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Evaluating parameters for ligand-based modeling with random forest on sparse data sets
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0002-8083-2864
2018 (English)In: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 10, article id 49Article in journal (Refereed) Published
Abstract [en]

Ligand-based predictive modeling is widely used to generate predictive models aiding decision making in e.g. drug discovery projects. With growing data sets and requirements on low modeling time comes the necessity to analyze data sets efficiently to support rapid and robust modeling. In this study we analyzed four data sets and studied the efficiency of machine learning methods on sparse data structures, utilizing Morgan fingerprints of different radii and hash sizes, and compared with molecular signatures descriptor of different height. We specifically evaluated the effect these parameters had on modeling time, predictive performance, and memory requirements using two implementations of random forest; Scikit-learn as well as FEST. We also compared with a support vector machine implementation. Our results showed that unhashed fingerprints yield significantly better accuracy than hashed fingerprints (p <= 0.05), with no pronounced deterioration in modeling time and memory usage. Furthermore, the fast execution and low memory usage of the FEST algorithm suggest that it is a good alternative for large, high dimensional sparse data. Both support vector machines and random forest performed equally well but results indicate that the support vector machine was better at using the extra information from larger values of the Morgan fingerprint's radius.

Place, publisher, year, edition, pages
BMC , 2018. Vol. 10, article id 49
Keywords [en]
Random forest, Support vector machines, Sparse representation, Fingerprint, Machine learning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:uu:diva-368445DOI: 10.1186/s13321-018-0304-9ISI: 000447254000001PubMedID: 30306349OAI: oai:DiVA.org:uu-368445DiVA, id: diva2:1269066
Funder
Knut and Alice Wallenberg FoundationSwedish Research Council FormasSwedish National Infrastructure for Computing (SNIC), SNIC 2017/7-241Available from: 2018-12-07 Created: 2018-12-07 Last updated: 2018-12-07Bibliographically approved

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Alvarsson, JonathanSpjuth, Ola

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