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1,118 results on '"Zheng, Mingyue"'

1. P2DFlow: A Protein Ensemble Generative Model with SE(3) Flow Matching

Author

Jin, Yaowei, Huang, Qi, Song, Ziyang, Zheng, Mingyue, Teng, Dan, and Shi, Qian

Subjects
Physics - Biological Physics, Computer Science - Machine Learning
Abstract

Biological processes, functions, and properties are intricately linked to the ensemble of protein conformations, rather than being solely determined by a single stable conformation. In this study, we have developed P2DFlow, a generative model based on SE(3) flow matching, to predict the structural ensembles of proteins. We specifically designed a valuable prior for the flow process and enhanced the model's ability to distinguish each intermediate state by incorporating an additional dimension to describe the ensemble data, which can reflect the physical laws governing the distribution of ensembles, so that the prior knowledge can effectively guide the generation process. When trained and evaluated on the MD datasets of ATLAS, P2DFlow outperforms other baseline models on extensive experiments, successfully capturing the observable dynamic fluctuations as evidenced in crystal structure and MD simulations. As a potential proxy agent for protein molecular simulation, the high-quality ensembles generated by P2DFlow could significantly aid in understanding protein functions across various scenarios. Code is available at https://github.com/BLEACH366/P2DFlow

Published
2024

2. Structure-Based Molecule Optimization via Gradient-Guided Bayesian Update

Author

Qiu, Keyue, Song, Yuxuan, Yu, Jie, Ma, Hongbo, Cao, Ziyao, Zhang, Zhilong, Wu, Yushuai, Zheng, Mingyue, Zhou, Hao, and Ma, Wei-Ying

Subjects
Quantitative Biology - Biomolecules, Computer Science - Artificial Intelligence
Abstract

Structure-based molecule optimization (SBMO) aims to optimize molecules with both continuous coordinates and discrete types against protein targets. A promising direction is to exert gradient guidance on generative models given its remarkable success in images, but it is challenging to guide discrete data and risks inconsistencies between modalities. To this end, we leverage a continuous and differentiable space derived through Bayesian inference, presenting Molecule Joint Optimization (MolJO), the first gradient-based SBMO framework that facilitates joint guidance signals across different modalities while preserving SE(3)-equivariance. We introduce a novel backward correction strategy that optimizes within a sliding window of the past histories, allowing for a seamless trade-off between explore-and-exploit during optimization. Our proposed MolJO achieves state-of-the-art performance on CrossDocked2020 benchmark (Success Rate 51.3% , Vina Dock -9.05 and SA 0.78), more than 4x improvement in Success Rate compared to the gradient-based counterpart, and 2x "Me-Better" Ratio as much as 3D baselines. Furthermore, we extend MolJO to a wide range of optimization settings, including multi-objective optimization and challenging tasks in drug design such as R-group optimization and scaffold hopping, further underscoring its versatility and potential., Comment: 27 pages, 17 figures

Published
2024

7. MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space

Author

Qu, Yanru, Qiu, Keyue, Song, Yuxuan, Gong, Jingjing, Han, Jiawei, Zheng, Mingyue, Zhou, Hao, and Ma, Wei-Ying

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning
Abstract

Generative models for structure-based drug design (SBDD) have shown promising results in recent years. Existing works mainly focus on how to generate molecules with higher binding affinity, ignoring the feasibility prerequisites for generated 3D poses and resulting in false positives. We conduct thorough studies on key factors of ill-conformational problems when applying autoregressive methods and diffusion to SBDD, including mode collapse and hybrid continuous-discrete space. In this paper, we introduce MolCRAFT, the first SBDD model that operates in the continuous parameter space, together with a novel noise reduced sampling strategy. Empirical results show that our model consistently achieves superior performance in binding affinity with more stable 3D structure, demonstrating our ability to accurately model interatomic interactions. To our best knowledge, MolCRAFT is the first to achieve reference-level Vina Scores (-6.59 kcal/mol) with comparable molecular size, outperforming other strong baselines by a wide margin (-0.84 kcal/mol). Code is available at https://github.com/AlgoMole/MolCRAFT., Comment: Accepted to ICML 2024

Published
2024

8. Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks

Author

Song, Yuxuan, Gong, Jingjing, Qu, Yanru, Zhou, Hao, Zheng, Mingyue, Liu, Jingjing, and Ma, Wei-Ying

Subjects
Physics - Chemical Physics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Quantitative Biology - Biomolecules
Abstract

Advanced generative model (e.g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry. This work introduces Geometric Bayesian Flow Networks (GeoBFN), which naturally fits molecule geometry by modeling diverse modalities in the differentiable parameter space of distributions. GeoBFN maintains the SE-(3) invariant density modeling property by incorporating equivariant inter-dependency modeling on parameters of distributions and unifying the probabilistic modeling of different modalities. Through optimized training and sampling techniques, we demonstrate that GeoBFN achieves state-of-the-art performance on multiple 3D molecule generation benchmarks in terms of generation quality (90.87% molecule stability in QM9 and 85.6% atom stability in GEOM-DRUG. GeoBFN can also conduct sampling with any number of steps to reach an optimal trade-off between efficiency and quality (e.g., 20-times speedup without sacrificing performance)., Comment: ICLR 2024

Published
2024

10. Structure-Based Drug Design via 3D Molecular Generative Pre-training and Sampling

Author

Yang, Yuwei, Ouyang, Siqi, Hu, Xueyu, Zheng, Mingyue, Zhou, Hao, and Li, Lei

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning
Abstract

Structure-based drug design aims at generating high affinity ligands with prior knowledge of 3D target structures. Existing methods either use conditional generative model to learn the distribution of 3D ligands given target binding sites, or iteratively modify molecules to optimize a structure-based activity estimator. The former is highly constrained by data quantity and quality, which leaves optimization-based approaches more promising in practical scenario. However, existing optimization-based approaches choose to edit molecules in 2D space, and use molecular docking to estimate the activity using docking predicted 3D target-ligand complexes. The misalignment between the action space and the objective hinders the performance of these models, especially for those employ deep learning for acceleration. In this work, we propose MolEdit3D to combine 3D molecular generation with optimization frameworks. We develop a novel 3D graph editing model to generate molecules using fragments, and pre-train this model on abundant 3D ligands for learning target-independent properties. Then we employ a target-guided self-learning strategy to improve target-related properties using self-sampled molecules. MolEdit3D achieves state-of-the-art performance on majority of the evaluation metrics, and demonstrate strong capability of capturing both target-dependent and -independent properties.

