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Neuromorphic visual scene understanding with resonator networks 利用共振网络实现神经形态视觉场景理解
IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-06-27 DOI: 10.1038/s42256-024-00848-0
Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, Bruno A. Olshausen, Yulia Sandamirskaya, Friedrich T. Sommer, E. Paxon Frady
Analysing a visual scene by inferring the configuration of a generative model is widely considered the most flexible and generalizable approach to scene understanding. Yet, one major problem is the computational challenge of the inference procedure, involving a combinatorial search across object identities and poses. Here we propose a neuromorphic solution exploiting three key concepts: (1) a computational framework based on vector symbolic architectures (VSAs) with complex-valued vectors, (2) the design of hierarchical resonator networks to factorize the non-commutative transforms translation and rotation in visual scenes and (3) the design of a multi-compartment spiking phasor neuron model for implementing complex-valued resonator networks on neuromorphic hardware. The VSA framework uses vector binding operations to form a generative image model in which binding acts as the equivariant operation for geometric transformations. A scene can therefore be described as a sum of vector products, which can then be efficiently factorized by a resonator network to infer objects and their poses. The hierarchical resonator network features a partitioned architecture in which vector binding is equivariant for horizontal and vertical translation within one partition and for rotation and scaling within the other partition. The spiking neuron model allows mapping the resonator network onto efficient and low-power neuromorphic hardware. Our approach is demonstrated on synthetic scenes composed of simple two-dimensional shapes undergoing rigid geometric transformations and colour changes. A companion paper demonstrates the same approach in real-world application scenarios for machine vision and robotics. The inference procedure for analysing a visual scene presents a computational challenge. Renner, Supic and colleagues develop a neural network model, the hierarchical resonator, to determine the generative factors of variation of objects in simple scenes. The resonator was implemented on neuromorphic hardware, using a spike-timing code for complex numbers.
通过推断生成模型的配置来分析视觉场景,被广泛认为是最灵活、最具通用性的场景理解方法。然而,一个主要问题是推理过程的计算挑战,其中涉及对物体身份和姿势的组合搜索。在此,我们提出了一种利用三个关键概念的神经形态解决方案:(1) 基于带有复值向量的向量符号架构(VSA)的计算框架;(2) 设计分层共振网络,将视觉场景中的非交换变换平移和旋转因数化;(3) 设计多室尖峰相位神经元模型,在神经形态硬件上实现复值共振网络。VSA 框架使用矢量绑定操作来形成生成图像模型,其中绑定操作是几何变换的等变操作。因此,场景可以描述为矢量乘积的总和,然后通过共振网络对其进行有效的因式分解,从而推断出物体及其姿势。分层共振网络采用分区架构,其中一个分区中的水平和垂直平移以及另一个分区中的旋转和缩放的矢量绑定是等变量的。尖峰神经元模型可将共振网络映射到高效、低功耗的神经形态硬件上。我们的方法在由简单的二维图形组成的合成场景上进行了演示,这些图形经历了刚性几何变换和颜色变化。另一篇论文则展示了在机器视觉和机器人技术的实际应用场景中的相同方法。
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引用次数: 0
Coordinate-based neural representations for computational adaptive optics in widefield microscopy 用于宽视场显微镜中计算自适应光学的基于坐标的神经表征
IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-06-24 DOI: 10.1038/s42256-024-00853-3
Iksung Kang, Qinrong Zhang, Stella X. Yu, Na Ji
Widefield microscopy is widely used for non-invasive imaging of biological structures at subcellular resolution. When applied to a complex specimen, its image quality is degraded by sample-induced optical aberration. Adaptive optics can correct wavefront distortion and restore diffraction-limited resolution but require wavefront sensing and corrective devices, increasing system complexity and cost. Here we describe a self-supervised machine learning algorithm, CoCoA, that performs joint wavefront estimation and three-dimensional structural information extraction from a single-input three-dimensional image stack without the need for external training datasets. We implemented CoCoA for widefield imaging of mouse brain tissues and validated its performance with direct-wavefront-sensing-based adaptive optics. Importantly, we systematically explored and quantitatively characterized the limiting factors of CoCoA’s performance. Using CoCoA, we demonstrated in vivo widefield mouse brain imaging using machine learning-based adaptive optics. Incorporating coordinate-based neural representations and a forward physics model, the self-supervised scheme of CoCoA should be applicable to microscopy modalities in general. Adaptive optics (AO) corrects aberrations and restores resolution but requires specialized hardware. Kang et al. introduce a self-supervised AO method (CoCoA) for widefield microscopy, achieving in vivo mouse brain imaging without wavefront sensors.
