核磁共振（NMR）光谱是结构生物学的主要技术之一，蛋白质数据库中沉积了11,800多个蛋白质结构。 NMR可以阐明溶液，活细胞和固体中中小型蛋白质的结构和动力学，但受到乏味的数据分析过程的限制。通常，它需要训练有素的专家进行数周或数月的手动工作，以将NMR测量变成蛋白质结构。该过程的自动化是一个空旷的问题，在30年前在该领域中提出。在这里，我们提出了解决这一挑战的解决方案，该解决方案可以在完成测量后几小时内对蛋白质NMR数据进行完全自动化的分析。仅使用NMR光谱和蛋白质序列作为输入，我们的基于机器学习的方法，Artina，可严格地提供信号位置，共振分配和结构，而无需任何人类干预。 Artina在包含1329个多维NMR光谱的100个蛋白基准测试中进行了测试，展示了其以1.44 {\ aa}中位数RMSD求解结构的能力，并识别91.36％的正确NMR共振分配。非专家可以使用Artina，从而减少了NMR的蛋白质分配或结构确定的努力，从而基本上是在样品的制备和光谱测量中进行的。

在三维分子结构上运行的计算方法有可能解决生物学和化学的重要问题。特别地，深度神经网络的重视，但它们在生物分子结构域中的广泛采用受到缺乏系统性能基准或统一工具包的限制，用于与分子数据相互作用。为了解决这个问题，我们呈现Atom3D，这是一个新颖的和现有的基准数据集的集合，跨越几个密钥的生物分子。我们为这些任务中的每一个实施多种三维分子学习方法，并表明它们始终如一地提高了基于单维和二维表示的方法的性能。结构的具体选择对于性能至关重要，具有涉及复杂几何形状的任务的三维卷积网络，在需要详细位置信息的系统中表现出良好的图形网络，以及最近开发的设备越多的网络显示出显着承诺。我们的结果表明，许多分子问题符合三维分子学习的增益，并且有可能改善许多仍然过分曝光的任务。为了降低进入并促进现场进一步发展的障碍，我们还提供了一套全面的DataSet处理，模型培训和在我们的开源ATOM3D Python包中的评估工具套件。所有数据集都可以从https://www.atom3d.ai下载。

虽然最近在许多科学领域都变得无处不在，但对其评估的关注较少。对于分子生成模型，最先进的是孤立或与其输入有关的输出。但是，它们的生物学和功能特性（例如配体 - 靶标相互作用）尚未得到解决。在这项研究中，提出了一种新型的生物学启发的基准，用于评估分子生成模型。具体而言，设计了三个不同的参考数据集，并引入了与药物发现过程直接相关的一组指标。特别是我们提出了一个娱乐指标，将药物目标亲和力预测和分子对接应用作为评估生成产量的互补技术。虽然所有三个指标均在测试的生成模型中均表现出一致的结果，但对药物目标亲和力结合和分子对接分数进行了更详细的比较，表明单峰预测器可能会导致关于目标结合在分子水平和多模式方法的错误结论，而多模式的方法是错误的结论。因此优选。该框架的关键优点是，它通过明确关注配体 - 靶标相互作用，将先前的物理化学域知识纳入基准测试过程，从而创建了一种高效的工具，不仅用于评估分子生成型输出，而且还用于丰富富含分子生成的输出。一般而言，药物发现过程。

蛋白质RNA相互作用对各种细胞活性至关重要。已经开发出实验和计算技术来研究相互作用。由于先前数据库的限制，尤其是缺乏蛋白质结构数据，大多数现有的计算方法严重依赖于序列数据，只有一小部分使用结构信息。最近，alphafold彻底改变了整个蛋白质和生物领域。可预应学，在即将到来的年份，也将显着促进蛋白质-RNA相互作用预测。在这项工作中，我们对该字段进行了彻底的审查，调查绑定站点和绑定偏好预测问题，并覆盖常用的数据集，功能和模型。我们还指出了这一领域的潜在挑战和机遇。本调查总结了过去的RBP-RNA互动领域的发展，并预见到了alphafold时代未来的发展。

