Prediction of Pharmacological Activity by Machine Learning Using Molecular Orbital Energy as an Explanatory Variable

Abstract

We constructed a mathematical model to predict the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging capacity (IC₅₀) for recently synthesized ferulic acid derivatives by machine learning with molecular orbital energy as an explanatory variable and IC₅₀ as an objective variable. We compared 96 regression models including xgbLinear and neuralnet included in R/caret package. We were able to construct IC₅₀ prediction models for these new ferulic acids by using xgbLinear, M5, ppr, and neuralnet as regression methods.