Molecular shape and geometry dictate key biophysical recognition processes, yet many graph neural networks disregard 3D information for molecular property prediction. Here, we propose a new contrastive-learning procedure for graph neural networks, Molecular Contrastive Learning from Shape Similarity (MolCLaSS), that implicitly learns a three-dimensional representation. Rather than directly encoding or targeting three-dimensional poses, MolCLaSS matches a similarity objective based on Gaussian overlays to learn a meaningful representation of molecular shape. We demonstrate how this framework naturally captures key aspects of three-dimensionality that two-dimensional representations cannot and provides an inductive framework for scaffold hopping.

自我监督学习（SSL）是一种通过利用数据中固有的监督来学习数据表示的方法。这种学习方法是药物领域的焦点，由于耗时且昂贵的实验，缺乏带注释的数据。使用巨大未标记数据的SSL显示出在分子属性预测方面表现出色的性能，但存在一些问题。 （1）现有的SSL模型是大规模的；在计算资源不足的情况下实现SSL有限制。 （2）在大多数情况下，它们不利用3D结构信息进行分子表示学习。药物的活性与药物分子的结构密切相关。但是，大多数当前模型不使用3D信息或部分使用它。 （3）以前对分子进行对比学习的模型使用置换原子和键的增强。因此，具有不同特征的分子可以在相同的阳性样品中。我们提出了一个新颖的对比学习框架，用于分子属性预测的小规模3D图对比度学习（3DGCL），以解决上述问题。 3DGCL通过不改变药物语义的预训练过程来反映分子的结构来学习分子表示。仅使用1,128个样本用于预训练数据和100万个模型参数，我们在四个回归基准数据集中实现了最先进或可比性的性能。广泛的实验表明，基于化学知识的3D结构信息对于用于财产预测的分子表示学习至关重要。

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。

Models that accurately predict properties based on chemical structure are valuable tools in drug discovery. However, for many properties, public and private training sets are typically small, and it is difficult for the models to generalize well outside of the training data. Recently, large language models have addressed this problem by using self-supervised pretraining on large unlabeled datasets, followed by fine-tuning on smaller, labeled datasets. In this paper, we report MolE, a molecular foundation model that adapts the DeBERTa architecture to be used on molecular graphs together with a two-step pretraining strategy. The first step of pretraining is a self-supervised approach focused on learning chemical structures, and the second step is a massive multi-task approach to learn biological information. We show that fine-tuning pretrained MolE achieves state-of-the-art results on 9 of the 22 ADMET tasks included in the Therapeutic Data Commons.

Models based on machine learning can enable accurate and fast molecular property predictions, which is of interest in drug discovery and material design. Various supervised machine learning models have demonstrated promising performance, but the vast chemical space and the limited availability of property labels make supervised learning challenging. Recently, unsupervised transformer-based language models pretrained on a large unlabelled corpus have produced state-of-the-art results in many downstream natural language processing tasks. Inspired by this development, we present molecular embeddings obtained by training an efficient transformer encoder model, MoLFormer, which uses rotary positional embeddings. This model employs a linear attention mechanism, coupled with highly distributed training, on SMILES sequences of 1.1 billion unlabelled molecules from the PubChem and ZINC datasets. We show that the learned molecular representation outperforms existing baselines, including supervised and self-supervised graph neural networks and language models, on several downstream tasks from ten benchmark datasets. They perform competitively on two others. Further analyses, specifically through the lens of attention, demonstrate that MoLFormer trained on chemical SMILES indeed learns the spatial relationships between atoms within a molecule. These results provide encouraging evidence that large-scale molecular language models can capture sufficient chemical and structural information to predict various distinct molecular properties, including quantum-chemical properties.

没有标签的预处理分子表示模型是各种应用的基础。常规方法主要是处理2D分子图，并仅专注于2D任务，使其预验证的模型无法表征3D几何形状，因此对于下游3D任务有缺陷。在这项工作中，我们从完整而新颖的意义上处理了3D分子预处理。特别是，我们首先提议采用基于能量的模型作为预处理的骨干，该模型具有实现3D空间对称性的优点。然后，我们为力预测开发了节点级预处理损失，在此过程中，我们进一步利用了Riemann-Gaussian分布，以确保损失为E（3） - 不变，从而实现了更多的稳健性。此外，还利用了图形噪声量表预测任务，以进一步促进最终的性能。我们评估了从两个具有挑战性的3D基准：MD17和QM9的大规模3D数据集GEOM-QM9预测的模型。实验结果支持我们方法对当前最新预处理方法的更好疗效，并验证我们设计的有效性。

分子表示学习（MRL）是建立机器学习与化学科学之间联系的关键步骤。特别是，它将分子编码为保留分子结构和特征的数值向量，在其上可以执行下游任务（例如，属性预测）。最近，MRL取得了相当大的进步，尤其是在基于深的分子图学习方法中。在这项调查中，我们系统地回顾了这些基于图的分子表示技术。具体而言，我们首先介绍2D和3D图分子数据集的数据和功能。然后，我们总结了专门为MRL设计的方法，并将其分为四种策略。此外，我们讨论了MRL支持的一些典型化学应用。为了促进该快速发展领域的研究，我们还列出了论文中的基准和常用数据集。最后，我们分享我们对未来研究方向的想法。

