Fragment Graphical Variational AutoEncoding for Screening Molecules with
Small Data
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by
John Armitage, Leszek J. Spalek, Malgorzata Nguyen, Mark Nikolka, Ian
E. Jacobs, Lorena Marañón, Iyad Nasrallah, Guillaume Schweicher, Ivan
Dimov, Dimitrios Simatos, Iain McCulloch, Christian B. Nielsen (+2 others)
2019
Abstract
In the majority of molecular optimization tasks, predictive machine learning
(ML) models are limited due to the unavailability and cost of generating big
experimental datasets on the specific task. To circumvent this limitation, ML
models are trained on big theoretical datasets or experimental indicators of
molecular suitability that are either publicly available or inexpensive to
acquire. These approaches produce a set of candidate molecules which have to be
ranked using limited experimental data or expert knowledge. Under the
assumption that structure is related to functionality, here we use a molecular
fragment-based graphical autoencoder to generate unique structural fingerprints
to efficiently search through the candidate set. We demonstrate that
fragment-based graphical autoencoding reduces the error in predicting physical
characteristics such as the solubility and partition coefficient in the small
data regime compared to other extended circular fingerprints and string based
approaches. We further demonstrate that this approach is capable of providing
insight into real world molecular optimization problems, such as searching for
stabilization additives in organic semiconductors by accurately predicting 92%
of test molecules given 69 training examples. This task is a model example of
black box molecular optimization as there is minimal theoretical and
experimental knowledge to accurately predict the suitability of the additives.
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