Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction

Elena L. Cáceres; Nicholas C. Mew; Michael J. Keiser

doi:10.1021/acs.jcim.0c00565

Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction

Journal of Chemical Information and Modeling ◽

10.1021/acs.jcim.0c00565 ◽

2020 ◽

Vol 60 (12) ◽

pp. 5957-5970

Author(s):

Elena L. Cáceres ◽

Nicholas C. Mew ◽

Michael J. Keiser

Keyword(s):

Machine Learning ◽

Bioactivity Prediction

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Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction

Journal of Computer-Aided Molecular Design ◽

10.1007/s10822-019-00274-0 ◽

2020 ◽

Vol 34 (7) ◽

pp. 717-730 ◽

Cited By ~ 9

Author(s):

Matthew C. Robinson ◽

Robert C. Glen ◽

Alpha A. Lee

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Numerical Experiments ◽

Large Scale ◽

Operating Characteristic ◽

Characteristic Curve ◽

Learning Models ◽

Bioactivity Prediction ◽

Operating Characteristic Curve ◽

Machine Learning Models

Abstract Machine learning methods may have the potential to significantly accelerate drug discovery. However, the increasing rate of new methodological approaches being published in the literature raises the fundamental question of how models should be benchmarked and validated. We reanalyze the data generated by a recently published large-scale comparison of machine learning models for bioactivity prediction and arrive at a somewhat different conclusion. We show that the performance of support vector machines is competitive with that of deep learning methods. Additionally, using a series of numerical experiments, we question the relevance of area under the receiver operating characteristic curve as a metric in virtual screening. We further suggest that area under the precision–recall curve should be used in conjunction with the receiver operating characteristic curve. Our numerical experiments also highlight challenges in estimating the uncertainty in model performance via scaffold-split nested cross validation.

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Adding stochastic negative examples into machine learning improves molecular bioactivity prediction

10.1101/2020.05.21.107748 ◽

2020 ◽

Cited By ~ 1

Author(s):

Elena L. Cáceres ◽

Nicholas C. Mew ◽

Michael J. Keiser

Keyword(s):

Machine Learning ◽

Drug Screening ◽

Drug Target ◽

Deep Neural Networks ◽

Bioactivity Prediction ◽

Screening Performance ◽

Classification And Regression ◽

Target Binding ◽

Test Scenarios ◽

Augmentation Procedure

ABSTRACTMultitask deep neural networks learn to predict ligand-target binding by example, yet public pharmacological datasets are sparse, imbalanced, and approximate. We constructed two hold-out benchmarks to approximate temporal and drug-screening test scenarios whose characteristics differ from a random split of conventional training datasets. We developed a pharmacological dataset augmentation procedure, Stochastic Negative Addition (SNA), that randomly assigns untested molecule-target pairs as transient negative examples during training. Under the SNA procedure, ligand drug-screening benchmark performance increases from R2 = 0.1926 ± 0.0186 to 0.4269±0.0272 (121.7%). This gain was accompanied by a modest decrease in the temporal benchmark (13.42%). SNA increases in drug-screening performance were consistent for classification and regression tasks and outperformed scrambled controls. Our results highlight where data and feature uncertainty may be problematic, but also show how leveraging uncertainty into training improves predictions of drug-target relationships.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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