支架结构的构建支持所需的基序，赋予蛋白质功能，显示出对疫苗和酶设计的希望。但是，解决这个主题交易问题的一般解决方案仍然开放。当前的脚手架设计的机器学习技术要么仅限于不切实际的小脚手架（长达20个长度），要么难以生产多种不同的脚手架。我们建议通过E（3） - 等级图神经网络学习各种蛋白质主链结构的分布。我们开发SMCDIFF以有效地从给定主题的条件下从该分布中采样脚手架；我们的算法是从理论上确保从扩散模型中的有条件样品，以大规模计算限制。我们通过与Alphafold2预测的结构保持一致的方式来评估我们设计的骨干。我们表明我们的方法可以（1）最多80个残基的样品支架，以及（2）实现固定基序的结构多样的支架。

这项工作引入了3D分子生成的扩散模型，该模型与欧几里得转化一样。我们的e（3）e象扩散模型（EDM）学会了通过均衡网络的扩散过程，该网络共同在连续（原子坐标）和分类特征（原子类型）上共同运行。此外，我们提供了一种概率分析，该分析使用我们的模型接受了分子的可能性计算。在实验上，所提出的方法显着优于先前关于生成样品质量和训练时效率的3D分子生成方法。

扩散模型是一类深入生成模型，在具有密集理论建立的各种任务上显示出令人印象深刻的结果。尽管与其他最先进的模型相比，扩散模型的样本合成质量和多样性令人印象深刻，但它们仍然遭受了昂贵的抽样程序和次优可能的估计。最近的研究表明，对提高扩散模型的性能的热情非常热情。在本文中，我们对扩散模型的现有变体进行了首次全面综述。具体而言，我们提供了扩散模型的第一个分类法，并将它们分类为三种类型，即采样加速增强，可能性最大化的增强和数据将来增强。我们还详细介绍了其他五个生成模型（即变异自动编码器，生成对抗网络，正常流量，自动回归模型和基于能量的模型），并阐明扩散模型与这些生成模型之间的连接。然后，我们对扩散模型的应用进行彻底研究，包括计算机视觉，自然语言处理，波形信号处理，多模式建模，分子图生成，时间序列建模和对抗性纯化。此外，我们提出了与这种生成模型的发展有关的新观点。

分子的产生，尤其是从头开始产生3D分子几何形状（即3D \ textit {de Novo} Generation）已成为药物设计中的一项基本任务。现有的基于扩散的3D分子生成方法可能会遭受性能不令人满意的性能，尤其是在产生大分子时。同时，产生的分子缺乏足够的多样性。本文提出了一个新的扩散模型，以应对这两个挑战。首先，原子关系不在分子的3D点云表示中。因此，现有生成模型很难捕获潜在的原子间力和丰富的局部约束。为了应对这一挑战，我们建议增强潜在的原子间力，并进一步涉及双重模棱两可的编码器，以编码不同强度的原子质力。其次，现有的基于扩散的模型基本上是沿数据密度梯度的几何元素。这样的过程在Langevin动力学的中间步骤中缺乏足够的探索。为了解决这个问题，我们在每个扩散/反向步骤中引入了一个分布控制变量，以实施彻底的探索并进一步改善发电多样性。对多个基准测试的广泛实验表明，所提出的模型明显优于无条件和条件生成任务的现有方法。我们还进行案例研究以帮助了解产生分子的理化特性。

深度学习表现出巨大的生成任务潜力。生成模型是可以根据某些隐含参数随机生成观测值的模型类。最近，扩散模型由于其发电能力而成为一类生成模型。如今，已经取得了巨大的成就。除了计算机视觉，语音产生，生物信息学和自然语言处理外，还需要在该领域探索更多应用。但是，扩散模型具有缓慢生成过程的自然缺点，从而导致许多增强的作品。该调查总结了扩散模型的领域。我们首先说明了两项具有里程碑意义的作品的主要问题-DDPM和DSM。然后，我们提供各种高级技术，以加快扩散模型 - 训练时间表，无训练采样，混合模型以及得分和扩散统一。关于现有模型，我们还根据特定的NFE提供了FID得分的基准和NLL。此外，引入了带有扩散模型的应用程序，包括计算机视觉，序列建模，音频和科学AI。最后，该领域以及局限性和进一步的方向都进行了摘要。

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.

Generating molecules that bind to specific proteins is an important but challenging task in drug discovery. Previous works usually generate atoms in an auto-regressive way, where element types and 3D coordinates of atoms are generated one by one. However, in real-world molecular systems, the interactions among atoms in an entire molecule are global, leading to the energy function pair-coupled among atoms. With such energy-based consideration, the modeling of probability should be based on joint distributions, rather than sequentially conditional ones. Thus, the unnatural sequentially auto-regressive modeling of molecule generation is likely to violate the physical rules, thus resulting in poor properties of the generated molecules. In this work, a generative diffusion model for molecular 3D structures based on target proteins as contextual constraints is established, at a full-atom level in a non-autoregressive way. Given a designated 3D protein binding site, our model learns the generative process that denoises both element types and 3D coordinates of an entire molecule, with an equivariant network. Experimentally, the proposed method shows competitive performance compared with prevailing works in terms of high affinity with proteins and appropriate molecule sizes as well as other drug properties such as drug-likeness of the generated molecules.

分子模拟的粗粒度（CG）通过将选定的原子分组为伪珠并大幅加速模拟来简化粒子的表示。但是，这种CG程序会导致信息损失，从而使准确的背景映射，即从CG坐标恢复细粒度（FG）坐标，这是一个长期存在的挑战。受生成模型和e象网络的最新进展的启发，我们提出了一个新型模型，该模型严格嵌入了背态转换的重要概率性质和几何一致性要求。我们的模型将FG的不确定性编码为不变的潜在空间，并通过Equivariant卷积将其解码为FG几何形状。为了标准化该领域的评估，我们根据分子动力学轨迹提供了三个综合基准。实验表明，我们的方法始终恢复更现实的结构，并以显着的边距胜过现有的数据驱动方法。

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。

这项工作引入了离题，这是一种用于生成具有分类节点和边缘属性图的图形的离散denoising扩散模型。我们的模型定义了一个扩散过程，该过程逐步编辑了具有噪声（添加或删除边缘，更改类别）的图形以及学会恢复此过程的图形变压器网络。有了这两种成分，我们将分布学习将上的分布学习减少到一个简单的分类任务序列。我们通过提出一个新的马尔可夫噪声模型来进一步提高样品质量，该模型在扩散过程中保留节点和边缘类型的边际分布，并通过在每个扩散步骤中添加从嘈杂图中得出的辅助图理论特征。最后，我们提出了一个指导程序，以根据图形级特征调理生成。总体而言，离题可以在分子和非分子数据集上达到最新性能，在平面图数据集上，有效性提高了3倍。特别是，这是第一个模型，将鳞片缩放到包含130万个药物样分子的大型鳄梨调子数据集，而无需使用分子特异性表示，例如微笑或片段。

高斯流程提供了一个优雅的框架，用于在功能上指定先验和后验分布。但是，它们在计算上也很昂贵，并且受其协方差函数的表达性限制。我们提出了基于扩散模型的新方法神经扩散过程（NDP），该方法学会了从功能上分布中采样。使用新颖的注意力块，我们可以将随机过程（例如交换性）的属性直接融合到NDP的体系结构中。我们从经验上表明，NDP能够捕获与高斯过程的真正贝叶斯后部接近的功能分布。这可以实现各种下游任务，包括高参数边缘化和贝叶斯优化。

Molecular dynamics (MD) has long been the de facto choice for simulating complex atomistic systems from first principles. Recently deep learning models become a popular way to accelerate MD. Notwithstanding, existing models depend on intermediate variables such as the potential energy or force fields to update atomic positions, which requires additional computations to perform back-propagation. To waive this requirement, we propose a novel model called DiffMD by directly estimating the gradient of the log density of molecular conformations. DiffMD relies on a score-based denoising diffusion generative model that perturbs the molecular structure with a conditional noise depending on atomic accelerations and treats conformations at previous timeframes as the prior distribution for sampling. Another challenge of modeling such a conformation generation process is that a molecule is kinetic instead of static, which no prior works have strictly studied. To solve this challenge, we propose an equivariant geometric Transformer as the score function in the diffusion process to calculate corresponding gradients. It incorporates the directions and velocities of atomic motions via 3D spherical Fourier-Bessel representations. With multiple architectural improvements, we outperform state-of-the-art baselines on MD17 and isomers of C7O2H10 datasets. This work contributes to accelerating material and drug discovery.

粗粒（CG）分子模拟已成为研究全原子模拟无法访问的时间和长度尺度上分子过程的标准工具。参数化CG力场以匹配全原子模拟，主要依赖于力匹配或相对熵最小化，这些熵最小化分别需要来自具有全原子或CG分辨率的昂贵模拟中的许多样本。在这里，我们提出了流量匹配，这是一种针对CG力场的新训练方法，它通过利用正常流量（一种生成的深度学习方法）来结合两种方法的优势。流量匹配首先训练标准化流程以表示CG概率密度，这等同于最小化相对熵而无需迭代CG模拟。随后，该流量根据学习分布生成样品和力，以通过力匹配来训练所需的CG能量模型。即使不需要全部原子模拟的力，流程匹配就数据效率的数量级优于经典力匹配，并产生CG模型，可以捕获小蛋白质的折叠和展开过渡。

计算抗体设计旨在自动创建与抗原结合的抗体。结合亲和力受3D结合界面的控制，其中抗体残基（角膜膜）与抗原残基（表位）紧密相互作用。因此，预测3D副观察复合物（对接）是找到最佳寄生虫的关键。在本文中，我们提出了一个新模型，称为层状码头和设计的名为层次层次的改进网络（HERN）。在对接过程中，Hern采用层次消息传递网络来预测原子力，并利用它们以迭代性，模棱两可的方式来完善结合复合物。在生成期间，其自动回解码器逐渐扩展了寄生虫，并构建了绑定界面的几何表示，以指导下一个残基选择。我们的结果表明，HERN在伞形对接和设计基准测试方面的先验最先进。

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.