宽场显微镜被广泛用于对亚细胞分辨率的生物结构进行无创成像。当应用于复杂样本时,其图像质量会因样本引起的光学像差而下降。自适应光学技术可以校正波前畸变,恢复衍射极限分辨率,但需要波前传感和校正设备,从而增加了系统的复杂性和成本。在这里,我们介绍了一种自监督机器学习算法 CoCoA,它可以从单一输入的三维图像堆栈中执行联合波前估计和三维结构信息提取,而无需外部训练数据集。我们将 CoCoA 应用于小鼠脑组织的宽场成像,并用基于直接波前传感的自适应光学验证了它的性能。重要的是,我们系统地探索并定量描述了限制 CoCoA 性能的因素。通过使用 CoCoA,我们利用基于机器学习的自适应光学技术演示了活体宽视场小鼠大脑成像。结合基于坐标的神经表征和前向物理模型,CoCoA 的自监督方案应适用于一般的显微镜模式。
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引用次数: 0
Laplace neural operator for solving differential equations 用于求解微分方程的拉普拉斯神经算子
IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-06-24 DOI: 10.1038/s42256-024-00844-4
Qianying Cao, Somdatta Goswami, George Em Karniadakis
Neural operators map multiple functions to different functions, possibly in different spaces, unlike standard neural networks. Hence, neural operators allow the solution of parametric ordinary differential equations (ODEs) and partial differential equations (PDEs) for a distribution of boundary or initial conditions and excitations, but can also be used for system identification as well as designing various components of digital twins. We introduce the Laplace neural operator (LNO), which incorporates the pole–residue relationship between input–output spaces, leading to better interpretability and generalization for certain classes of problems. The LNO is capable of processing non-periodic signals and transient responses resulting from simultaneously zero and non-zero initial conditions, which makes it achieve better approximation accuracy over other neural operators for extrapolation circumstances in solving several ODEs and PDEs. We also highlight the LNO’s good interpolation ability, from a low-resolution input to high-resolution outputs at arbitrary locations within the domain. To demonstrate the scalability of LNO, we conduct large-scale simulations of Rossby waves around the globe, employing millions of degrees of freedom. Taken together, our findings show that a pretrained LNO model offers an effective real-time solution for general ODEs and PDEs at scale and is an efficient alternative to existing neural operators. Neural operators are powerful neural networks that approximate nonlinear dynamical systems and their responses. Cao and colleagues introduce the Laplace neural operator, a scalable approach that can effectively deal with non-periodic signals and transient responses and can outperform existing neural operators on certain classes of ODE and PDE problems.
与标准神经网络不同,神经算子可将多个函数映射为不同的函数,可能是不同空间的函数。因此,神经算子可以求解参数常微分方程(ODE)和偏微分方程(PDE)的边界或初始条件和激励分布,也可用于系统识别以及设计数字双胞胎的各种组件。我们引入了拉普拉斯神经算子(LNO),它结合了输入-输出空间之间的极点-残差关系,从而使某些类别的问题具有更好的可解释性和通用性。拉普拉斯神经算子能够处理非周期性信号以及由零和非零初始条件同时产生的瞬态响应,这使得它在求解若干 ODE 和 PDE 时,在外推法情况下比其他神经算子获得更好的近似精度。我们还强调了 LNO 在域内任意位置从低分辨率输入到高分辨率输出的良好插值能力。为了证明 LNO 的可扩展性,我们采用数百万自由度对全球各地的罗斯比波进行了大规模模拟。总之,我们的研究结果表明,经过预训练的 LNO 模型可为大规模的一般 ODE 和 PDE 提供有效的实时解决方案,是现有神经算子的有效替代品。
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引用次数: 0
Challenges, evaluation and opportunities for open-world learning 开放世界学习的挑战、评估和机遇
IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-06-24 DOI: 10.1038/s42256-024-00852-4
Mayank Kejriwal, Eric Kildebeck, Robert Steininger, Abhinav Shrivastava
Environmental changes can profoundly impact the performance of artificial intelligence systems operating in the real world, with effects ranging from overt catastrophic failures to non-robust behaviours that do not take changing context into account. Here we argue that designing machine intelligence that can operate in open worlds, including detecting, characterizing and adapting to structurally unexpected environmental changes, is a critical goal on the path to building systems that can solve complex and relatively under-determined problems. We present and distinguish between three forms of open-world learning (OWL)—weak, semi-strong and strong—and argue that a fully developed OWL system should be antifragile, rather than merely robust. An antifragile system, an example of which is the immune system, is not only robust to adverse events, but adapts to them quickly and becomes better at handling them in subsequent encounters. We also argue that, because OWL approaches must be capable of handling the unexpected, their practical evaluation can pose an interesting conceptual problem. AI systems operating in the real world unavoidably encounter unexpected environmental changes and need a built-in robustness and capability to learn fast, making use of advances such as lifelong and few-shot learning. Kejriwal et al. discuss three categories of such open-world learning and discuss applications such as self-driving cars and robotic inspection.
