng Pan, Sinno Jialin Nanyang Technol Univ Singapore 639798 Singapore Chinese Univ Hong Kong Hong Kong Peoples R China

Generating graph-structured data is a challenging problem, which requires learning the underlying distribution of graphs. Various models such as graph VAE, graph GANs, and graph diffusion models have been proposed to generate meaningful and reliable graphs, among which the diffusion models have achieved state-of-the-art performance. In this paper, we argue that running full-rank diffusion SDEs on the whole graph adjacency matrix space hinders diffusion models from learning graph topology generation, and hence significantly deteriorates the quality of generated graph data. To address this limitation, we propose an efficient yet effective graph Spectral Diffusion model (GSDM), which is driven by low-rank diffusion SDEs on the graph spectrum space. Our spectral diffusion model is further proven to enjoy a substantially stronger theoretical guarantee than standard diffusion models. Extensive experiments across various datasets demonstrate that our proposed GSDM turns out to be the SOTA model, by exhibiting both significantly higher generation quality and much less computational consumption than the baselines.

关键词： graph diffusion graph generative model stochastic differential equations

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graph Polish: A Novel graph Generation Paradigm for Molecular Optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第5期34卷 2323-2337页

作者： Ji, Chaojie Zheng, Yijia Wang, Ruxin Cai, Yunpeng Wu, Hongyan Chinese Acad Sci Shenzhen Inst Adv Technol Joint Engn Res Ctr Hlth Big Data Intelligent Anal Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Shenzhen Inst Adv Technol Chinese Acad Sci Dept Comp Sci & Technol Shenzhen 518055 Peoples R China

Molecular optimization, which transforms a given input molecule X into another Y with desired properties, is essential in molecular drug discovery. The traditional approaches either suffer from sample-inefficient learning or ignore information that can be captured with the supervised learning of optimized molecule pairs. In this study, we present a novel molecular optimization paradigm, graph Polish. In this paradigm, with the guidance of the source and target molecule pairs of the desired properties, a heuristic optimization solution can be derived: given an input molecule, we first predict which atom can be viewed as the optimization center, and then the nearby regions are optimized around this center. We then propose an effective and efficient learning framework, Teacher and Student polish, to capture the dependencies in the optimization steps. A teacher component automatically identifies and annotates the optimization centers and the preservation, removal, and addition of some parts of the molecules;a student component learns these knowledges and applies them to a new molecule. The proposed paradigm can offer an intuitive interpretation for the molecular optimization result. Experiments with multiple optimization tasks are conducted on several benchmark datasets. The proposed approach achieves a significant advantage over the six state-of-the-art baseline methods. Also, extensive studies are conducted to validate the effectiveness, explainability, and time savings of the novel optimization paradigm.

关键词： Optimization Task analysis Junctions Drugs Compounds Supervised learning Benchmark testing graph generation graph generative model graph neural network molecular optimization

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Bringing Your Own View: graph Contrastive Learning without Prefabricated Data Augmentations 22

Bringing Your Own View: Graph Contrastive Learning without P...

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15th ACM International Conference on Web Search and Data Mining (WSDM)

作者： You, Yuning Chen, Tianlong Wang, Zhangyang Shen, Yang Texas A&M Univ College Stn TX 77843 USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781450391320

Self-supervision is recently surging at its new frontier of graph learning. It facilitates graph representations beneficial to downstream tasks;but its success could hinge on domain knowledge for handcraft or the often expensive trials and errors. Even its state-of-the-art representative, graph contrastive learning (graphCL), is not completely free of those needs as graphCL uses a prefabricated prior reflected by the ad-hoc manual selection of graph data augmentations. Our work aims at advancing graphCL by answering the following questions: How to represent the space of graph augmented views? What principle can be relied upon to learn a prior in that space? And what framework can be constructed to learn the prior in tandem with contrastive learning? Accordingly, we have extended the prefabricated discrete prior in the augmentation set, to a learnable continuous prior in the parameter space of graph generators, assuming that graph priors per se, similar to the concept of image manifolds, can be learned by data generation. Furthermore, to form contrastive views without collapsing to trivial solutions due to the prior learnability, we have leveraged both principles of information minimization (InfoMin) and information bottleneck (InfoBN) to regularize the learned priors. Eventually, contrastive learning, InfoMin, and InfoBN are incorporated organically into one framework of bi-level optimization. Our principled and automated approach has proven to be competitive against the state-of-the-art graph self-supervision methods, including graphCL, on benchmarks of small graphs;and shown even better generalizability on large-scale graphs, without resorting to human expertise or downstream validation. Our code is publicly released at https://***/Shen-Lab/graphCL_Automated.

关键词： graph contrastive learning graph generative model information minimization information bottleneck

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MoFlow: An Invertible Flow model for Generating Molecular graphs 20

MoFlow: An Invertible Flow Model for Generating Molecular Gr...

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26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Zang, Chengxi Wang, Fei Weill Cornell Med Dept Populat Hlth Sci New York NY 10021 USA

ISBN: (纸本)9781450379984

Generating molecular graphs with desired chemical properties driven by deep graph generative models provides a very promising way to accelerate drug discovery process. Such graph generative models usually consist of two steps: learning latent representations and generation of molecular graphs. However, to generate novel and chemically-valid molecular graphs from latent representations is very challenging because of the chemical constraints and combinatorial complexity of molecular graphs. In this paper, we propose MoFlow, a flow-based graph generative model to learn invertible mappings between molecular graphs and their latent representations. To generate molecular graphs, our MoFlow first generates bonds (edges) through a Glow based model, then generates atoms (nodes) given bonds by a novel graph conditional flow, and finally assembles them into a chemically valid molecular graph with a posthoc validity correction. Our MoFlow has merits including exact and tractable likelihood training, efficient one-pass embedding and generation, chemical validity guarantees, 100% reconstruction of training data, and good generalization ability. We validate our model by four tasks: molecular graph generation and reconstruction, visualization of the continuous latent space, property optimization, and constrained property optimization. Our MoFlow achieves state-of-the-art performance, which implies its potential efficiency and effectiveness to explore large chemical space for drug discovery.