Denoising diffusions are state-of-the-art generative models which exhibit remarkable empirical performance and come with theoretical guarantees. The core idea of these models is to progressively transform the empirical data distribution into a simple Gaussian distribution by adding noise using a diffusion. We obtain new samples whose distribution is close to the data distribution by simulating a "denoising" diffusion approximating the time reversal of this "noising" diffusion. This denoising diffusion relies on approximations of the logarithmic derivatives of the noised data densities, known as scores, obtained using score matching. Such models can be easily extended to perform approximate posterior simulation in high-dimensional scenarios where one can only sample from the prior and simulate synthetic observations from the likelihood. These methods have been primarily developed for data on $\mathbb{R}^d$ while extensions to more general spaces have been developed on a case-by-case basis. We propose here a general framework which not only unifies and generalizes this approach to a wide class of spaces but also leads to an original extension of score matching. We illustrate the resulting class of denoising Markov models on various applications.

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.

Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology. Previous methods usually first predict the interatomic distances, the gradients of interatomic distances or the local structures (e.g., torsion angles) of a molecule, and then reconstruct its 3D conformation. How to directly generate the conformation without the above intermediate values is not fully explored. In this work, we propose a method that directly predicts the coordinates of atoms: (1) the loss function is invariant to roto-translation of coordinates and permutation of symmetric atoms; (2) the newly proposed model adaptively aggregates the bond and atom information and iteratively refines the coordinates of the generated conformation. Our method achieves the best results on GEOM-QM9 and GEOM-Drugs datasets. Further analysis shows that our generated conformations have closer properties (e.g., HOMO-LUMO gap) with the groundtruth conformations. In addition, our method improves molecular docking by providing better initial conformations. All the results demonstrate the effectiveness of our method and the great potential of the direct approach. The code is released at https://github.com/DirectMolecularConfGen/DMCG

抗体设计对于治疗用法和生物学研究很有价值。现有的基于深度学习的方法遇到了几个关键问题：1）互补性区域（CDRS）生成的不完整上下文； 2）无法捕获输入结构的整个3D几何； 3）以自回归方式对CDR序列的效率低下。在本文中，我们提出了多通道等效的注意网络（平均值），这是一个能够共同设计1D序列和CDR的3D结构的端到端模型。要具体，平均值将抗体设计作为条件图翻译问题，通过导入包括靶抗原和抗体的轻链在内的额外组件。然后，平均诉诸于E（3） - 等级信息以及提出的注意机制，以更好地捕获不同组件之间的几何相关性。最后，它通过多轮渐进式完整射击方案来输出1D序列和3D结构，该方案在以前的自动回归方法上具有更高的效率。我们的方法显着超过了序列和结构建模，抗原结合抗体设计和结合亲和力优化的最新模型。具体而言，抗原结合CDR设计的相对改善约为22％，亲和力优化为34％。

RNA结构的确定和预测可以促进靶向RNA的药物开发和可用的共性元素设计。但是，由于RNA的固有结构灵活性，所有三种主流结构测定方法（X射线晶体学，NMR和Cryo-EM）在解决RNA结构时会遇到挑战，这导致已解决的RNA结构的稀缺性。计算预测方法作为实验技术的补充。但是，\ textit {de从头}的方法都不基于深度学习，因为可用的结构太少。取而代之的是，他们中的大多数采用了耗时的采样策略，而且它们的性能似乎达到了高原。在这项工作中，我们开发了第一种端到端的深度学习方法E2FOLD-3D，以准确执行\ textit {de de novo} RNA结构预测。提出了几个新的组件来克服数据稀缺性，例如完全不同的端到端管道，二级结构辅助自我鉴定和参数有效的骨干配方。此类设计在独立的，非重叠的RNA拼图测试数据集上进行了验证，并达到平均sub-4 \ aa {}根平方偏差，与最先进的方法相比，它表现出了优越的性能。有趣的是，它在预测RNA复杂结构时也可以取得令人鼓舞的结果，这是先前系统无法完成的壮举。当E2FOLD-3D与实验技术耦合时，RNA结构预测场可以大大提高。