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ScaffoldGVAE: scaffold generation and hopping of drug molecules via a variational autoencoder based on multi-view graph neural networks

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JOURNAL OF CHEMINFORMATICS 2023年第1期15卷 91-91页

作者： Hu, Chao Li, Song Yang, Chenxing Chen, Jun Xiong, Yi Fan, Guisheng Liu, Hao Hong, Liang Shanghai Jiao Tong Univ Sch Phys & Astron Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Inst Nat Sci Shanghai 200240 Peoples R China Shanghai Matwings Technol Co Ltd Shanghai 200240 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Sch Life Sci & Biotechnol Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Zhangjiang Inst Adv Study Shanghai 201203 Peoples R China

In recent years, drug design has been revolutionized by the application of deep learning techniques, and molecule generation is a crucial aspect of this transformation. However, most of the current deep learning approaches do not explicitly consider and apply scaffold hopping strategy when performing molecular generation. In this work, we propose ScaffoldGVAE, a variational autoencoder based on multi-view graph neural networks, for scaffold generation and scaffold hopping of drug molecules. The model integrates several important components, such as node-central and edge-central message passing, side-chain embedding, and Gaussian mixture distribution of scaffolds. To assess the efficacy of our model, we conduct a comprehensive evaluation and comparison with baseline models based on seven general generative model evaluation metrics and four scaffold hopping generative model evaluation metrics. The results demonstrate that ScaffoldGVAE can explore the unseen chemical space and generate novel molecules distinct from known compounds. Especially, the scaffold hopped molecules generated by our model are validated by the evaluation of GraphDTA, LeDock, and MM/GBSA. The case study of generating inhibitors of LRRK2 for the treatment of PD further demonstrates the effectiveness of ScaffoldGVAE in generating novel compounds through scaffold hopping. This novel approach can also be applied to other protein targets of various diseases, thereby contributing to the future development of new drugs. Source codes and data are available at https://***/ecust-hc/ScaffoldGVAE.

关键词： Drug design Molecule generation Scaffold hopping variational autoencoder Multi-view graph neural networks

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Metallogenic-Factor variational autoencoder for Geochemical Anomaly Detection by Ad-Hoc and Post-Hoc Interpretability Algorithms

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NATURAL RESOURCES RESEARCH 2023年第3期32卷 835-853页

作者： Luo, Zijing Zuo, Renguang Xiong, Yihui Zhou, Bao China Univ Geosci State Key Lab Geol Proc & Mineral Resources Wuhan 430074 Peoples R China Hubei Geol Survey Wuhan 430034 Peoples R China Hubei Geol Explorat Engn Technol Res Ctr Wuhan 430034 Peoples R China

Deep learning algorithms (DLAs) are becoming hot tools in processing geochemical survey data for mineral exploration. However, it is difficult to understand their working mechanisms and decision-making behaviors, which may lead to unreliable results. The construction of a reliable and interpretable DLA has become a focus in data-driven geoscience discovery. This study utilized a SHapley Additive exPlanations (SHAP) framework, a popular post-hoc interpretability analysis method, incorporated with the variational autoencoder (VAE) to explore the contribution of geochemical elements for multivariate geochemical anomaly recognition. The sorting of element importance obtained by SHAP tool can provide a novel view for selecting a suitable elemental association related to mineralization. Based on the metallogenic model in the southeastern Hubei Province of China, a metallogenic-factor-based VAE model was constructed using an ad-hoc interpretable modeling technique. The interpretability of the model in identifying the abnormal distribution of the element associations can be improved by constructing a hidden layer and loss function containing metallogenic regularity and key metallogenic factors. The highly anomalous areas identified by the metallogenic-factor VAE model not only contain most of the known Au deposits, but also can reasonably identify the abnormal elemental associations related to ore-forming processes under the guidance of the metallogenic regularity. According to the output visualization of the new hidden layer, and the results of receiver operating characteristic curve and success-rate curve, the metallogenic-factor VAE model exhibits satisfied interpretability and performance. The geochemical anomalies identified in this study provide critical clues for future mineral exploration.

关键词： Geochemical anomalies variational autoencoder SHapley Additive exPlanations Interpretable deep learning Metallogenic regularity Metallogenic factor

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An attempt to construct the individual model of daily facial skin temperature using variational autoencoder

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ARTIFICIAL LIFE AND ROBOTICS 2021年第4期26卷 488-493页

作者： Masaki, Ayaka Nagumo, Kent Iwashita, Yuki Oiwa, Kosuke Nozawa, Akio Aoyama Gakuin Univ Tokyo Japan

Facial skin temperature (FST) has also gained prominence as an indicator for detecting anomalies such as fever due to the COVID-19. When FST is used for engineering applications, it is enough to be able to recognize normal. We are also focusing on research to detect some anomaly in FST. In a previous study, it was confirmed that abnormal and normal conditions could be separated based on FST by using a variational autoencoder (VAE), a deep generative model. However, the simulations so far have been a far cry from reality. In this study, normal FST with a diurnal variation component was defined as a normal state, and a model of normal FST in daily life was individually reconstructed using VAE. Using the constructed model, the anomaly detection performance was evaluated by applying the Hotelling theory. As a result, the area under the curve (AUC) value in ROC analysis was confirmed to be 0.89 to 1.00 in two subjects.

