HiGNN: A Hierarchical Informative Graph Neural Network for Molecular Property Prediction Equipped with Feature-Wise Attention

Zhu, Weimin; Zhang, Yi; Zhao, Duancheng; Xu, Jianrong; Wang, Ling

doi:10.1021/acs.jcim.2c01099

Cited by 32 publications

(28 citation statements)

References 56 publications

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“…The Chiral InterRoto-Invariant Neural Network (ChIRo) was designed to enable property predictions that depend upon a molecule’s chirality . The hierarchical informative GNN (HiGNN) represents another framework for predicting diverse molecular properties, whose use was demonstrated for a range of physiochemical, biophysics, physiology, and toxicity observables …”

Section: A Foray Into Additional Topicsmentioning

confidence: 99%

“…369 The hierarchical informative GNN (HiGNN) represents another framework for predicting diverse molecular properties, whose use was demonstrated for a range of physiochemical, biophysics, physiology, and toxicity observables. 370 Miscellaneous. To help identify chemical and structural reasons for why molecules predicted by ML models satisfy certain properties, the algorithm exmol was developed; 371 it finds counterfactuals and is generalizable to any ML model.…”

Section: A Foray Into Additional Topicsmentioning

confidence: 99%

See 1 more Smart Citation

Open-Source Machine Learning in Computational Chemistry

Hagg

2023

J. Chem. Inf. Model.

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The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.

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Section: A Foray Into Additional Topicsmentioning

confidence: 99%

Section: A Foray Into Additional Topicsmentioning

confidence: 99%

Open-Source Machine Learning in Computational Chemistry

Hagg

2023

J. Chem. Inf. Model.

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show abstract

“…This algorithm can restrict the substructures containing different root atom ranges according to different tasks to improve the efficiency of the model. Zhu et al 140 utilized a molecular-fragment similarity mechanism algorithm to analyze the interpretation of the neural network at the subgraph level. However, to make a reasonable interpretation, unrealistic assumptions are sometimes introduced to facilitate theoretical analysis, which compromise the validity of the interpretation.…”

Section: Interpretability Modeling Reinforces Practical Valuementioning

confidence: 99%

A Systematic Review on Intensifications of Artificial Intelligence Assisted Green Solvent Development

Wen,

Nan,

et al. 2023

Ind. Eng. Chem. Res.

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Solvents are indispensable components of chemical processes, and the application of ecofriendly, safe, and efficient solvents is vital for building green chemical processes. Nowadays, new techniques have been applied in discovering and exploring green solvents, among which artificial intelligence (AI) plays an increasingly important role in predicting their physical and chemical properties. Being able to explore the chemical space of green solvents, AI can also be utilized to screen out expected solvents and inversely design new solvents. This review introduces the application of AI assisted green solvent design, focusing on intensification techniques in the processes of green solvent design and property prediction. First, the various intensification techniques of quantitative structure−property relationships (QSPR) employed in the process of solvent property prediction are summarized, including the optimization and intensification of feature extraction, ensemble learning, uncertainty analysis, and interpretability modeling. After that, the basic principles and latest theoretical advances in the application of inverse molecular design for green solvents are reviewed, including high-throughput screening, computer-aided molecular design (CAMD), and deep generative models. Finally, new ideas are proposed for the improvement of each intensification technique in order to better match the high demands of the particular application in green solvent design.

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“…Recent studies have emphasized the crucial role of microflora in regulating drug activity and side effects [53]. So, it is a key issue to treat diseases by modulating the activity and toxicity of drugs in terms of microbes [54]. For example, Immune Checkpoint Inhibitors(ICIs) are drugs used to treat cancer.…”

Section: Introductionmentioning

confidence: 99%

MDMD: a computational model for predicting drug-related microbes based on the aggregated metapaths from a heterogeneous network

Xing,

Zhang,

Wang

2023

Preprint

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Clinical studies have shown that microbes are closely related to the occurrence of diseases in the human body. It is beneficial for treating diseases by means of microbes to modulate the activity and toxicity of drugs. Therefore, it is significant in predicting associations between drugs and microbes. Recently, there are several computational models for addressing the issue. However, most of them only focus on drug-related microbes and neglect related diseases, which can lead to insufficient training. Here we introduce a new model (called MDMD) is proposed to predict drug-related microbes based on the Metapaths from a heterogeneous network constructed by using the data of Diseases, Microbes, Drugs, the associations of microbe-disease and disease-drug. The MDMD uses an aggregation of the metapath features that can effectively abundance the embedding of the features for different types of nodes and edges in the heterogeneous networks. Then, the MDMD uses the attention mechanism to mark the importance of the metapath vector for each node type which can improve the quality of feature embedding. Experimental results demonstrate that the MDMD improves accuracy by 1.9% compared with other models. The MDMD is also used to predict the microbes of two drugs Lamivudine and Tenofovir which are the antiretroviral drugs used to treat the Acquired Immune Deficiency Syndrome(AIDS). The results show that 90-95% of microbes are reported in the PubMed. Mycobacterium tuberculosis(Mtb) is a specific microbe only predicted by the MDMD. An online platform of the MDMD is available in https://mdmd2023.bit1024.top/, in which the source code of the MDMD and the data in the work can be downloaded.

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HiGNN: A Hierarchical Informative Graph Neural Network for Molecular Property Prediction Equipped with Feature-Wise Attention

Cited by 32 publications

References 56 publications

Open-Source Machine Learning in Computational Chemistry

Open-Source Machine Learning in Computational Chemistry

A Systematic Review on Intensifications of Artificial Intelligence Assisted Green Solvent Development

MDMD: a computational model for predicting drug-related microbes based on the aggregated metapaths from a heterogeneous network

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