M-polynomial driven machine learning models for predicting physicochemical properties of antibiotics.

ABSTRACT: Accurate prediction of the physicochemical properties of drug compounds is critical for the development of effective and safe antibiotics. In this study, we employ advanced machine learning techniques to address this challenge, using input data that includes M-Polynomials and various physicochemical descriptors. Three models were implemented: basic Support Vector Regression (SVR-Basic), optimized SVR (SVR-Tuned), and Random Forest (RF), trained on known compounds and tested on previously unseen drug samples to evaluate generalization. Model performance was comprehensively assessed using R2, MSE, RMSE, and MAE, alongside detailed error and residual analyses to ensure precision and robustness. Furthermore, residual-based metrics such as the Mean Residual (MR), Standard Deviation of Residuals

SUBMITTER: Li X 

PROVIDER: S-EPMC12724536 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature