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Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction
Graph neural networks have recently become a standard method for analysi...
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ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors
Timely assessment of compound toxicity is one of the biggest challenges ...
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Multitask Learning On Graph Neural Networks Applied To Molecular Property Predictions
Prediction of molecular properties, including physico-chemical propertie...
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ATOM3D: Tasks On Molecules in Three Dimensions
Computational methods that operate directly on three-dimensional molecul...
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Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks
Functional groups (FGs) serve as a foundation for analyzing chemical pro...
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Deep Learning Based Regression and Multi-class Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction
For quantitative structure-property relationship (QSPR) studies in chemo...
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Descriptor Selection via Self-Paced Learning for Bioactivity of Molecular Structure in QSAR Classification
Quantitative structure-activity relationship (QSAR) modelling is effecti...
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Are Learned Molecular Representations Ready For Prime Time?
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 rarely examined these new models in industry research settings in comparison to existing employed models. In this paper, we benchmark models extensively on 19 public and 15 proprietary industrial datasets spanning a wide variety of chemical endpoints. In addition, we introduce a graph convolutional model that consistently outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary datasets. Our empirical findings indicate that while approaches based on these representations have yet to reach the level of experimental reproducibility, our proposed model nevertheless offers significant improvements over models currently used in industrial workflows.
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