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Open AccessArticle

How Sure Can We Be about ML Methods-Based Evaluation of Compound Activity: Incorporation of Information about Prediction Uncertainty Using Deep Learning Techniques

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Faculty of Mathematics and Computer Science, Jagiellonian University, 6 S. Łojasiewicza Street, 30-348 Cracow, Poland
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Department of Technology and Biotechnology of Drugs, Jagiellonian University, Medical College, 9 Medyczna Street, 30-688 Cracow, Poland
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Maj Institute of Pharmacology, 12 Smętna Street, 31-343 Cracow, Poland
*
Author to whom correspondence should be addressed.
Academic Editors: Igor F. Tsigelny and Bernard Maigret
Molecules 2020, 25(6), 1452; https://doi.org/10.3390/molecules25061452
Received: 27 December 2019 / Revised: 28 February 2020 / Accepted: 22 March 2020 / Published: 23 March 2020
(This article belongs to the Special Issue AI in Drug Design)
A great variety of computational approaches support drug design processes, helping in selection of new potentially active compounds, and optimization of their physicochemical and ADMET properties. Machine learning is a group of methods that are able to evaluate in relatively short time enormous amounts of data. However, the quality of machine-learning-based prediction depends on the data supplied for model training. In this study, we used deep neural networks for the task of compound activity prediction and developed dropout-based approaches for estimating prediction uncertainty. Several types of analyses were performed: the relationships between the prediction error, similarity to the training set, prediction uncertainty, number and standard deviation of activity values were examined. It was tested whether incorporation of information about prediction uncertainty influences compounds ranking based on predicted activity and prediction uncertainty was used to search for the potential errors in the ChEMBL database. The obtained outcome indicates that incorporation of information about uncertainty of compound activity prediction can be of great help during virtual screening experiments. View Full-Text
Keywords: machine learning; prediction uncertainty; deep learning; ligands; ChEMBL database machine learning; prediction uncertainty; deep learning; ligands; ChEMBL database
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Sieradzki, I.; Leśniak, D.; Podlewska, S. How Sure Can We Be about ML Methods-Based Evaluation of Compound Activity: Incorporation of Information about Prediction Uncertainty Using Deep Learning Techniques. Molecules 2020, 25, 1452.

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