Prediction of ADMET Properties of Anti-Breast Cancer Compounds Using Three Machine Learning Algorithms

Abstract

In recent years, machine learning methods have been applied successfully in many fields. In this paper, three machine learning algorithms, including partial least squares-discriminant analysis (PLS-DA), adaptive boosting (AdaBoost), and light gradient boosting machine (LGBM), were applied to establish models for predicting the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET for short) properties, namely Caco-2, CYP3A4, hERG, HOB, MN of anti-breast cancer compounds. To the best of our knowledge, the LGBM algorithm was applied to classify the ADMET property of anti-breast cancer compounds for the first time. We evaluated the established models in the prediction set using accuracy, precision, recall, and F1-score. Compared with the performance of the models established using the three algorithms, the LGBM yielded most satisfactory results (accuracy > 0.87, precision > 0.72, recall > 0.73, and F1-score > 0.73). According to the obtained results, it can be inferred that LGBM can establish reliable models to predict the molecular ADMET properties and provide a useful tool for virtual screening and drug design researchers.

Boosting the Accuracy and Chemical Space Coverage of the Detection of Small Colloidal Aggregating Molecules Using the BAD Molecule Filter

“…Although the SVM classifier model had comparable performance to CatBoost for the Lactamase data set, we excluded it due to its high processing time. The selected models are widely known for their good performance in different ML applications. − These models learn from data using decision trees. However, they differ in their features handling and training methodologies, which leads to different performances across various classification problems .…”

ToxMPNN: A deep learning model for small molecule toxicity prediction

“…In the last decade, numerous toxicity prediction models with acceptable accuracy have been developed using various ML algorithms (Galati et al, 2022; Li et al, 2023; Wu et al, 2021; Yang, Zhong, Zhao, & Chen, 2022; Zhang, Norinder, & Svensson, 2021). Commonly used models for toxicity evaluation include k‐nearest neighbors (kNN), support vector machine (SVM), random forest (RF), artificial neural networks (ANNs), and DL methods (Cavasotto & Scardino, 2022).…”