环境变化会对在现实世界中运行的人工智能系统的性能产生深远影响,其影响范围从明显的灾难性故障到不考虑环境变化的非稳健行为。在这里,我们认为,设计能在开放世界中运行的机器智能,包括检测、描述和适应结构上意想不到的环境变化,是建立能解决复杂和相对不确定问题的系统的关键目标。我们介绍并区分了三种形式的开放世界学习(OWL)--弱型、半强型和强型--并认为,一个全面开发的开放世界学习系统应该是反脆弱的,而不仅仅是稳健的。反脆弱系统的一个例子是免疫系统,它不仅对不利事件具有鲁棒性,而且能迅速适应这些事件,并在以后的遭遇中更好地处理这些事件。我们还认为,由于 OWL 方法必须能够处理意外事件,因此对它们的实际评估可能会带来一个有趣的概念问题。
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引用次数: 0
Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models 利用代用模型从基因组深度神经网络中解读顺式调控机制
IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-06-21 DOI: 10.1038/s42256-024-00851-5
Evan E. Seitz, David M. McCandlish, Justin B. Kinney, Peter K. Koo
Deep neural networks (DNNs) have greatly advanced the ability to predict genome function from sequence. However, elucidating underlying biological mechanisms from genomic DNNs remains challenging. Existing interpretability methods, such as attribution maps, have their origins in non-biological machine learning applications and therefore have the potential to be improved by incorporating domain-specific interpretation strategies. Here we introduce SQUID (Surrogate Quantitative Interpretability for Deepnets), a genomic DNN interpretability framework based on domain-specific surrogate modelling. SQUID approximates genomic DNNs in user-specified regions of sequence space using surrogate models—simpler quantitative models that have inherently interpretable mathematical forms. SQUID leverages domain knowledge to model cis-regulatory mechanisms in genomic DNNs, in particular by removing the confounding effects that nonlinearities and heteroscedastic noise in functional genomics data can have on model interpretation. Benchmarking analysis on multiple genomic DNNs shows that SQUID, when compared to established interpretability methods, identifies motifs that are more consistent across genomic loci and yields improved single-nucleotide variant-effect predictions. SQUID also supports surrogate models that quantify epistatic interactions within and between cis-regulatory elements, as well as global explanations of cis-regulatory mechanisms across sequence contexts. SQUID thus advances the ability to mechanistically interpret genomic DNNs. The intersection of genomics and deep learning shows promise for real impact on healthcare and biological research, but the lack of interpretability in terms of biological mechanisms is limiting utility and further development. As a potential solution, Koo et al. present SQUID, an interpretability framework built using domain-specific genomic surrogate models.
