离子液体（ILS）是可持续过程的重要溶剂，并且需要预测IL中溶质的活性系数（AC）。最近，矩阵完成方法（MCM），变压器和图神经网络（GNN）在预测二元混合物的AC方面表现出很高的精度，例如宇宙RS和UNIFAC优于公认的模型。 GNN在这里特别有希望，因为他们学习了分子图到特性的关系，而无需预处理，通常是变压器所需的，并且与MCMS不同，适用于不包括训练中不包括的分子。但是，对于ILS，目前缺少GNN应用程序。在此，我们提出了一个GNN，以预测IL中溶质的温度依赖性无限稀释液。我们在包括40,000多个AC值的数据库上训练GNN，并将其与最先进的MCM进行比较。 GNN和MCM实现了类似的高预测性能，GNN还可以对培训期间未考虑的IL和溶质的AC进行高质量的预测。

The accurate prediction of physicochemical properties of chemical compounds in mixtures (such as the activity coefficient at infinite dilution $\gamma_{ij}^\infty$) is essential for developing novel and more sustainable chemical processes. In this work, we analyze the performance of previously-proposed GNN-based models for the prediction of $\gamma_{ij}^\infty$, and compare them with several mechanistic models in a series of 9 isothermal studies. Moreover, we develop the Gibbs-Helmholtz Graph Neural Network (GH-GNN) model for predicting $\ln \gamma_{ij}^\infty$ of molecular systems at different temperatures. Our method combines the simplicity of a Gibbs-Helmholtz-derived expression with a series of graph neural networks that incorporate explicit molecular and intermolecular descriptors for capturing dispersion and hydrogen bonding effects. We have trained this model using experimentally determined $\ln \gamma_{ij}^\infty$ data of 40,219 binary-systems involving 1032 solutes and 866 solvents, overall showing superior performance compared to the popular UNIFAC-Dortmund model. We analyze the performance of GH-GNN for continuous and discrete inter/extrapolation and give indications for the model's applicability domain and expected accuracy. In general, GH-GNN is able to produce accurate predictions for extrapolated binary-systems if at least 25 systems with the same combination of solute-solvent chemical classes are contained in the training set and a similarity indicator above 0.35 is also present. This model and its applicability domain recommendations have been made open-source at https://github.com/edgarsmdn/GH-GNN.

图形神经网络（GNN）正在化学工程中出现，以基于分子图的物理化学特性端到端学习。 GNNS的一个关键要素是合并函数，将原子矢量结合到分子指纹中。大多数以前的作品都使用标准池功能来预测各种属性。但是，不合适的合并功能会导致概括不佳的非物理GNN。我们根据有关学习特性的物理知识比较并选择有意义的GNN合并方法。通过量子机械计算计算出的分子特性证明了物理池函数的影响。我们还将结果与最近的SET2Set合并方法进行了比较。我们建议使用总和池来预测取决于分子大小的性能并比较分子大小无关的属性的池函数。总体而言，我们表明物理池功能的使用显着增强了概括。

机器学习（ML）已经证明了用于准确和结晶材料的准确性能预测的承诺。为了化学结构的高度精确的ML型号的化学结构属性预测，需要具有足够样品的数据集。然而，获得昂贵的化学性质的获得和充分数据可以是昂贵的令人昂贵的，这大大限制了ML模型的性能。通过计算机视觉和黑暗语言处理中数据增强的成功，我们开发了奥古里希姆：数据八级化图书馆化学结构。引入了弃头晶系统和分子的增强方法，其可以对基于指纹的ML模型和图形神经网络（GNNS）进行脱颖而出。我们表明，使用我们的增强策略意义地提高了ML模型的性能，特别是在使用GNNS时，我们开发的增强件在训练期间可以用作广告插件模块，并在用不同的GNN实施时证明了有效性。模型通过Theauglichem图书馆。基于Python的封装我们实现了EugliChem：用于化学结构的数据增强库，可公开获取：https：//github.com/baratilab/auglichem.1

对混合物相位平衡的知识在性质和技术化学本质上至关重要。混合物的相位平衡计算需要活性系数。但是，由于实验成本高，有关活性系数的实验数据通常受到限制。为了准确有效地预测活性系数，最近已经开发了机器学习方法。然而，对于未知分子的活性系数，当前的机器学习方法仍然很差。在这项工作中，我们介绍了一个自然语言处理网络的微笑到properties-Transformer（SPT），以预测微笑代码的二进制限制活动系数。为了克服可用实验数据的局限性，我们最初是在从COSMO-RS（1000万个数据点）采样的大型合成数据数据集上训练我们的网络，然后对实验数据（20 870个数据点）进行微调。该训练策略使SPT能够准确预测限制活动系数，即使对于未知分子，与最新的活动系数预测（例如COSMO-RS，UNIFAC）相比，将平均预测误差一半切成一半，并改善了最近的机器学习方法。