Published
2024

11. Sodium oligomannate disrupts the adherence of Ribhigh bacteria to gut epithelia to block SAA-triggered Th1 inflammation in 5XFAD transgenic mice

Author

Wang, Xinyi, Xie, Zuoquan, Yuan, Jie, Jin, Enjing, Lian, Wen, Chang, Shuaishuai, Sun, Guangqiang, Feng, Zhengnan, Xu, Hui, Du, Chen, Yang, Xinying, Xia, Aihua, Qiu, Ji, Zhang, Qingli, Lin, Feifei, Liu, Jia, Li, Liang, Du, Xiaoguang, Xiao, Zhongping, Yi, Zhou, Luo, Zhiyu, Ge, Changrong, Li, Rui, Zheng, Mingyue, Jiang, Yi, Wang, Tao, Zhang, Jing, Guo, Qihao, and Geng, Meiyu

Published
2024
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17. Structure-based design of pan-coronavirus inhibitors targeting host cathepsin L and calpain-1

Author

Xie, Xiong, Lan, Qiaoshuai, Zhao, Jinyi, Zhang, Sulin, Liu, Lu, Zhang, Yumin, Xu, Wei, Shao, Maolin, Peng, Jingjing, Xia, Shuai, Zhu, Yan, Zhang, Keke, Zhang, Xianglei, Zhang, Ruxue, Li, Jian, Dai, Wenhao, Ge, Zhen, Hu, Shulei, Yu, Changyue, Wang, Jiang, Ma, Dakota, Zheng, Mingyue, Yang, Haitao, Xiao, Gengfu, Rao, Zihe, Lu, Lu, Zhang, Leike, Bai, Fang, Zhao, Yao, Jiang, Shibo, and Liu, Hong

Published
2024
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27. An overview of recent advances and challenges in predicting compound-protein interaction (CPI)

Author

Li Yanbei, Fan Zhehuan, Rao Jingxin, Chen Zhiyi, Chu Qinyu, Zheng Mingyue, and Li Xutong

Subjects
compound-protein interaction prediction, drug discovery, artificial intelligence, scoring function, chemogenomics, Medicine
Abstract

Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.

Published
2023
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35. Sequence-based drug design as a concept in computational drug design

Author

Chen, Lifan, Fan, Zisheng, Chang, Jie, Yang, Ruirui, Hou, Hui, Guo, Hao, Zhang, Yinghui, Yang, Tianbiao, Zhou, Chenmao, Sui, Qibang, Chen, Zhengyang, Zheng, Chen, Hao, Xinyue, Zhang, Keke, Cui, Rongrong, Zhang, Zehong, Ma, Hudson, Ding, Yiluan, Zhang, Naixia, Lu, Xiaojie, Luo, Xiaomin, Jiang, Hualiang, Zhang, Sulin, and Zheng, Mingyue

Published
2023
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38. Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge.

Author

Xiong, Zhaoping, Jeon, Minji, Allaway, Robert J, Kang, Jaewoo, Park, Donghyeon, Lee, Jinhyuk, Jeon, Hwisang, Ko, Miyoung, Jiang, Hualiang, Zheng, Mingyue, Tan, Aik Choon, Guo, Xindi, Multi-Targeting Drug DREAM Challenge Community, Dang, Kristen K, Tropsha, Alex, Hecht, Chana, Das, Tirtha K, Carlson, Heather A, Abagyan, Ruben, Guinney, Justin, Schlessinger, Avner, and Cagan, Ross

Subjects
Multi-Targeting Drug DREAM Challenge Community, Rare Diseases, Brain Disorders, Orphan Drug, 5.1 Pharmaceuticals, Bioinformatics, Mathematical Sciences, Biological Sciences, Information and Computing Sciences
Abstract

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ('polypharmacology'). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

Published
2021

47. Targeting UHRF1-SAP30-MXD4 axis for leukemia initiating cell eradication in myeloid leukemia

Author

Hu, Cheng-Long, Chen, Bing-Yi, Li, Zijuan, Yang, Tianbiao, Xu, Chun-Hui, Yang, Ruirui, Yu, Peng-Cheng, Zhao, Jingyao, Liu, Ting, Liu, Na, Shan, Bin, Zhang, Qunling, Song, Junhong, Fei, Ming-Yue, Zong, Li-Juan, Zhang, Jia-Ying, Wu, Ji-Chuan, Chen, Shu-Bei, Wang, Yong, Chang, Binhe, Hou, Dan, Liu, Ping, Jiang, Yilun, Li, Xiya, Chen, Xinchi, Deng, Chu-Han, Ren, Yi-Yi, Wang, Roujia, Jin, Jiacheng, Xue, Kai, Zhang, Ying, Du, Meirong, Shi, Jun, Wu, Ling-Yun, Chang, Chun-Kang, Shen, Shuhong, Chen, Zhu, Chen, Sai-Juan, Liu, Xiaolong, Sun, Xiao-Jian, Zheng, Mingyue, and Wang, Lan

Published
2022
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