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. DeepBlock is a deep learning framework for ligand generation, inspired by the DNA-encoded compound library technique, that enhances ligand design with building blocks and a rule-based reconstruction algorithm, achieving better drug properties.
{"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":"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. DeepBlock is a deep learning framework for ligand generation, inspired by the DNA-encoded compound library technique, that enhances ligand design with building blocks and a rule-based reconstruction algorithm, achieving better drug properties.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 11","pages":"851-864"},"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}
Pub Date : 2024-11-08DOI: 10.1038/s43588-024-00726-0
Loïc Lannelongue
A recent study has modeled and quantified the expected rise in electronic waste due to the increasing deployment of generative artificial intelligence.
最近的一项研究模拟并量化了由于越来越多地部署生成式人工智能，电子垃圾预计会增加。
{"title":"Modeling the increase of electronic waste due to generative AI","authors":"Loïc Lannelongue","doi":"10.1038/s43588-024-00726-0","DOIUrl":"10.1038/s43588-024-00726-0","url":null,"abstract":"A recent study has modeled and quantified the expected rise in electronic waste due to the increasing deployment of generative artificial intelligence.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 11","pages":"805-806"},"PeriodicalIF":12.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142634047","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}
Pub Date : 2024-11-08DOI: 10.1038/s43588-024-00724-2
Minghui Li
A recent study introduces a series of approaches that predict protein fitness and stability after the introduction of mutations. The work focuses on combining different data and pre-training to overcome data scarcity.
{"title":"Enhancing protein stability prediction with geometric learning and pre-training strategies","authors":"Minghui Li","doi":"10.1038/s43588-024-00724-2","DOIUrl":"10.1038/s43588-024-00724-2","url":null,"abstract":"A recent study introduces a series of approaches that predict protein fitness and stability after the introduction of mutations. The work focuses on combining different data and pre-training to overcome data scarcity.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 11","pages":"807-808"},"PeriodicalIF":12.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633965","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}
Pub Date : 2024-11-08DOI: 10.1038/s43588-024-00717-1
Daniella Bar-Lev, Omer Sabary, Eitan Yaakobi
This Perspective surveys the critical computational challenges associated with in vitro DNA-based data storage. As digital data expand exponentially, traditional storage media are becoming less viable, making DNA a promising solution due to its density and durability. However, numerous obstacles remain, including error correction, data retrieval from large volumes of noisy reads, and scalability. The Perspective also highlights challenges for DNA-based data centers, such as fault tolerance, random access, and data removal, which must be addressed to make DNA-based storage practical. As digital data expand exponentially, traditional storage media are becoming less viable, making DNA a promising solution due to its density and durability. In this Perspective, the authors discuss the critical computational challenges associated with in vitro DNA-based data storage.
本视角探讨了与基于 DNA 的体外数据存储相关的关键计算挑战。随着数字数据呈指数级增长，传统的存储介质越来越不可行，而 DNA 因其密度和耐用性成为一种有前途的解决方案。然而，仍存在许多障碍，包括纠错、从大量嘈杂读数中检索数据以及可扩展性。透视》还强调了基于 DNA 的数据中心所面临的挑战，如容错、随机存取和数据移除，要使基于 DNA 的存储实用化，就必须解决这些问题。
{"title":"The zettabyte era is in our DNA","authors":"Daniella Bar-Lev, Omer Sabary, Eitan Yaakobi","doi":"10.1038/s43588-024-00717-1","DOIUrl":"10.1038/s43588-024-00717-1","url":null,"abstract":"This Perspective surveys the critical computational challenges associated with in vitro DNA-based data storage. As digital data expand exponentially, traditional storage media are becoming less viable, making DNA a promising solution due to its density and durability. However, numerous obstacles remain, including error correction, data retrieval from large volumes of noisy reads, and scalability. The Perspective also highlights challenges for DNA-based data centers, such as fault tolerance, random access, and data removal, which must be addressed to make DNA-based storage practical. As digital data expand exponentially, traditional storage media are becoming less viable, making DNA a promising solution due to its density and durability. In this Perspective, the authors discuss the critical computational challenges associated with in vitro DNA-based data storage.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 11","pages":"813-817"},"PeriodicalIF":12.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142634050","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}
Pub Date : 2024-11-06DOI: 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
Non-line-of-sight (NLOS) imaging aims at recovering the shape and albedo of hidden objects. Despite recent advances, real-time video of complex and dynamic scenes remains a major challenge owing to the weak signal of multiply scattered light. Here we propose and demonstrate a framework of spectrum filtering and motion compensation to realize high-quality NLOS video for room-sized scenes. Spectrum filtering leverages a wave-based model for denoising and deblurring in the frequency domain, enabling computational image reconstruction with a small number of sampling points. Motion compensation tailored with an interleaved scanning scheme can compute high-resolution live video during the acquisition of low-quality image sequences. Together, we demonstrate live NLOS videos at 4 fps for a variety of dynamic real-life scenes. The results mark a substantial stride toward real-time, large-scale and low-power NLOS imaging and sensing applications. The authors propose a framework incorporating spectrum filtering and motion compensation, which enables non-line-of-sight live videos at 4 fps for a variety of dynamic real-life scenes.