鉴定新型药物靶标相互作用（DTI）是药物发现中的关键和速率限制步骤。虽然已经提出了深入学习模型来加速识别过程，但我们表明最先进的模型无法概括到新颖（即，从未见过的）结构上。我们首先揭示负责此缺点的机制，展示模型如何依赖于利用蛋白质 - 配体二分网络拓扑的捷径，而不是学习节点特征。然后，我们介绍AI-BIND，这是一个与无监督的预训练的基于网络的采样策略相结合的管道，使我们能够限制注释不平衡并改善新型蛋白质和配体的结合预测。我们通过预测具有结合亲和力的药物和天然化合物对SARS-COV-2病毒蛋白和相关的人蛋白质来说明Ai-reat的值。我们还通过自动扩展模拟和与最近的实验证据进行比较来验证这些预测。总体而言，AI-Bind提供了一种强大的高通量方法来识别药物目标组合，具有成为药物发现中强大工具的可能性。

Applying deep learning concepts from image detection and graph theory has greatly advanced protein-ligand binding affinity prediction, a challenge with enormous ramifications for both drug discovery and protein engineering. We build upon these advances by designing a novel deep learning architecture consisting of a 3-dimensional convolutional neural network utilizing channel-wise attention and two graph convolutional networks utilizing attention-based aggregation of node features. HAC-Net (Hybrid Attention-Based Convolutional Neural Network) obtains state-of-the-art results on the PDBbind v.2016 core set, the most widely recognized benchmark in the field. We extensively assess the generalizability of our model using multiple train-test splits, each of which maximizes differences between either protein structures, protein sequences, or ligand extended-connectivity fingerprints. Furthermore, we perform 10-fold cross-validation with a similarity cutoff between SMILES strings of ligands in the training and test sets, and also evaluate the performance of HAC-Net on lower-quality data. We envision that this model can be extended to a broad range of supervised learning problems related to structure-based biomolecular property prediction. All of our software is available as open source at https://github.com/gregory-kyro/HAC-Net/.

使用精确能量功能的原子模拟可以为气体和冷凝相中的分子的功能运动提供分子水平洞察。与最近开发的和目前在整合和结合的努力与机器学习技术相结合，提供了一个独特的机会，使这种动态模拟更接近现实。这种观点界定了现场其他人的努力和您自己的工作的现状，并讨论了开放问题和未来的前景。

蛋白质 - 配体相互作用（PLIS）是生化研究的基础，其鉴定对于估计合理治疗设计的生物物理和生化特性至关重要。目前，这些特性的实验表征是最准确的方法，然而，这是非常耗时和劳动密集型的。在这种情况下已经开发了许多计算方法，但大多数现有PLI预测大量取决于2D蛋白质序列数据。在这里，我们提出了一种新颖的并行图形神经网络（GNN），以集成PLI预测的知识表示和推理，以便通过专家知识引导的深度学习，并通过3D结构数据通知。我们开发了两个不同的GNN架构，GNNF是采用不同特种的基础实现，以增强域名认识，而GNNP是一种新颖的实现，可以预测未经分子间相互作用的先验知识。综合评价证明，GNN可以成功地捕获配体和蛋白质3D结构之间的二元相互作用，对于GNNF的测试精度和0.958，用于预测蛋白质 - 配体络合物的活性。这些模型进一步适用于回归任务以预测实验结合亲和力，PIC50对于药物效力和功效至关重要。我们在实验亲和力上达到0.66和0.65的Pearson相关系数，分别在PIC50和GNNP上进行0.50和0.51，优于基于2D序列的模型。我们的方法可以作为可解释和解释的人工智能（AI）工具，用于预测活动，效力和铅候选的生物物理性质。为此，我们通过筛选大型复合库并将我们的预测与实验测量数据进行比较来展示GNNP对SARS-COV-2蛋白靶标的实用性。

A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.

Accurate determination of a small molecule candidate (ligand) binding pose in its target protein pocket is important for computer-aided drug discovery. Typical rigid-body docking methods ignore the pocket flexibility of protein, while the more accurate pose generation using molecular dynamics is hindered by slow protein dynamics. We develop a tiered tensor transform (3T) algorithm to rapidly generate diverse protein-ligand complex conformations for both pose and affinity estimation in drug screening, requiring neither machine learning training nor lengthy dynamics computation, while maintaining both coarse-grain-like coordinated protein dynamics and atomistic-level details of the complex pocket. The 3T conformation structures we generate are closer to experimental co-crystal structures than those generated by docking software, and more importantly achieve significantly higher accuracy in active ligand classification than traditional ensemble docking using hundreds of experimental protein conformations. 3T structure transformation is decoupled from the system physics, making future usage in other computational scientific domains possible.

