As a streaker flashed across the stage at the 1974 Oscars, the forever cheerful and charming co-host David Niven turned back to the audience and said, “Well ladies and gentleman, that was almost bound to happen…” Given the long history of efforts to predict properties of virtual molecules, and interest in Neural Nets in the 90’s, then Random Forest, “it was bound to happen” that Deep Neural Nets (DNN) would be applied to chemical data sets. Even less surprising was Bob Sheridan would be one of the first to publish.
The importance of encoding molecules in the right form (descriptors) rings true in these publications, as does the reliance on the quality (not quantity) of data. Equally pay attention to the amount of gain DNN provides over previous methods, we still have a way to travel.
The great volume of DNN papers current being submitted led us to select several papers – enjoy them all.
“Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships”
Ma, Sheridan, Liaw, Dahl & Svetnik J. Chem. Inf. Model. 2015, 55, 2, 263-274
“DeepTox: Toxicity Prediction using Deep Learning”
Mayr, Klambauer, Unterthiner & Hochreiter Frontiers in Env. Sci, 2016, 3, 2 – 15
“PotentialNet for Molecular Property Prediction”
Feinberg, Sur, Wu, Husic, Mai, Li, Sun, Yang, Ramsundar & Pande ACS Cent. Sci. 2018, 4, 1520−1530
A word a caution….think about the errors in any prediction. Frequently a new virtual compound requiring a prediction is out-of-domain, even for these new DNN models.
The Relative Importance of Domain Applicability Metrics for Estimating Prediction Errors in QSAR Varies with Training Set Diversity
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