Salicylic acid solubility prediction in different solvents based on machine learning algorithms

Abstract

This study aims to predict the solubility of salicylic acid in 13 different solvents, such as methanol, water, ethanol, ethyl acetate, PEG 300, 1,4-dioxane, 1-propanol, and others, given the significance of salicylic acid in the pharmaceutical industry. based on machine learning has been studied. In this study, 6 machine learning algorithms including neural network, linear regression, logistic regression, decision tree, random forest and kNN (k- Nearest Neighbors) were used. The comparison between the predictions of these algorithms and experimental data highlights the accuracy of predicting the solubility of salicylic acid for 217 samples based on 15 variables (13 solvents, temperature, and pressure). Based on the results of this study, the lowest total error (difference between experimental and predicted values) was 0.00016835 related to the random forest algorithm, and the highest value was 0.024768 related to k-Nearest Neighbors.

Keywords

• Salicylic acid solubility
• Machine learning
• Neural network
• Decision tree
• Random forest
• Linear regression