准确的蛋白质结合亲和力预测在药物设计和许多其他分子识别问题中至关重要。尽管基于机器学习技术的亲和力预测取得了许多进步，但由于蛋白质 - 配体结合取决于原子和分子的动力学，它们仍然受到限制。为此，我们策划了一个包含3,218个动态蛋白质配合物的MD数据集，并进一步开发了DynaFormer，这是一个基于图的深度学习框架。 DynaFormer可以通过考虑相互作用的各种几何特征来完全捕获动态结合规则。我们的方法显示出优于迄今报告的方法。此外，我们通过将模型与基于结构的对接整合在一起，对热休克蛋白90（HSP90）进行了虚拟筛选。我们对其他基线进行了基准测试，表明我们的方法可以鉴定具有最高实验效力的分子。我们预计大规模的MD数据集和机器学习模型将形成新的协同作用，为加速药物发现和优化提供新的途径。

Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, as well as a low number of new drugs that are approved each year. To solve these problems, new and innovate technologies are needed that make the drug discovery process of small-molecules more time and cost-efficient, and which allow to target previously undruggable target classes such as protein-protein interactions. Structure-based virtual screenings have become a leading contender in this context. In this review, we give an introduction to the foundations of structure-based virtual screenings, and survey their progress in the past few years. We outline key principles, recent success stories, new methods, available software, and promising future research directions. Virtual screenings have an enormous potential for the development of new small-molecule drugs, and are already starting to transform early-stage drug discovery.

最近，基于深度神经网络（DNN）的药物 - 目标相互作用（DTI）模型以高精度突出显示，具有实惠的计算成本。然而，模型在硅药物发现的实践中仍然是一个具有挑战性的问题。我们提出了两项​​关键策略，以提高DTI模型的概括。首先是通过用神经网络参数化的物理通知方程来预测原子原子对相互作用，并提供蛋白质 - 配体复合物作为其总和的总结合亲和力。通过增强更广泛的绑定姿势和配体来培训数据，我们进一步改善了模型泛化。我们验证了我们的模型，PIGNET，在评分职能（CASF）2016的比较评估中，展示了比以前的方法更优于对接和筛选力。我们的物理信息策略还通过可视化配体副结构的贡献来解释预测的亲和力，为进一步配体优化提供了见解。

The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding. Recent advances have proved the power of language models (LMs) in processing the protein sequence databases, which inherit the advantages of attention networks and capture useful information in learning representations for proteins. The past two years have witnessed remarkable success in tertiary protein structure prediction (PSP), including evolution-based and single-sequence-based PSP. It seems that instead of using energy-based models and sampling procedures, protein language model (pLM)-based pipelines have emerged as mainstream paradigms in PSP. Despite the fruitful progress, the PSP community needs a systematic and up-to-date survey to help bridge the gap between LMs in the natural language processing (NLP) and PSP domains and introduce their methodologies, advancements and practical applications. To this end, in this paper, we first introduce the similarities between protein and human languages that allow LMs extended to pLMs, and applied to protein databases. Then, we systematically review recent advances in LMs and pLMs from the perspectives of network architectures, pre-training strategies, applications, and commonly-used protein databases. Next, different types of methods for PSP are discussed, particularly how the pLM-based architectures function in the process of protein folding. Finally, we identify challenges faced by the PSP community and foresee promising research directions along with the advances of pLMs. This survey aims to be a hands-on guide for researchers to understand PSP methods, develop pLMs and tackle challenging problems in this field for practical purposes.

Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from structure remains a major challenge. Here, we introduce Holographic Convolutional Neural Network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein function, including stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.

促性腺营养蛋白释放激素受体（GNRH1R）是治疗子宫疾病的有前途的治疗靶标。迄今为止，在临床研究中可以使用几个GNRH1R拮抗剂，而不满足多个财产约束。为了填补这一空白，我们旨在开发一个基于学习的框架，以促进有效，有效地发现具有理想特性的新的口服小型分子药物靶向GNRH1R。在目前的工作中，首先通过充分利用已知活性化合物和靶蛋白的结构的信息，首先提出了配体和结构组合模型，即LS-Molgen，首先提出了分子生成的方法，该信息通过其出色的性能证明了这一点。比分别基于配体或结构方法。然后，进行了A中的计算机筛选，包括活性预测，ADMET评估，分子对接和FEP计算，其中约30,000个生成的新型分子被缩小到8，以进行实验合成和验证。体外和体内实验表明，其中三个表现出有效的抑制活性（化合物5 IC50 = 0.856 nm，化合物6 IC50 = 0.901 nm，化合物7 IC50 = 2.54 nm对GNRH1R，并且化合物5在基本PK属性中表现良好例如半衰期，口服生物利用度和PPB等。我们认为，提议的配体和结构组合结合的分子生成模型和整个计算机辅助工作流程可能会扩展到从头开始的类似任务或铅优化的类似任务。

