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The Implementation of Genetic Algorithm-Ensemble Learning on QSAR Study of Diacylglycerol Acyltransferase-1(DGAT1) Inhibitors as Anti-diabetes

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Data Science and Emerging Technologies (DaSET 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 165))

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Abstract

Diabetes Mellitus (DM) is one of the most common chronic diseases suffered by the population in the world. DM is the fourth leading cause of death in developing countries. Treatment of diabetes is done by using drugs and blood sugar-lowering therapy. However, the use of inappropriate drugs has side effects in long-term use, such as hypoglycemia and gastrointestinal disorders. One solution that is being tried is to use an inhibitor of the Diacylglycerol Acyltransferase-1 (DGAT-1) enzymes. In the theory of computational drug discovery, Quantitative Structure-Activity Relationship (QSAR) has been successfully created to accelerate the drug discovery process of biopharmaceutical properties of compounds that have not been tested. Build a QSAR model for predicting the activity of DGAT inhibitors as an anti-diabetic target by using the ensemble method. The ensemble method used in this study is Random Forest, AdaBoost, and Gradient Boosting. The best results are obtained by using a gradient boosting model with accuracy and the f1-score are 0.80 and 0.82 respectively.

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Authors and Affiliations

  1. School of Computing, Telkom University, Bandung, Indonesia

    Irfanul Arifa, Annisa Aditsania & Isman Kurniawan

Authors
  1. Irfanul Arifa
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  2. Annisa Aditsania
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  3. Isman Kurniawan
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Corresponding author

Correspondence to Irfanul Arifa .

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Editors and Affiliations

  1. UNITAR Graduate School, UNITAR International University, Selangor, Malaysia

    Yap Bee Wah

  2. University of Tennessee, Knoxville, TN, USA

    Michael W. Berry

  3. Institute for Big Data Analytics and Artificial Intelligence, Universiti Teknologi MARA (UiTM), Shah Alam, Selangor, Malaysia

    Azlinah Mohamed

  4. School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK

    Dhiya Al-Jumeily

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Arifa, I., Aditsania, A., Kurniawan, I. (2023). The Implementation of Genetic Algorithm-Ensemble Learning on QSAR Study of Diacylglycerol Acyltransferase-1(DGAT1) Inhibitors as Anti-diabetes. In: Wah, Y.B., Berry, M.W., Mohamed, A., Al-Jumeily, D. (eds) Data Science and Emerging Technologies. DaSET 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-99-0741-0_20

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