深度神经网络(DNN)大大提高了从序列预测基因组功能的能力。然而,从基因组 DNNs 中阐明潜在的生物机制仍然具有挑战性。现有的可解释性方法,如归因图,起源于非生物机器学习应用,因此有可能通过纳入特定领域的解释策略来加以改进。在此,我们介绍基于特定领域代用建模的基因组 DNN 可解释性框架 SQUID(Surrogate Quantitative Interpretability for Deepnets)。SQUID 在用户指定的序列空间区域使用代用模型--具有固有可解释数学形式的更简单定量模型--来近似基因组 DNN。SQUID 利用领域知识对基因组 DNN 中的顺式调控机制进行建模,特别是消除了功能基因组学数据中的非线性和异方差噪声对模型解释的干扰效应。对多个基因组 DNN 的基准分析表明,与已有的可解释性方法相比,SQUID 能识别跨基因组位点更一致的主题,并能改进单核苷酸变异效应预测。SQUID 还支持对顺式调控元件内部和之间的表观相互作用进行量化的替代模型,以及跨序列上下文的顺式调控机制的全局解释。因此,SQUID 提高了从机理上解释基因组 DNN 的能力。
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引用次数: 0
Reconciling privacy and accuracy in AI for medical imaging 协调医学影像人工智能的隐私和准确性
IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-06-21 DOI: 10.1038/s42256-024-00858-y
Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard F. Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
Artificial intelligence (AI) models are vulnerable to information leakage of their training data, which can be highly sensitive, for example, in medical imaging. Privacy-enhancing technologies, such as differential privacy (DP), aim to circumvent these susceptibilities. DP is the strongest possible protection for training models while bounding the risks of inferring the inclusion of training samples or reconstructing the original data. DP achieves this by setting a quantifiable privacy budget. Although a lower budget decreases the risk of information leakage, it typically also reduces the performance of such models. This imposes a trade-off between robust performance and stringent privacy. Additionally, the interpretation of a privacy budget remains abstract and challenging to contextualize. Here we contrast the performance of artificial intelligence models at various privacy budgets against both theoretical risk bounds and empirical success of reconstruction attacks. We show that using very large privacy budgets can render reconstruction attacks impossible, while drops in performance are negligible. We thus conclude that not using DP at all is negligent when applying artificial intelligence models to sensitive data. We deem our results to lay a foundation for further debates on striking a balance between privacy risks and model performance. Ziller and colleagues present a balanced investigation of the trade-off between privacy and performance when training artificially intelligent models for medical imaging analysis tasks. The authors evaluate the use of differential privacy in realistic threat scenarios, leading to their conclusion to promote the use of differential privacy, but implementing it in a manner that also retains performance.
人工智能(AI)模型很容易受到训练数据信息泄露的影响,而训练数据可能是高度敏感的,例如在医学成像中。隐私增强技术,如差分隐私(DP),旨在规避这些敏感性。DP 是对训练模型可能提供的最强保护,同时限制了推断训练样本或重建原始数据的风险。DP 通过设置可量化的隐私预算来实现这一目标。虽然较低的预算会降低信息泄露的风险,但通常也会降低此类模型的性能。这就需要在强大的性能和严格的隐私保护之间做出权衡。此外,对隐私预算的解释仍然是抽象的,难以具体化。在此,我们将人工智能模型在不同隐私预算下的性能与理论风险界限和重构攻击的经验成功率进行对比。我们的研究表明,使用非常大的隐私预算可以使重构攻击变得不可能,而性能的下降可以忽略不计。因此,我们得出结论,在将人工智能模型应用于敏感数据时,完全不使用 DP 是可以忽略不计的。我们认为,我们的研究结果为进一步讨论如何在隐私风险和模型性能之间取得平衡奠定了基础。
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引用次数: 0
Systematic analysis of 32,111 AI model cards characterizes documentation practice in AI 对 32 111 张人工智能模型卡进行系统分析,揭示人工智能文献实践的特点
IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-06-21 DOI: 10.1038/s42256-024-00857-z
Weixin Liang, Nazneen Rajani, Xinyu Yang, Ezinwanne Ozoani, Eric Wu, Yiqun Chen, Daniel Scott Smith, James Zou
The rapid proliferation of AI models has underscored the importance of thorough documentation, which enables users to understand, trust and effectively use these models in various applications. Although developers are encouraged to produce model cards, it’s not clear how much or what information these cards contain. In this study we conduct a comprehensive analysis of 32,111 AI model documentations on Hugging Face, a leading platform for distributing and deploying AI models. Our investigation sheds light on the prevailing model card documentation practices. Most AI models with a substantial number of downloads provide model cards, although with uneven informativeness. We find that sections addressing environmental impact, limitations and evaluation exhibit the lowest filled-out rates, whereas the training section is the one most consistently filled-out. We analyse the content of each section to characterize practitioners’ priorities. Interestingly, there are considerable discussions of data, sometimes with equal or even greater emphasis than the model itself. Our study provides a systematic assessment of community norms and practices surroinding model documentation through large-scale data science and linguistic analysis. As the number of AI models has rapidly grown, there is an increased focus on improving the documentation through model cards. Liang et al. explore questions around adoption practices and the type of information provided in model cards through a large-scale analysis of 32,111 model card documentation from 74,970 models.