Ionic Liquids (ILs) provide a promising solution for CO$_2$ capture and storage to mitigate global warming. However, identifying and designing the high-capacity IL from the giant chemical space requires expensive, and exhaustive simulations and experiments. Machine learning (ML) can accelerate the process of searching for desirable ionic molecules through accurate and efficient property predictions in a data-driven manner. But existing descriptors and ML models for the ionic molecule suffer from the inefficient adaptation of molecular graph structure. Besides, few works have investigated the explainability of ML models to help understand the learned features that can guide the design of efficient ionic molecules. In this work, we develop both fingerprint-based ML models and Graph Neural Networks (GNNs) to predict the CO$_2$ absorption in ILs. Fingerprint works on graph structure at the feature extraction stage, while GNNs directly handle molecule structure in both the feature extraction and model prediction stage. We show that our method outperforms previous ML models by reaching a high accuracy (MAE of 0.0137, $R^2$ of 0.9884). Furthermore, we take the advantage of GNNs feature representation and develop a substructure-based explanation method that provides insight into how each chemical fragments within IL molecules contribute to the CO$_2$ absorption prediction of ML models. We also show that our explanation result agrees with some ground truth from the theoretical reaction mechanism of CO$_2$ absorption in ILs, which can advise on the design of novel and efficient functional ILs in the future.

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。

财产数据的可用性是化学过程开发中的主要瓶颈之一，通常需要耗时且昂贵的实验或将设计空间限制为少数已知分子。这种瓶颈一直是预测性财产模型持续发展的动机。对于新分子的性质预测，群体贡献方法一直在开创性。最近，机器学习加入了更具成熟的财产预测模型。但是，即使取得了最近的成功，将物理约束集成到机器学习模型中仍然具有挑战性。物理约束对于许多热力学特性，例如吉布斯 - 杜纳姆（Gibbs-Dunham）关系至关重要，它将额外的复杂性层引入预测中。在这里，我们介绍了SPT-NRTL，这是一种机器学习模型，以预测热力学一致的活动系数并提供NRTL参数，以便于过程模拟。结果表明，SPT-NRTL在所有官能团的活性系数预测中的精度高于UNIFAC，并且能够以几乎实验的精度预测许多蒸气 - 液位均衡性，如示例性混合物所示。 N-己烷。为了简化SPT-NRTL的应用，用SPT-NRTL计算了100 000 000的NRTL参数，并在线提供。

电子密度$ \ rho（\ vec {r}）$是用密度泛函理论（dft）计算地面能量的基本变量。除了总能量之外，$ \ rho（\ vec {r}）$分布和$ \ rho（\ vec {r}）$的功能通常用于捕获电子规模以功能材料和分子中的关键物理化学现象。方法提供对$ \ rho（\ vec {r}）的可紊乱系统，其具有少量计算成本的复杂无序系统可以是对材料相位空间的加快探索朝向具有更好功能的新材料的逆设计的游戏更换者。我们为预测$ \ rho（\ vec {r}）$。该模型基于成本图形神经网络，并且在作为消息传递图的一部分的特殊查询点顶点上预测了电子密度，但仅接收消息。该模型在多个数据组中进行测试，分子（QM9），液体乙烯碳酸酯电解质（EC）和Lixniymnzco（1-Y-Z）O 2锂离子电池阴极（NMC）。对于QM9分子，所提出的模型的准确性超过了从DFT获得的$ \ Rho（\ vec {r}）$中的典型变异性，以不同的交换相关功能，并显示超出最先进的准确性。混合氧化物（NMC）和电解质（EC）数据集更好的精度甚至更好。线性缩放模型同时探测成千上万点的能力允许计算$ \ Rho（\ vec {r}）$的大型复杂系统，比DFT快于允许筛选无序的功能材料。

Graph classification is an important area in both modern research and industry. Multiple applications, especially in chemistry and novel drug discovery, encourage rapid development of machine learning models in this area. To keep up with the pace of new research, proper experimental design, fair evaluation, and independent benchmarks are essential. Design of strong baselines is an indispensable element of such works. In this thesis, we explore multiple approaches to graph classification. We focus on Graph Neural Networks (GNNs), which emerged as a de facto standard deep learning technique for graph representation learning. Classical approaches, such as graph descriptors and molecular fingerprints, are also addressed. We design fair evaluation experimental protocol and choose proper datasets collection. This allows us to perform numerous experiments and rigorously analyze modern approaches. We arrive to many conclusions, which shed new light on performance and quality of novel algorithms. We investigate application of Jumping Knowledge GNN architecture to graph classification, which proves to be an efficient tool for improving base graph neural network architectures. Multiple improvements to baseline models are also proposed and experimentally verified, which constitutes an important contribution to the field of fair model comparison.

Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors, and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule.However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has

学习表达性分子表示对于促进分子特性的准确预测至关重要。尽管图形神经网络（GNNS）在分子表示学习中取得了显着进步，但它们通常面临诸如邻居探索，不足，过度光滑和过度阵列之类的局限性。同样，由于参数数量大，GNN通常具有较高的计算复杂性。通常，当面对相对大尺寸的图形或使用更深的GNN模型体系结构时，这种限制会出现或增加。克服这些问题的一个想法是将分子图简化为小型，丰富且有益的信息，这更有效，更具挑战性的培训GNN。为此，我们提出了一个新颖的分子图粗化框架，名为FUNQG利用函数组，作为分子的有影响力的构件来确定其性质，基于称为商图的图理论概念。通过实验，我们表明所产生的信息图比分子图小得多，因此是训练GNN的良好候选者。我们将FUNQG应用于流行的分子属性预测基准，然后比较所获得的数据集上的GNN体系结构的性能与原始数据集上的几个最先进的基线。通过实验，除了其参数数量和低计算复杂性的急剧减少之外，该方法除了其急剧减少之外，在各种数据集上的表现显着优于先前的基准。因此，FUNQG可以用作解决分子表示学习问题的简单，成本效益且可靠的方法。

基于深度学习的分子建模的最新进步令人兴奋地加速硅药发现。可获得血清的生成模型，构建原子原子和键合或逐片键的分子。然而，许多药物发现项目需要固定的支架以存在于所生成的分子中，并纳入该约束仅探讨了该约束。在这里，我们提出了一种基于图形的模型，其自然地支持支架作为生成过程的初始种子，这是可能的，因为它不调节在发电历史上。我们的实验表明，Moler与最先进的方法进行了相当的方法，在无约会的分子优化任务上，并且在基于脚手架的任务上优于它们，而不是比现有方法从培训和样本更快的数量级。此外，我们展示了许多看似小设计选择对整体性能的影响。

Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels.Recently, large scale quantum chemistry calculation and molecular dynamics simulations coupled with advances in high throughput experiments have begun to generate data at an unprecedented rate. Most classical techniques do not make effective use of the larger amounts of data that are now available. The time is ripe to apply more powerful and flexible machine learning methods to these problems, assuming we can find models with suitable inductive biases. The symmetries of atomic systems suggest neural networks that operate on graph structured data and are invariant to graph isomorphism might also be appropriate for molecules. Sufficiently successful models could someday help automate challenging chemical search problems in drug discovery or materials science.In this paper, our goal is to demonstrate effective machine learning models for chemical prediction problems

分子特性预测是与关键现实影响的深度学习的增长最快的应用之一。包括3D分子结构作为学习模型的输入可以提高它们对许多分子任务的性能。但是，此信息是不可行的，可以以几个现实世界应用程序所需的规模计算。我们建议预先训练模型，以推理仅给予其仅为2D分子图的分子的几何形状。使用来自自我监督学习的方法，我们最大化3D汇总向量和图形神经网络（GNN）的表示之间的相互信息，使得它们包含潜在的3D信息。在具有未知几何形状的分子上进行微调期间，GNN仍然产生隐式3D信息，并可以使用它来改善下游任务。我们表明3D预训练为广泛的性质提供了显着的改进，例如八个量子力学性能的22％的平均MAE。此外，可以在不同分子空间中的数据集之间有效地传送所学习的表示。

Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make datadriven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph-atoms, bonds, distances, etc.-which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

这项工作介绍了神经性等因素的外部潜力（NEQUIP），E（3） - 用于学习分子动力学模拟的AB-INITIO计算的用于学习网状体电位的e（3）的神经网络方法。虽然大多数当代对称的模型使用不变的卷曲，但仅在标量上采取行动，Nequip采用E（3） - 几何张量的相互作用，举起Quivariant卷曲，导致了更多的信息丰富和忠实的原子环境代表。该方法在挑战和多样化的分子和材料集中实现了最先进的准确性，同时表现出显着的数据效率。 Nequip优先于现有型号，最多三个数量级的培训数据，挑战深度神经网络需要大量培训套装。该方法的高数据效率允许使用高阶量子化学水平的理论作为参考的精确潜力构建，并且在长时间尺度上实现高保真分子动力学模拟。

通过定向消息传递通过方向消息通过的图形神经网络最近在多个分子特性预测任务上设置了最先进的技术。然而，它们依赖于通常不可用的原子位置信息，并获得它通常非常昂贵甚至不可能。在本文中，我们提出了合成坐标，使得能够使用高级GNN而不需要真正的分子配置。我们提出了两个距离作为合成坐标：使用个性化PageRank的对称变体指定分子配置的粗糙范围和基于图的距离的距离界限。为了利用距离和角度信息，我们提出了一种将正常图形神经网络转换为定向MPNN的方法。我们表明，通过这种转变，我们可以将正常图形神经网络的误差减少55％在锌基准。我们还通过在SMP和DimeNet ++模型中纳入合成坐标，在锌和自由QM9上设定了最新技术。我们的实现可在线获取。

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/.

Here, we demonstrate how machine learning enables the prediction of comonomers reactivity ratios based on the molecular structure of monomers. We combined multi-task learning, multi-inputs, and Graph Attention Network to build a model capable of predicting reactivity ratios based on the monomers chemical structures.