Developing a Machine Learning Model for Hydrogen Bond Acceptance Based on Natural Bond Orbital Descriptors.

ABSTRACT: This study employs machine learning (ML) to assess the predictive power of electronic descriptors derived from natural bond orbital (NBO) analysis for hydrogen bond acceptance. Using a data set of 979 hydrogen bond complexes, each formed by a hydrogen bond acceptor and 4-fluorophenol as the donor, we optimized geometries via GFN2-xTB, followed by DFT single-point calculations. From these, NBO analysis was used to extract intramolecular donor-acceptor interactions, particularly the orbital stabilization energies (E⁽²⁾), which reflect electron delocalization and relate to canonical resonance structures. The E⁽²⁾ values served as features to train seven ML models, based on different techniques: KNN, Decision Tree, SVM, RF, MLP, XGBoost, and CatBoost. To our

SUBMITTER: Melo DU 

PROVIDER: S-EPMC12281572 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature