Predicting the activity of chemical compounds based on machine learning
  approaches
Do Hoang Tu Tran Van Lang Pham Cong Xuyen Le Mau Long
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