Drug efficacy prediction using machine learning with molecular orbital energy as explanatory variable

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.