人工智能模型的迅速扩散凸显了详尽文档的重要性,它能让用户理解、信任并在各种应用中有效地使用这些模型。尽管我们鼓励开发者制作模型卡片,但这些卡片包含多少信息或包含哪些信息却并不清楚。在本研究中,我们对 Hugging Face 上的 32111 份人工智能模型文档进行了全面分析,Hugging Face 是分发和部署人工智能模型的领先平台。我们的调查揭示了模型卡片文档的普遍做法。大多数有大量下载的人工智能模型都提供了模型卡,但信息量参差不齐。我们发现,涉及环境影响、局限性和评估的部分填写率最低,而训练部分则是填写率最高的部分。我们分析了每个部分的内容,以确定实践者的优先事项。有趣的是,我们对数据进行了大量讨论,有时对数据的重视程度甚至超过了模型本身。我们的研究通过大规模的数据科学和语言学分析,系统地评估了模型文档之外的社区规范和实践。
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引用次数: 0
Multiscale topology-enabled structure-to-sequence transformer for protein–ligand interaction predictions 用于蛋白质配体相互作用预测的多尺度拓扑结构-序列转换器
IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-06-21 DOI: 10.1038/s42256-024-00855-1
Dong Chen, Jian Liu, Guo-Wei Wei
Despite the success of pretrained natural language processing (NLP) models in various fields, their application in computational biology has been hindered by their reliance on biological sequences, which ignores vital three-dimensional (3D) structural information incompatible with the sequential architecture of NLP models. Here we present a topological transformer (TopoFormer), which is built by integrating NLP models and a multiscale topology technique, the persistent topological hyperdigraph Laplacian (PTHL), which systematically converts intricate 3D protein–ligand complexes at various spatial scales into an NLP-admissible sequence of topological invariants and homotopic shapes. PTHL systematically transforms intricate 3D protein–ligand complexes into NLP-compatible sequences of topological invariants and shapes, capturing essential interactions across spatial scales. TopoFormer gives rise to exemplary scoring accuracy and excellent performance in ranking, docking and screening tasks in several benchmark datasets. This approach can be utilized to convert general high-dimensional structured data into NLP-compatible sequences, paving the way for broader NLP based research. Transformers show much promise for applications in computational biology, but they rely on sequences, and a challenge is to incorporate 3D structural information. TopoFormer, proposed by Dong Chen et al., combines transformers with a mathematical multiscale topology technique to model 3D protein–ligand complexes, substantially enhancing performance in a range of prediction tasks of interest to drug discovery.
尽管预训练自然语言处理(NLP)模型在各个领域都取得了成功,但它们在计算生物学中的应用却因依赖生物序列而受到阻碍,因为生物序列忽略了重要的三维(3D)结构信息,与 NLP 模型的序列架构不兼容。在这里,我们介绍一种拓扑变换器(TopoFormer),它是通过整合 NLP 模型和多尺度拓扑技术--持久拓扑超图拉普拉斯(PTHL)--而建立起来的,它能系统地将各种空间尺度上错综复杂的三维蛋白质配体复合物转换成 NLP 允许的拓扑不变式和同位形状序列。PTHL 将复杂的三维蛋白质配体复合物系统地转换成与 NLP 兼容的拓扑不变式和形状序列,捕捉跨空间尺度的基本相互作用。TopoFormer 在多个基准数据集的排序、对接和筛选任务中,具有极高的评分准确性和出色的性能。这种方法可用于将一般的高维结构化数据转换成与 NLP 兼容的序列,为更广泛的基于 NLP 的研究铺平道路。
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引用次数: 0
Machine learning-aided generative molecular design 机器学习辅助生成分子设计
IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-06-18 DOI: 10.1038/s42256-024-00843-5
Yuanqi Du, Arian R. Jamasb, Jeff Guo, Tianfan Fu, Charles Harris, Yingheng Wang, Chenru Duan, Pietro Liò, Philippe Schwaller, Tom L. Blundell
Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing machine learning models that can achieve this on the fly to the satisfaction of medicinal chemists remains a challenge owing to the enormous search space. Researchers have addressed de novo design of molecules by decomposing the problem into a series of tasks determined by design criteria. Here we provide a comprehensive overview of the current state of the art in molecular design using machine learning models as well as important design decisions, such as the choice of molecular representations, generative methods and optimization strategies. Subsequently, we present a collection of practical applications in which the reviewed methodologies have been experimentally validated, encompassing both academic and industrial efforts. Finally, we draw attention to the theoretical, computational and empirical challenges in deploying generative machine learning and highlight future opportunities to better align such approaches to achieve realistic drug discovery end points. Data-driven generative methods have the potential to greatly facilitate molecular design tasks for drug design.
