人工智能(AI)已被广泛应用于药物发现中,其主要任务是分子财产预测。尽管分子表示学习中AI技术的繁荣,但尚未仔细检查分子性质预测的一些关键方面。在这项研究中,我们对三个代表性模型,即随机森林,莫尔伯特和格罗弗进行了系统比较,该模型分别利用了三个主要的分子表示,扩展连接的指纹,微笑的字符串和分子图。值得注意的是,莫尔伯特(Molbert)和格罗弗(Grover)以自我监督的方式在大规模的无标记分子库中进行了预定。除了常用的分子基准数据集外,我们还组装了一套与阿片类药物相关的数据集进行下游预测评估。我们首先对标签分布和结构分析进行了数据集分析;我们还检查了阿片类药物相关数据集中的活动悬崖问题。然后,我们培训了4,320个预测模型,并评估了学习表示的有用性。此外,我们通过研究统计测试,评估指标和任务设置的效果来探索模型评估。最后,我们将化学空间的概括分解为施加间和支柱内的概括,并测量了预测性能,以评估两种设置下模型的普遍性。通过采取这种喘息,我们反映了分子财产预测的基本关键方面,希望在该领域带来更好的AI技术的意识。
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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。
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Molecular machine learning has been maturing rapidly over the last few years.Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem
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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.
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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。
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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.
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在过去的十年中,AI AID毒品发现(AIDD)的计算方法和数据集策划的繁荣发展。但是,现实世界中的药物数据集经常表现出高度不平衡的分布,这在很大程度上被当前的文献忽略了,但可能会严重损害机器学习应用程序的公平性和概括。在这一观察结果的激励下,我们介绍了Imdrug,这是一个全面的基准标准,其开源python库由4个不平衡设置,11个AI-Ready数据集,54个学习任务和16种为不平衡学习量身定制的基线算法。它为涵盖广泛的药物发现管道(例如分子建模,药物靶标相互作用和逆合合成)的问题和解决方案提供了可访问且可定制的测试床。我们通过新的评估指标进行广泛的实证研究,以证明现有算法在数据不平衡情况下无法解决药物和药物挑战。我们认为,Imdrug为未来的研究和发展开辟了途径,在AIDD和深度不平衡学习的交集中对现实世界中的挑战开辟了道路。
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阐明并准确预测分子的吸毒性和生物活性在药物设计和发现中起关键作用,并且仍然是一个开放的挑战。最近,图神经网络(GNN)在基于图的分子属性预测方面取得了显着进步。但是,当前基于图的深度学习方法忽略了分子的分层信息以及特征通道之间的关系。在这项研究中,我们提出了一个精心设计的分层信息图神经网络框架(称为hignn),用于通过利用分子图和化学合成的可见的无限元素片段来预测分子特性。此外,首先在Hignn体系结构中设计了一个插件功能的注意块,以适应消息传递阶段后自适应重新校准原子特征。广泛的实验表明,Hignn在许多具有挑战性的药物发现相关基准数据集上实现了最先进的预测性能。此外,我们设计了一种分子碎片的相似性机制,以全面研究Hignn模型在子图水平上的解释性,表明Hignn作为强大的深度学习工具可以帮助化学家和药剂师识别出设计更好分子的关键分子,以设计更好的分子,以设计出所需的更好分子。属性或功能。源代码可在https://github.com/idruglab/hignn上公开获得。
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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
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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。
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Deep learning models that leverage large datasets are often the state of the art for modelling molecular properties. When the datasets are smaller (< 2000 molecules), it is not clear that deep learning approaches are the right modelling tool. In this work we perform an extensive study of the calibration and generalizability of probabilistic machine learning models on small chemical datasets. Using different molecular representations and models, we analyse the quality of their predictions and uncertainties in a variety of tasks (binary, regression) and datasets. We also introduce two simulated experiments that evaluate their performance: (1) Bayesian optimization guided molecular design, (2) inference on out-of-distribution data via ablated cluster splits. We offer practical insights into model and feature choice for modelling small chemical datasets, a common scenario in new chemical experiments. We have packaged our analysis into the DIONYSUS repository, which is open sourced to aid in reproducibility and extension to new datasets.
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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。
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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.
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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。
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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.
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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.
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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
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预测药物目标相互作用是药物发现的关键。最近基于深度学习的方法显示出令人鼓舞的表现,但仍有两个挑战:(i)如何明确建模并学习药物与目标之间的局部互动,以更好地预测和解释; (ii)如何从不同分布的新型药物目标对上概括预测性能。在这项工作中,我们提出了Dugban,这是一个深层双线性注意网络(BAN)框架,并适应了域的适应性,以明确学习药物与目标之间的配对局部相互作用,并适应了分布数据外的数据。 Dugban在药物分子图和靶蛋白序列上进行预测的作品,有条件结构域对抗性学习,以使跨不同分布的学习相互作用表示,以更好地对新型药物目标对进行更好的概括。在内域和跨域设置下,在三个基准数据集上进行的实验表明,对于五个最先进的基准,Dugban取得了最佳的总体表现。此外,可视化学习的双线性注意图图提供了可解释的见解,从预测结果中提供了可解释的见解。
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使用图神经网络(GNN)提取分子的信息表示,对于AI驱动的药物发现至关重要。最近,图形研究界一直在试图复制自然语言处理预处理的成功,并获得了一些成功。但是,我们发现在许多情况下,自我监督预审计对分子数据的益处可以忽略不计。我们对GNN预处理的关键组成部分进行了彻底的消融研究,包括预处理目标,数据拆分方法,输入特征,预处理数据集量表和GNN体系结构,以决定下游任务的准确性。我们的第一个重要发现是,在许多情况下,自我监督的图表预处理没有统计学上的显着优势。其次,尽管可以通过额外的监督预处理可以观察到改进,但通过更丰富或更平衡的数据拆分,改进可能会减少。第三,实验性超参数对下游任务的准确性具有更大的影响,而不是训练训练的任务。我们假设对分子进行预训练的复杂性不足,从而导致下游任务的可转移知识较低。
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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。
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