A systematic survey and benchmark of four deep learning paradigms for molecular property prediction that organizes the field, critiques current data practices, and outlines three future directions.
Highly accurate quantum chemical property prediction with uni-mol+
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Farthest point sampling in property-designated chemical feature space produces more diverse training sets that improve predictive accuracy and reduce overfitting in ML models for small chemical datasets compared with random sampling.
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A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era
A systematic survey and benchmark of four deep learning paradigms for molecular property prediction that organizes the field, critiques current data practices, and outlines three future directions.
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Farthest Point Sampling in Property Designated Chemical Feature Space as a General Strategy for Enhancing the Machine Learning Model Performance for Small Scale Chemical Dataset
Farthest point sampling in property-designated chemical feature space produces more diverse training sets that improve predictive accuracy and reduce overfitting in ML models for small chemical datasets compared with random sampling.