关键词： Facial skin temperature Infrared thermography Deep learning Anomaly detection variational autoencoder Hotelling's theory

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Asset Pricing via the Conditional Quantile variational autoencoder

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JOURNAL OF BUSINESS & ECONOMIC STATISTICS 2024年第2期42卷 681-694页

作者： Yang, Xuanling Zhu, Zhoufan Li, Dong Zhu, Ke Tsinghua Univ Ctr Stat Sci Dept Ind Engn Beijing Peoples R China Shanghai Univ Finance & Econ Sch Stat & Management Shanghai Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Hong Kong Peoples R China

We propose a new asset pricing model that is applicable to the big panel of return data. The main idea of this model is to learn the conditional distribution of the return, which is approximated by a step distribution function constructed from conditional quantiles of the return. To study conditional quantiles of the return, we propose a new conditional quantile variational autoencoder (CQVAE) network. The CQVAE network specifies a factor structure for conditional quantiles with latent factors learned from a VAE network and nonlinear factor loadings learned from a "multi-head" network. Under the CQVAE network, we allow the observed covariates such as asset characteristics to guide the structure of latent factors and factor loadings. Furthermore, we provide a two-step estimation procedure for the CQVAE network. Using the learned conditional distribution of return from the CQVAE network, we propose our asset pricing model from the mean of this distribution, and additionally, we use both the mean and variance of this distribution to select portfolios. Finally, we apply our CQVAE asset pricing model to analyze a large 60-year US equity return dataset. Compared with the benchmark conditional autoencoder model, the CQVAE model not only delivers much larger values of out-of-sample total and predictive R-2's, but also earns at least 30.9% higher values of Sharpe ratios for both long-short and long-only portfolios.

关键词： Big data Conditional asset pricing model Dynamic loadings Machine learning Neural networks Nonlinear quantile factor model variational autoencoder

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Anomaly detection in facial skin temperature using variational autoencoder

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ARTIFICIAL LIFE AND ROBOTICS 2021年第1期26卷 122-128页

作者： Masaki, Ayaka Nagumo, Kent Lamsal, Bikash Oiwa, Kosuke Nozawa, Akio Aoyama Gakuin Univ Sagamihara Kanagawa Japan Kajima Corp Chofu Tokyo Japan

Facial skin temperature is a physiological index that varies with skin blood flow controlled by autonomic nervous system activity. The facial skin temperature can be remotely measured using infrared thermography, and it has recently attracted attention as a remote biomarker. For example, studies have been reported to estimate human emotions, drowsiness, and mental stress on facial skin temperature. However, it is impossible to make a machine that can discriminate all infinite physiological and psychological states. Considering the practicality of skin temperature, a machine that can determine the normal state of facial skin temperature may be sufficient. In this study, we propose a completely new approach to incorporate the concept of anomaly detection into the analysis of physiological and psychological states by facial skin temperature. In this paper, the method for separating normal and anomaly facial thermal images using an anomaly detection model was investigated to evaluate the applicability of variational autoencoder (VAE) to facial thermal images.

关键词： Facial skin temperature Thermal face image Infrared thermography Deep learning Anomaly detection variational autoencoder

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variational autoencoder based receiver for orthogonal time frequency space modulation

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DIGITAL SIGNAL PROCESSING 2021年 117卷 103170-103170页

作者： Li, Qiao Xiang, Zheng Ren, Peng Li, Wanlu Xidian Univ Sch Telecommun Engn Xian 710071 Shaanxi Peoples R China

The emerging orthogonal time frequency space (OTFS) modulation is demonstrated to offer reliable communication performance advantages over orthogonal frequency division multiplexing (OFDM) in doubly-dispersive fading channel. However, the existing embedded pilot-aided method to estimate channel impulse response (CIR) requires enormous spectral overhead to avoid the contamination of pilot symbols. In this paper, we present a variational autoencoder (VAE) based receiver for OTFS modulation that achieve a joint estimation and detection without pilot in delay-Doppler (DD) domain. The variational approach is considered to simplify the problem, and evidence lower bound (ELBO) is derived as loss function. In encoder step, an approximate posterior probability is introduced and utilized to minimize the Kullback-Leibler (KL) distance. Then we estimate CIR in decoder step and maximize the ELBO at last. From our simulation results, the proposed VAE based receiver for OTFS modulation enjoys a promising performance with other methods. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Orthogonal time frequency space modulation variational autoencoder Delay-Doppler domain Doubly dispersion

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Adversarial variational autoencoder for Top-N Recommender Systems 9

Adversarial Variational Autoencoder for Top-N Recommender Sy...