关键词： graph generative model graph Normalizing Flow graph Conditional Flow Deep generative model De novo Drug Design Molecular graph Generation Molecular graph Optimization

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TG-GAN: Continuous-time Temporal graph Deep generative models with Time-Validity Constraints 21

TG-GAN: Continuous-time Temporal Graph Deep Generative Model...

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30th World Wide Web Conference (WWW)

作者： Zhang, Liming Zhao, Liang Qin, Shan Pfoser, Dieter Ling, Chen George Mason Univ Fairfax VA 22030 USA Emory Univ Atlanta GA 30322 USA Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781450383127

Deep generative models of graph-structured data have become popular in very recent years. Although initial research has focused on static graphs in applications such as molecular design and social networks, many challenges involve temporal graphs whose topology and attribute values evolve dynamically over time. Sophisticated and unknown network processes that affect temporal graphs cannot be captured adequately by prescribed models. Application areas include social mobility networks and catastrophic cybersecurity failures. These web-scale applications challenge current deep graph generative models with the need to capture 1) time-validity constraints, 2) time and topological distributions, and 3) joint time and graph encoding and decoding. Here, we propose the "Temporal graph generative Adversarial Network" (TG-GAN) for continuous-time graph generation with time-validity constraints(1). TG-GAN can jointly generate the time, node, and edge information for truncated temporal walks via a novel recurrent-based model and a valid time decoder. The generated truncated temporal walks are then assembled into time-budgeted temporal walks for temporal graphs under the learned topological and temporal dependencies. In addition, a discriminator is proposed to combine time and node encoding operations over a recurrent architecture to distinguish generated sequences from real ones sampled by a truncated temporal walk sampler. Extensive experiments on both synthetic and real-world datasets confirm that TG-GAN significantly outperforms five benchmarking methods in terms of efficiency and effectiveness.

关键词： graph generative model Temporal graph Temporal Walks

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graphGen: A Scalable Approach to Domain-agnostic Labeled graph Generation 20

GraphGen: A Scalable Approach to Domain-agnostic Labeled Gra...

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29th World Wide Web Conference (WWW)

作者： Goyal, Nikhil Jain, Harsh Vardhan Ranu, Sayan Indian Inst Technol Delhi India

ISBN: (纸本)9781450370233

graph generative models have been extensively studied in the data mining literature. While traditional techniques are based on generating structures that adhere to a pre-decided distribution, recent techniques have shifted towards learning this distribution directly from the data. While learning-based approaches have imparted significant improvement in quality, some limitations remain to be addressed. First, learning graph distributions introduces additional computational overhead, which limits their scalability to large graph databases. Second, many techniques only learn the structure and do not address the need to also learn node and edge labels, which encode important semantic information and influence the structure itself Third, existing techniques often incorporate domain-specific rules and lack generalizability. Fourth, the experimentation of existing techniques is not comprehensive enough due to either using weak evaluation metrics or focusing primarily on synthetic or small datasets. In this work, we develop a domain-agnostic technique called graphGEN to overcome all of these limitations. graphGEN converts graphs to sequences using minimum DFS codes. Minimum DFS codes are canonical labels and capture the graph structure precisely along with the label information. The complex joint distributions between structure and semantic labels are learned through a novel LSTM architecture. Extensive experiments on million-sized, real graph datasets show graphGEN to be 4 times faster on average than state-of-the-art techniques while being significantly better in quality across a comprehensive set of 11 different metrics. Our code is released at: https://***/idea-iitd/graphgen.

关键词： neural networks canonical labels graph generative model

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GF-VAE: A Flow-based Variational Autoencoder for Molecule Generation 21

GF-VAE: A Flow-based Variational Autoencoder for Molecule Ge...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Ma, Changsheng Zhang, Xiangliang King Abdullah Univ Sci & Technol Jeddah Saudi Arabia

ISBN: (纸本)9781450384469

Generating novel molecules with desired properties is a fundamental problem in modern drug discovery. This is a challenging problem because it requires the optimization of the given objectives while obeying the rules of chemical valence. An effective approach is to incorporate the molecular graph with deep generative models. However, recent generative models with high-performance are still computationally expensive. In this paper, we propose GF-VAE, a flow-based variational autoencoder (VAE) model for molecular graph generation. Specifically, the model equips VAE a lightweight flow model as its decoder, in which, the encoder aims to accelerate the training process of the decoder, while the decoder in turns to optimize the performance of the encoder. Thanks to the invertibility of flow model, the generation process is easily accomplished by reversing the decoder. Additionally, the final generated molecules are processed by validity correction. Therefore, our GF-VAE inherits the advantages of both VAE and flow-based methods. We validate our model on molecule generation and reconstruction, smoothness of learned latent space, property optimization and constrained property optimization. The results show that our model achieves state-of-the-arts performance on these tasks. Moreover, the time performance of GF-VAE on two classical datasets can achieve 31.3% and 62.9% improvements separately than the state-of-the-art model.

关键词： graph generative model Variational AutoEncoder Normalizing Flow Molecular Generation Molecular property optimization

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