分子表示学习有助于多个下游任务，例如分子性质预测和药物设计。为了适当地代表分子，图形对比学习是一个有前途的范式，因为它利用自我监督信号并没有人类注释要求。但是，先前的作品未能将基本域名知识纳入图表语义，因此忽略了具有共同属性的原子之间的相关性，但不通过键连接连接。为了解决这些问题，我们构建化学元素知识图（KG），总结元素之间的微观关联，并提出了一种用于分子代表学习的新颖知识增强的对比学习（KCL）框架。 KCL框架由三个模块组成。第一个模块，知识引导的图形增强，基于化学元素kg增强原始分子图。第二模块，知识意识的图形表示，利用用于原始分子图的公共曲线图编码器和通过神经网络（KMPNN）的知识感知消息来提取分子表示来编码增强分子图中的复杂信息。最终模块是一种对比目标，在那里我们在分子图的这两个视图之间最大化协议。广泛的实验表明，KCL获得了八个分子数据集上的最先进基线的优异性能。可视化实验适当地解释了在增强分子图中从原子和属性中了解的KCL。我们的代码和数据可用于补充材料。

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。

我们可以将袖珍配体的相互作用知识注入预训练的模型并共同学习其化学空间吗？近年来，预处理的分子和蛋白质引起了很大的关注，而这些方法中的大多数都集中在学习一个化学空间，并且缺乏注射生物学知识。我们提出一个共同监督预告片（COSP）的框架，以同时学习3D口袋和配体表示。我们使用封闭式的几何消息传递层来对3D口袋和配体进行建模，其中每个节点的化学特征，几何位置和方向都被考虑。为了学习生物学有意义的嵌入，我们通过对比度损失将袖珍配体相互作用知识注入预处理模型。考虑到分子的特异性，我们进一步提出了化学相似性增强的负抽样策略，以提高对比度学习绩效。通过广泛的实验，我们得出的结论是，COSP可以在口袋匹配，分子属性预测和虚拟筛选中获得竞争成果。

Molecular representation learning is crucial for the problem of molecular property prediction, where graph neural networks (GNNs) serve as an effective solution due to their structure modeling capabilities. Since labeled data is often scarce and expensive to obtain, it is a great challenge for GNNs to generalize in the extensive molecular space. Recently, the training paradigm of "pre-train, fine-tune" has been leveraged to improve the generalization capabilities of GNNs. It uses self-supervised information to pre-train the GNN, and then performs fine-tuning to optimize the downstream task with just a few labels. However, pre-training does not always yield statistically significant improvement, especially for self-supervised learning with random structural masking. In fact, the molecular structure is characterized by motif subgraphs, which are frequently occurring and influence molecular properties. To leverage the task-related motifs, we propose a novel paradigm of "pre-train, prompt, fine-tune" for molecular representation learning, named molecule continuous prompt tuning (MolCPT). MolCPT defines a motif prompting function that uses the pre-trained model to project the standalone input into an expressive prompt. The prompt effectively augments the molecular graph with meaningful motifs in the continuous representation space; this provides more structural patterns to aid the downstream classifier in identifying molecular properties. Extensive experiments on several benchmark datasets show that MolCPT efficiently generalizes pre-trained GNNs for molecular property prediction, with or without a few fine-tuning steps.

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

DNA-Encoded Library (DEL) technology has enabled significant advances in hit identification by enabling efficient testing of combinatorially-generated molecular libraries. DEL screens measure protein binding affinity though sequencing reads of molecules tagged with unique DNA-barcodes that survive a series of selection experiments. Computational models have been deployed to learn the latent binding affinities that are correlated to the sequenced count data; however, this correlation is often obfuscated by various sources of noise introduced in its complicated data-generation process. In order to denoise DEL count data and screen for molecules with good binding affinity, computational models require the correct assumptions in their modeling structure to capture the correct signals underlying the data. Recent advances in DEL models have focused on probabilistic formulations of count data, but existing approaches have thus far been limited to only utilizing 2-D molecule-level representations. We introduce a new paradigm, DEL-Dock, that combines ligand-based descriptors with 3-D spatial information from docked protein-ligand complexes. 3-D spatial information allows our model to learn over the actual binding modality rather than using only structured-based information of the ligand. We show that our model is capable of effectively denoising DEL count data to predict molecule enrichment scores that are better correlated with experimental binding affinity measurements compared to prior works. Moreover, by learning over a collection of docked poses we demonstrate that our model, trained only on DEL data, implicitly learns to perform good docking pose selection without requiring external supervision from expensive-to-source protein crystal structures.