机器学习利用药物化学家的经验和设计偏好,将分子生成和筛选步骤结合在一个架构中,为加速早期药物发现提供了一种方法。然而,由于搜索空间巨大,要设计出能让药物化学家满意的机器学习模型仍然是一项挑战。研究人员通过将问题分解为一系列由设计标准决定的任务来解决分子的从头设计问题。在此,我们将全面概述目前使用机器学习模型进行分子设计的最新技术,以及重要的设计决策,如分子表征、生成方法和优化策略的选择。随后,我们介绍了一系列实际应用,其中所回顾的方法已在实验中得到验证,包括学术界和工业界的努力。最后,我们提请大家注意在部署生成式机器学习时所面临的理论、计算和经验方面的挑战,并强调未来有机会更好地调整这些方法,以实现现实的药物发现终点。
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引用次数: 0
Physicochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data 从序列数据中学习蛋白质配体相互作用指纹的物理化学图神经网络
IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-06-17 DOI: 10.1038/s42256-024-00847-1
Huan Yee Koh, Anh T. N. Nguyen, Shirui Pan, Lauren T. May, Geoffrey I. Webb
In drug discovery, determining the binding affinity and functional effects of small-molecule ligands on proteins is critical. Current computational methods can predict these protein–ligand interaction properties but often lose accuracy without high-resolution protein structures and falter in predicting functional effects. Here we introduce PSICHIC (PhySIcoCHemICal graph neural network), a framework incorporating physicochemical constraints to decode interaction fingerprints directly from sequence data alone. This enables PSICHIC to attain capabilities in decoding mechanisms underlying protein–ligand interactions, achieving state-of-the-art accuracy and interpretability. Trained on identical protein–ligand pairs without structural data, PSICHIC matched and even surpassed leading structure-based methods in binding-affinity prediction. In an experimental library screening for adenosine A1 receptor agonists, PSICHIC discerned functional effects effectively, ranking the sole novel agonist within the top three. PSICHIC’s interpretable fingerprints identified protein residues and ligand atoms involved in interactions, and helped in unveiling selectivity determinants of protein–ligand interaction. We foresee PSICHIC reshaping virtual screening and deepening our understanding of protein–ligand interactions. Predicting the binding affinity between small-molecule ligands and proteins is a key task in drug discovery; however, sequence-based methods are often less accurate than structure-based ones. Koh et al. develop a graph neural network using physicochemical constraints that discovers interactions between small molecules and proteins directly from sequence data and that can achieve state-of-the-art performance without the need for costly, experimental 3D structures.
在药物发现过程中,确定小分子配体与蛋白质的结合亲和力和功能效应至关重要。目前的计算方法可以预测这些蛋白质-配体相互作用的特性,但如果没有高分辨率的蛋白质结构,往往会失去准确性,在预测功能效应方面也会出现偏差。在这里,我们引入了 PSICHIC(PhySIcoCHemICal graph neural network),这是一个结合了物理化学约束的框架,可直接从序列数据中解码相互作用指纹。这使得 PSICHIC 能够解码蛋白质-配体相互作用的基本机制,达到最先进的准确性和可解释性。在没有结构数据的情况下,对相同的蛋白质配体对进行训练,PSICHIC 在结合亲和力预测方面达到甚至超过了基于结构的主要方法。在腺苷 A1 受体激动剂的实验库筛选中,PSICHIC 有效地识别了功能效应,将唯一的新型激动剂排在了前三位。PSICHIC 可解释的指纹识别出了参与相互作用的蛋白质残基和配体原子,并帮助揭示了蛋白质-配体相互作用的选择性决定因素。我们预计 PSICHIC 将重塑虚拟筛选,加深我们对蛋白质配体相互作用的理解。
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引用次数: 0
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Nature Machine Intelligence
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