Predicting the activity of chemical compounds based on machine learning approaches

Do Hoang Tu, Tran Van Lang, Pham Cong Xuyen, Le Mau Long
9/10/2023
q-bio.BMcs.LG

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Abstract

Exploring methods and techniques of machine learning (ML) to address specific challenges in various fields is essential. In this work, we tackle a problem in the domain of Cheminformatics; that is, providing a suitable solution to aid in predicting the activity of a chemical compound to the best extent possible. To address the problem at hand, this study conducts experiments on 100 different combinations of existing techniques. These solutions are then selected based on a set of criteria that includes the G-means, F1-score, and AUC metrics. The results have been tested on a dataset of about 10,000 chemical compounds from PubChem that have been classified according to their activity

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Cite this paper

@article{tu2023predicting,
  title  = {Predicting the activity of chemical compounds based on machine learning approaches},
  author = {Do Hoang Tu and Tran Van Lang and Pham Cong Xuyen and Le Mau Long},
  year   = {2023},
  eprint = {2401.01004},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.01004v1}
}

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