{"title":"Real-time non-line-of-sight computational imaging using spectrum filtering and motion compensation","authors":"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","doi":"10.1038/s43588-024-00722-4","DOIUrl":"10.1038/s43588-024-00722-4","url":null,"abstract":"Non-line-of-sight (NLOS) imaging aims at recovering the shape and albedo of hidden objects. Despite recent advances, real-time video of complex and dynamic scenes remains a major challenge owing to the weak signal of multiply scattered light. Here we propose and demonstrate a framework of spectrum filtering and motion compensation to realize high-quality NLOS video for room-sized scenes. Spectrum filtering leverages a wave-based model for denoising and deblurring in the frequency domain, enabling computational image reconstruction with a small number of sampling points. Motion compensation tailored with an interleaved scanning scheme can compute high-resolution live video during the acquisition of low-quality image sequences. Together, we demonstrate live NLOS videos at 4 fps for a variety of dynamic real-life scenes. The results mark a substantial stride toward real-time, large-scale and low-power NLOS imaging and sensing applications. The authors propose a framework incorporating spectrum filtering and motion compensation, which enables non-line-of-sight live videos at 4 fps for a variety of dynamic real-life scenes.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"920-927"},"PeriodicalIF":12.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142591484","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}
Pub Date : 2024-11-06DOI: 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
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. A deep learning platform for structure-guided, generative design of RNA sequences is developed and used to discover fluorescent RNA aptamers.
{"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":"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. A deep learning platform for structure-guided, generative design of RNA sequences is developed and used to discover fluorescent RNA aptamers.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 11","pages":"829-839"},"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}
Pub Date : 2024-11-01DOI: 10.1038/s43588-024-00713-5
Weikang Li, Dong-Ling Deng
A method is introduced to compute provable bounds on noise-free quantum expectation values from noisy samples, promising potential applications in quantum optimization and machine learning.
Pub Date : 2024-11-01DOI: 10.1038/s43588-024-00709-1
Samantha V. Barron, Daniel J. Egger, Elijah Pelofske, Andreas Bärtschi, Stephan Eidenbenz, Matthis Lehmkuehler, Stefan Woerner
Quantum computing has emerged as a powerful computational paradigm capable of solving problems beyond the reach of classical computers. However, today’s quantum computers are noisy, posing challenges to obtaining accurate results. Here, we explore the impact of noise on quantum computing, focusing on the challenges in sampling bit strings from noisy quantum computers and the implications for optimization and machine learning. We formally quantify the sampling overhead to extract good samples from noisy quantum computers and relate it to the layer fidelity, a metric to determine the performance of noisy quantum processors. Further, we show how this allows us to use the conditional value at risk of noisy samples to determine provable bounds on noise-free expectation values. We discuss how to leverage these bounds for different algorithms and demonstrate our findings through experiments on real quantum computers involving up to 127 qubits. The results show strong alignment with theoretical predictions. In this study, the authors investigate the impact of noise on quantum computing with a focus on the challenges in sampling bit strings from noisy quantum computers, which has implications for optimization and machine learning.