由于它们在元素之间代表复杂互动的能力，变压器已成为许多应用中的选择方法。然而，将变压器架构扩展到非顺序数据，例如分子，并使其对小型数据集的训练仍然是一个挑战。在这项工作中，我们引入了一种用于分子性能预测的基于变压器的架构，其能够捕获分子的几何形状。我们通过分子几何形状的初始编码来修改经典位置编码器，以及学习的门控自我关注机制。我们进一步提出了一种增强方案，用于避免通过过次分辨率的架构引起的过度拟合的分子数据。所提出的框架优于最先进的方法，同时仅基于纯机器学习，即，即该方法不包含量子化学的域知识，并且在成对原子距离旁边没有使用延伸的几何输入。

In this work, we propose MEDICO, a Multi-viEw Deep generative model for molecule generation, structural optimization, and the SARS-CoV-2 Inhibitor disCOvery. To the best of our knowledge, MEDICO is the first-of-this-kind graph generative model that can generate molecular graphs similar to the structure of targeted molecules, with a multi-view representation learning framework to sufficiently and adaptively learn comprehensive structural semantics from targeted molecular topology and geometry. We show that our MEDICO significantly outperforms the state-of-the-art methods in generating valid, unique, and novel molecules under benchmarking comparisons. In particular, we showcase the multi-view deep learning model enables us to generate not only the molecules structurally similar to the targeted molecules but also the molecules with desired chemical properties, demonstrating the strong capability of our model in exploring the chemical space deeply. Moreover, case study results on targeted molecule generation for the SARS-CoV-2 main protease (Mpro) show that by integrating molecule docking into our model as chemical priori, we successfully generate new small molecules with desired drug-like properties for the Mpro, potentially accelerating the de novo design of Covid-19 drugs. Further, we apply MEDICO to the structural optimization of three well-known Mpro inhibitors (N3, 11a, and GC376) and achieve ~88% improvement in their binding affinity to Mpro, demonstrating the application value of our model for the development of therapeutics for SARS-CoV-2 infection.

学习有效的蛋白质表示在生物学的各种任务中至关重要，例如预测蛋白质功能或结构。现有的方法通常在大量未标记的氨基酸序列上预先蛋白质语言模型，然后在下游任务中使用一些标记的数据来对模型进行修复。尽管基于序列的方法具有有效性，但尚未探索蛋白质性能预测的已知蛋白质结构的预处理功能，尽管蛋白质结构已知是蛋白质功能的决定因素，但尚未探索。在本文中，我们建议根据其3D结构预处理蛋白质。我们首先提出一个简单而有效的编码器，以学习蛋白质的几何特征。我们通过利用多视图对比学习和不同的自我预测任务来预先蛋白质图编码器。对功能预测和折叠分类任务的实验结果表明，我们提出的预处理方法表现优于或与最新的基于最新的序列方法相提并论，同时使用较少的数据。我们的实施可在https://github.com/deepgraphlearning/gearnet上获得。

图表自学学习（GSSL）铺平了无需专家注释的学习图嵌入的方式，这对分子图特别有影响，因为可能的分子数量很大，并且标签昂贵。但是，通过设计，GSSL方法没有经过训练，可以在一个下游任务上表现良好，而是旨在将其转移到许多人方面，从而使评估不那么直接。作为获得具有多种多样且可解释属性的分子图嵌入曲线的一步，我们引入了分子图表示评估（Molgrapheval），这是一组探针任务，分为（i）拓扑 - ，（ii）子结构 - 和（iii）和（iii）嵌入空间属性。通过对现有下游数据集和Molgrapheval上的现有GSSL方法进行基准测试，我们发现单独从现有数据集中得出的结论与更细粒度的探测之间存在令人惊讶的差异，这表明当前的评估协议没有提供整个图片。我们的模块化，自动化的端到端GSSL管道代码将在接受后发布，包括标准化的图形加载，实验管理和嵌入评估。

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.

最近，在对图形结构数据上应用深度神经网络有很大的成功。然而，大多数工作侧重于节点或图形级监督学习，例如节点，链接或图形分类或节点级无监督学习（例如节点群集）。尽管其应用广泛，但图表级无监督的学习尚未受到很多关注。这可能主要归因于图形的高表示复杂性，可以由n表示！等效邻接矩阵，其中n是节点的数量。在这项工作中，我们通过提出用于图形结构数据的置换不变变化自动码器来解决此问题。我们所提出的模型间接学习以匹配输入和输出图的节点排序，而不施加特定节点排序或执行昂贵的图形匹配。我们展示了我们提出模型对各种图形重建和生成任务的有效性，并评估了下游图形水平分类和回归提取的表示的表现力。

人工智能（AI）在过去十年中一直在改变药物发现的实践。各种AI技术已在广泛的应用中使用，例如虚拟筛选和药物设计。在本调查中，我们首先概述了药物发现，并讨论了相关的应用，可以减少到两个主要任务，即分子性质预测和分子产生。然后，我们讨论常见的数据资源，分子表示和基准平台。此外，为了总结AI在药物发现中的进展情况，我们介绍了在调查的论文中包括模型架构和学习范式的相关AI技术。我们预计本调查将作为有兴趣在人工智能和药物发现界面工作的研究人员的指南。我们还提供了GitHub存储库（HTTPS:///github.com/dengjianyuan/survey_survey_au_drug_discovery），其中包含文件和代码，如适用，作为定期更新的学习资源。