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Improving the use of deep convolutional neural networks for the prediction of molecular properties
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2128-7090
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0001-8884-2154
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2973-3112
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0001-7106-0025
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2019 (English)In: Practical Applications of Computational Biology and Bioinformatics, 12th International Conference / [ed] Florentino Fdez-Riverola, Mohd Saberi Mohamad, Miguel Rocha, Juan F. De Paz, Pascual González, Cham: Springer, 2019, Vol. 803, p. 71-79Conference paper, Published paper (Refereed)
Abstract [en]

We present a flexible deep convolutional neural network method for the analyse of arbitrary sized graph structures representing molecules. The method makes use of RDKit, an open-source cheminformatics software, allowing the incorporation of any global molecular (such as molecular charge) and local (such as atom type) information. We evaluate the method on the Side Effect Resource (SIDER) v4.1 dataset and show that it significantly outperforms another recently proposed method based on deep convolutional neural networks. We also reflect on how different types of information and input data affect the predictive power of our model. This reflection highlights several open problems that should be solved to further improve the use of deep learning within cheminformatics.

Place, publisher, year, edition, pages
Cham: Springer, 2019. Vol. 803, p. 71-79
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 803
Keywords [en]
drug discovery, graph convolutional neural network, molecular property prediction, bioinformatics, convolution, neural networks, open source software, open systems, cheminformatics, convolutional neural network, deep convolutional neural networks, graph structures, molecular charge, molecular properties, predictive power, deep neural networks
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
URN: urn:nbn:se:his:diva-16230DOI: 10.1007/978-3-319-98702-6_9ISI: 000468071900009Scopus ID: 2-s2.0-85052956812ISBN: 978-3-319-98701-9 (print)ISBN: 978-3-319-98702-6 (electronic)OAI: oai:DiVA.org:his-16230DiVA, id: diva2:1250816
Conference
PACBB2018: International Conference on Practical Applications of Computational Biology & Bioinformatics, Toledo, June 20-22, 2018
Available from: 2018-09-25 Created: 2018-09-25 Last updated: 2021-04-19Bibliographically approved
In thesis
1. Integrating domain knowledge into deep learning: Increasing model performance through human expertise
Open this publication in new window or tab >>Integrating domain knowledge into deep learning: Increasing model performance through human expertise
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The research in this thesis is focused on how deep learning models can be designed and implemented to better emulate and integrate the heuristic reasoning process of human experts. Several case studies, within the domains of steel making and drug discovery, are conducted in order to evaluate the performance of these models, in comparison to models that do not consider human expert knowledge from the targeted domain. These case studies focus on separate problems in targeted industries and, for example, deals with predictions of the outcome in the melting of steel and the rolling of steel sheets. This thesis also deals with property predictions of molecules and the generation of new drug candidates.

The research in this thesis addresses three main qustions. Firstly, the impact of different data representations and especially representations that are close to how human experts represent the addressed problem is studied. Secondly, focus is put on how the internal structure of data-driven algorithms can be designed to address problems in the same way as human experts. Finally, it is investigated how constraints, which are specifiedby experts, can be integrated into models to get models that generalise better and prevent non-feasible extrapolation. These questions are addressed in empirical case studies, where the model development is steered by knowledge about the problem provided by domain experts. In the presented cases, the developed algorithms performed significantly better, under the performance metric used, than in other commonly used machine learning models. Hence, this thesis shows the need for the involvement of domain experts in the design of models possessing high performance. This requires models that can both be applied to various data representations and be designed to compute and reason in multiple ways. Hence, the models must be flexible, so that they can be designed to reason in different ways and be applied to different data representations that correspond to the mental representations that is used by the human experts for the targeted problem.

The conclusion of this thesis is that such models can be designed using artificial neural networks, and that these models perform better than conventional machine learning models, when applied to new data. Such models can be designed so that they can be applied to complex representations of the data, representing the targeted problem better. More information regarding the problem can also be provided by designing the model in such a way that the reasoning of human experts is emulated. This can either bedone by emulating the expert behaviour in computational steps or by specifying known boundaries of the problem. The main conclusion is that models that are applied to datarepresentations that capture the real world problem in a good way, emulate the steps that human experts take to solve the problem, or know the limitations around the problem, have more information about the problem, and therefore, have an edge over other models, ultimately resulting in better performance.

Place, publisher, year, edition, pages
Skövde: Högskolan i Skövde, 2021
Series
Dissertation Series ; 39
National Category
Computer Sciences
Research subject
INF301 Data Science; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-19630 (URN)978-91-984919-3-7 (ISBN)
Public defence
2021-05-10, Insikten, Kaplansgatan 11 (Portalen), Skövde, 13:00 (English)
Opponent
Supervisors
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Publications; Low relevance

VIII. Niclas Ståhl, Göran Falkman, Gunnar Mathiason, and Alexander Karlsson (2018c). “A Self-Organizing Ensemble of Deep Neural Networks for the Classification of Data from Complex Processes.” In: International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. Springer, pp. 248–259.

IX. Juhee Bae, Yurong Li, Niclas Ståhl, Gunnar Mathiason, and Niklas Kojola (2020). “Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System.” In: Metallurgical and materials transactions. B, process metallurgy andmaterials processing science.

X. Juhee Bae, Alexander Karlsson, Jonas Mellin, Niclas Ståhl, and Vicenç Torra (2019). “Complex data analysis.” In: Data Science in Practice. Springer, pp. 157–169.

XI. Denio Duarte and Niclas Ståhl (2019). “Machine learning: a concise overview.” In: Data Science in Practice. Springer, pp. 27–58.

Available from: 2021-04-19 Created: 2021-04-16 Last updated: 2021-07-06Bibliographically approved

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Ståhl, NiclasFalkman, GöranKarlsson, AlexanderMathiason, Gunnar

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