Nature computational science最新文献

Collective deliberation driven by AI. 由人工智能驱动的集体审议。

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Nature computational science Pub Date : 2024-11-18 DOI: 10.1038/s43588-024-00736-y

Fernando Chirigati

引用次数: 0

Harnessing deep learning to build optimized ligands. 利用深度学习构建优化配体。

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Nature computational science Pub Date : 2024-11-14 DOI: 10.1038/s43588-024-00725-1

Orestis A Ntintas, Theodoros Daglis, Vassilis G Gorgoulis

引用次数: 0

MassiveFold: unveiling AlphaFold's hidden potential with optimized and parallelized massive sampling. MassiveFold：通过优化和并行化的大规模采样挖掘 AlphaFold 隐藏的潜力。

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Nature computational science Pub Date : 2024-11-11 DOI: 10.1038/s43588-024-00714-4

Nessim Raouraoua, Claudio Mirabello, Thibaut Véry, Christophe Blanchet, Björn Wallner, Marc F Lensink, Guillaume Brysbaert

引用次数: 0

A deep learning approach for rational ligand generation with toxicity control via reactive building blocks. 通过活性构件控制毒性的深度学习配体生成方法。

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Nature computational science Pub Date : 2024-11-08 DOI: 10.1038/s43588-024-00718-0

Pengyong Li, Kaihao Zhang, Tianxiao Liu, Ruiqiang Lu, Yangyang Chen, Xiaojun Yao, Lin Gao, Xiangxiang Zeng

{"title":"A deep learning approach for rational ligand generation with toxicity control via reactive building blocks.","authors":"Pengyong Li, Kaihao Zhang, Tianxiao Liu, Ruiqiang Lu, Yangyang Chen, Xiaojun Yao, Lin Gao, Xiangxiang Zeng","doi":"10.1038/s43588-024-00718-0","DOIUrl":"https://doi.org/10.1038/s43588-024-00718-0","url":null,"abstract":"Deep generative models are gaining attention in the field of de novo drug design. However, the rational design of ligand molecules for novel targets remains challenging, particularly in controlling the properties of the generated molecules. Here, inspired by the DNA-encoded compound library technique, we introduce DeepBlock, a deep learning approach for block-based ligand generation tailored to target protein sequences while enabling precise property control. DeepBlock neatly divides the generation process into two steps: building blocks generation and molecule reconstruction, accomplished by a neural network and a rule-based reconstruction algorithm we proposed, respectively. Furthermore, DeepBlock synergizes the optimization algorithm and deep learning to regulate the properties of the generated molecules. Experiments show that DeepBlock outperforms existing methods in generating ligands with affinity, synthetic accessibility and drug likeness. Moreover, when integrated with simulated annealing or Bayesian optimization using toxicity as the optimization objective, DeepBlock successfully generates ligands with low toxicity while preserving affinity with the target.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing protein stability prediction with geometric learning and pre-training strategies. 利用几何学习和预训练策略增强蛋白质稳定性预测。

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Nature computational science Pub Date : 2024-11-08 DOI: 10.1038/s43588-024-00724-2

Minghui Li

引用次数: 0

Modeling the increase of electronic waste due to generative AI. 模拟生成式人工智能导致的电子垃圾增加。

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Nature computational science Pub Date : 2024-11-08 DOI: 10.1038/s43588-024-00726-0

Loïc Lannelongue

引用次数: 0

The zettabyte era is in our DNA. zettabyte 时代是我们的基因。

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Nature computational science Pub Date : 2024-11-08 DOI: 10.1038/s43588-024-00717-1

Daniella Bar-Lev, Omer Sabary, Eitan Yaakobi

引用次数: 0

Real-time non-line-of-sight computational imaging using spectrum filtering and motion compensation. 利用频谱滤波和运动补偿进行实时非视距计算成像。

IF 12

Nature computational science Pub Date : 2024-11-06 DOI: 10.1038/s43588-024-00722-4

Jun-Tian Ye, Yi Sun, Wenwen Li, Jian-Wei Zeng, Yu Hong, Zheng-Ping Li, Xin Huang, Xianghui Xue, Xin Yuan, Feihu Xu, Xiankang Dou, Jian-Wei Pan

引用次数: 0

Deep generative design of RNA aptamers using structural predictions. 利用结构预测深度生成设计 RNA 配合物。

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Nature computational science Pub Date : 2024-11-06 DOI: 10.1038/s43588-024-00720-6

Felix Wong, Dongchen He, Aarti Krishnan, Liang Hong, Alexander Z Wang, Jiuming Wang, Zhihang Hu, Satotaka Omori, Alicia Li, Jiahua Rao, Qinze Yu, Wengong Jin, Tianqing Zhang, Katherine Ilia, Jack X Chen, Shuangjia Zheng, Irwin King, Yu Li, James J Collins

{"title":"Deep generative design of RNA aptamers using structural predictions.","authors":"Felix Wong, Dongchen He, Aarti Krishnan, Liang Hong, Alexander Z Wang, Jiuming Wang, Zhihang Hu, Satotaka Omori, Alicia Li, Jiahua Rao, Qinze Yu, Wengong Jin, Tianqing Zhang, Katherine Ilia, Jack X Chen, Shuangjia Zheng, Irwin King, Yu Li, James J Collins","doi":"10.1038/s43588-024-00720-6","DOIUrl":"https://doi.org/10.1038/s43588-024-00720-6","url":null,"abstract":"RNAs represent a class of programmable biomolecules capable of performing diverse biological functions. Recent studies have developed accurate RNA three-dimensional structure prediction methods, which may enable new RNAs to be designed in a structure-guided manner. Here, we develop a structure-to-sequence deep learning platform for the de novo generative design of RNA aptamers. We show that our approach can design RNA aptamers that are predicted to be structurally similar, yet sequence dissimilar, to known light-up aptamers that fluoresce in the presence of small molecules. We experimentally validate several generated RNA aptamers to have fluorescent activity, show that these aptamers can be optimized for activity in silico, and find that they exhibit a mechanism of fluorescence similar to that of known light-up aptamers. Our results demonstrate how structural predictions can guide the targeted and resource-efficient design of new RNA sequences.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142591559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extracting reliable quantum outputs for noisy devices. 为噪声设备提取可靠的量子输出。

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Nature computational science Pub Date : 2024-11-01 DOI: 10.1038/s43588-024-00713-5

Weikang Li, Dong-Ling Deng

引用次数: 0