Protein structure prediction aims to determine the three-dimensional shape of a protein from its amino acid sequence 1 . This problem is of fundamental importance to biology as the structure of a protein largely determines its function 2 but can be hard to determine experimentally. In recent years, considerable progress has been made by leveraging genetic information: analysing the co-variation of homologous sequences can allow one to infer which amino acid residues are in contact, which in turn can aid structure prediction 3 . In this work, we show that we can train a neural network to accurately predict the distances between pairs of residues in a protein which convey more about structure than contact predictions. With this information we construct a potential of mean force 4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimised by a simple gradient descent algorithm, to realise structures without the need for complex sampling procedures.The resulting system, named AlphaFold, has been shown to achieve high accuracy, even for sequences with relatively few homologous sequences. In the most recent Critical Assessment of Protein Structure Prediction 5 (CASP13), a blind assessment of the state of the field of protein structure prediction, AlphaFold created high-accuracy structures (with TM-scores † of 0.7 or higher) for 24 out of 43 free modelling domains whereas the next best method, using sampling and contact information, achieved such accuracy for only 14 out of 43 domains.AlphaFold represents a significant advance in protein structure prediction. We expect the increased accuracy of structure predictions for proteins to enable insights in understanding the function and malfunction of these proteins, especially in cases where no homologous proteins have been experimentally determined 7 .Proteins are at the core of most biological processes. Since the function of a protein is dependent on its structure, understanding protein structure has been a grand challenge in biology for decades. While several experimental structure determination techniques have been developed

蛋白质复合物形成是生物学中的核心问题，参与了大部分细胞的过程，以及对应用是必不可少的，例如，药物设计或蛋白质工程。我们解决刚性体蛋白 - 蛋白质对接，即计算地预测来自个体未结合结构的蛋白质 - 蛋白质复合物的3D结构，假设在结合期间蛋白质内没有构象变化。我们设计一种新的成对独立的SE（3）-Quivariant的图形匹配网络，以预测旋转和翻译，以将其中一个蛋白质放置在右对接位置相对于第二蛋白质。我们在数学上保证了基本原理：无论两个结构的初始位置和方向如何，预测复合物都是相同的。我们的模型，名为Equidock，近似于绑定口袋并通过最佳传输和可分辨率的Kabsch算法实现，实现了使用关键点匹配和对准的对接姿势。凭经验，尽管没有依赖于沉重的候选抽样，结构细化或模板，我们才能实现显着的运行时间改进，并且通常优于现有的对接软件。

Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area. However, its efficacy is largely constrained due to the limited quantity of structural data. Meanwhile, protein language models trained on substantial 1D sequences have shown burgeoning capabilities with scale in a broad range of applications. Nevertheless, no preceding studies consider combining these different protein modalities to promote the representation power of geometric neural networks. To address this gap, we make the foremost step to integrate the knowledge learned by well-trained protein language models into several state-of-the-art geometric networks. Experiments are evaluated on a variety of protein representation learning benchmarks, including protein-protein interface prediction, model quality assessment, protein-protein rigid-body docking, and binding affinity prediction, leading to an overall improvement of 20% over baselines and the new state-of-the-art performance. Strong evidence indicates that the incorporation of protein language models' knowledge enhances geometric networks' capacity by a significant margin and can be generalized to complex tasks.

我们向高吞吐量基准介绍了用于材料和分子数据集的化学系统的多种表示的高吞吐量基准的机器学习（ML）框架。基准测试方法的指导原理是通过将模型复杂性限制在简单的回归方案的同时，在执行最佳ML实践的同时将模型复杂性限制为简单的回归方案，允许通过沿着同步的列车测试分裂的系列进行学习曲线来评估学习进度来评估原始描述符性能。结果模型旨在为未来方法开发提供通知的基线，旁边指示可以学习给定的数据集多么容易。通过对各种物理化学，拓扑和几何表示的培训结果的比较分析，我们介绍了这些陈述的相对优点以及它们的相互关联。