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9th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Zhang, Guijuan Liu, Yang Jin, Xiaoning Beijing Univ Technol Beijing Adv Innovat Ctr Future Internet Technol Beijing Peoples R China

ISBN: (纸本)9781538665657

Recommender systems play an important role in the age of mass information. They allow users to discover items that match their tastes. In this paper, we propose a novel method, called adversarial variational autoencoder, for top-N recommendation. We use generative adversarial networks to regularize variational autoencoder by imposing an arbitrary prior on the latent representation of VAE, which makes the recommendation model. We define a joint objective function as a minimization problem. Our experiments on three datasets show that the proposed model achieves high recommendation accuracy compared to other state-of-the-art models.

关键词： variational autoencoder generative adversarial networks recommender system collaborative filtering

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Interpreting rate-distortion of variational autoencoder and using model uncertainty for anomaly detection

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2022年第7-9期90卷 735-752页

作者： Park, Seonho Adosoglou, George Pardalos, Panos M. Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA

Building a scalable machine learning system for unsupervised anomaly detection via representation learning is highly desirable. One of the prevalent methods is using a reconstruction error of variational autoencoder (VAE) by maximizing the evidence lower bound. We revisit VAE from the perspective of information theory to provide some theoretical foundations on using the reconstruction error and finally arrive at a simpler yet effective model for anomaly detection. In addition, to enhance the effectiveness of detecting anomalies, we incorporate a practical model uncertainty measure into the anomaly score. We show empirically the competitive performance of our approach on benchmark data sets.

关键词： Anomaly detection Rate-Distortion theory Information theory Model uncertainty MC dropout variational autoencoder

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variational autoencoder-based topological optimization of an anechoic coating: An efficient- and neural network-based design

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MATERIALS TODAY COMMUNICATIONS 2022年 32卷

作者： Sun, Yiping Li, Zhaoyu Chen, Jiadui Zhao, Xuefeng Tao, Meng Guizhou Univ Sch Mech Engn Guiyang 550025 Peoples R China Guizhou Open Univ Coll Aeronaut Guiyang 550023 Peoples R China Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Peoples R China

An anechoic coating is an artificial heterogeneous composite material composed of periodic cells with cavities. Using the local resonance of cavities to reduce their sound absorption frequencies and widen their frequency bands has been a research hotspot in recent years. One of the main challenges involves optimizing the cavity structure of an anechoic coating to obtain low-frequency, broadband, and strong sound absorption properties. In this paper, a variational autoencoder (VAE) model-based topology optimization method was investigated. First, the finite-element method (FEM) was used to calculate the sound absorption coefficient, and a dataset was constructed with samples whose average sound absorption coefficients ranged from 200 to 6000 Hz and were greater than 0.75. Then, the VAE model was trained to learn the key features of an anechoic coating. Finally, the data were reconstructed with Gaussian distributions. The decoder network of the trained VAE model was used to design a new anechoic coating. It took only approximately 3 s to generate 100 new topologies, and the average absorption coefficients were all greater than 0.754. This efficient neural network-based method can be further generalized to optimize the designs of various mechanical structural materials with specific functions.

关键词： Anechoic coating Topology optimization variational autoencoder Neural network

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Curiosity-Driven variational autoencoder for Deep Q Network 24th

Curiosity-Driven Variational Autoencoder for Deep Q Network

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24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Han, Gao-Jie Zhang, Xiao-Fang Wang, Hao Mao, Chen-Guang Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China

ISBN: (纸本)9783030474263;9783030474256

In recent years, deep reinforcement learning (DRL) has achieved tremendous success in high-dimensional and large-scale space control and sequential decision-making tasks. However, the current model-free DRL methods suffer from low sample efficiency, which is a bottleneck that limits their performance. To alleviate this problem, some researchers used the generative model for modeling the environment. But the generative model may become inaccurate or even collapse if the state has not been sufficiently explored. In this paper, we introduce a model called Curiosity-driven variational autoencoder (CVAE), which combines variational autoencoder and curiosity-driven exploration. During the training process, the CVAE model can improve sample efficiency while curiosity-driven exploration can make sufficient exploration in a complex environment. Then, a CVAE-based algorithm is proposed, namely DQN-CVAE, that scales CVAE to higher dimensional environments. Finally, the performance of our algorithm is evaluated through several Atari 2600 games, and the experimental results show that the DQN-CVAE achieves better performance in terms of average reward per episode on these games.

关键词： Reinforcement learning Deep Q learning Exploration variational autoencoder

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