{"title":"Provable bounds for noise-free expectation values computed from noisy samples","authors":"Samantha V. Barron, Daniel J. Egger, Elijah Pelofske, Andreas Bärtschi, Stephan Eidenbenz, Matthis Lehmkuehler, Stefan Woerner","doi":"10.1038/s43588-024-00709-1","DOIUrl":"10.1038/s43588-024-00709-1","url":null,"abstract":"Quantum computing has emerged as a powerful computational paradigm capable of solving problems beyond the reach of classical computers. However, today’s quantum computers are noisy, posing challenges to obtaining accurate results. Here, we explore the impact of noise on quantum computing, focusing on the challenges in sampling bit strings from noisy quantum computers and the implications for optimization and machine learning. We formally quantify the sampling overhead to extract good samples from noisy quantum computers and relate it to the layer fidelity, a metric to determine the performance of noisy quantum processors. Further, we show how this allows us to use the conditional value at risk of noisy samples to determine provable bounds on noise-free expectation values. We discuss how to leverage these bounds for different algorithms and demonstrate our findings through experiments on real quantum computers involving up to 127 qubits. The results show strong alignment with theoretical predictions. In this study, the authors investigate the impact of noise on quantum computing with a focus on the challenges in sampling bit strings from noisy quantum computers, which has implications for optimization and machine learning.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 11","pages":"865-875"},"PeriodicalIF":12.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00709-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142564932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Generative artificial intelligence (GAI) requires substantial computational resources for model training and inference, but the electronic-waste (e-waste) implications of GAI and its management strategies remain underexplored. Here we introduce a computational power-driven material flow analysis framework to quantify and explore ways of managing the e-waste generated by GAI, with a particular focus on large language models. Our findings indicate that this e-waste stream could increase, potentially reaching a total accumulation of 1.2–5.0 million tons during 2020–2030, under different future GAI development settings. This may be intensified in the context of geopolitical restrictions on semiconductor imports and the rapid server turnover for operational cost savings. Meanwhile, we show that the implementation of circular economy strategies along the GAI value chain could reduce e-waste generation by 16–86%. This underscores the importance of proactive e-waste management in the face of advancing GAI technologies. Generative artificial intelligence (GAI) is driving a surge in e-waste due to intensive computational infrastructure needs. This study emphasizes the necessity for proactive implementation of circular economy practices throughout GAI value chains.
生成式人工智能（GAI）需要大量计算资源来进行模型训练和推理，但 GAI 及其管理策略对电子垃圾（e-waste）的影响仍未得到充分探索。在此，我们引入了一个计算力驱动的物质流分析框架，以量化和探索管理 GAI 产生的电子垃圾的方法，尤其侧重于大型语言模型。我们的研究结果表明，在未来不同的 GAI 发展环境下，电子废物流可能会增加，在 2020-2030 年期间可能达到 120-500 万吨的总积累量。在地缘政治对半导体进口的限制以及服务器为节约运营成本而快速更替的背景下，这种情况可能会加剧。同时，我们的研究表明，在 GAI 价值链上实施循环经济战略可将电子垃圾的产生量减少 16-86%。这凸显了面对不断进步的 GAI 技术，积极管理电子废物的重要性。
{"title":"E-waste challenges of generative artificial intelligence","authors":"Peng Wang, Ling-Yu Zhang, Asaf Tzachor, Wei-Qiang Chen","doi":"10.1038/s43588-024-00712-6","DOIUrl":"10.1038/s43588-024-00712-6","url":null,"abstract":"Generative artificial intelligence (GAI) requires substantial computational resources for model training and inference, but the electronic-waste (e-waste) implications of GAI and its management strategies remain underexplored. Here we introduce a computational power-driven material flow analysis framework to quantify and explore ways of managing the e-waste generated by GAI, with a particular focus on large language models. Our findings indicate that this e-waste stream could increase, potentially reaching a total accumulation of 1.2–5.0 million tons during 2020–2030, under different future GAI development settings. This may be intensified in the context of geopolitical restrictions on semiconductor imports and the rapid server turnover for operational cost savings. Meanwhile, we show that the implementation of circular economy strategies along the GAI value chain could reduce e-waste generation by 16–86%. This underscores the importance of proactive e-waste management in the face of advancing GAI technologies. Generative artificial intelligence (GAI) is driving a surge in e-waste due to intensive computational infrastructure needs. This study emphasizes the necessity for proactive implementation of circular economy practices throughout GAI value chains.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 11","pages":"818-823"},"PeriodicalIF":12.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142523796","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：