Graph Convolutional Neural Network-Enabled Frontier Molecular Orbital Prediction: A Case Study with Neurotransmitters and Antidepressants.

ABSTRACT: With the advancement of artificial intelligence-embedded methodologies, their application to predict fundamental molecular properties has become increasingly prevalent. In this study, a graph convolutional neural network fingerprint-enabled artificial neural network (GCN-ANN) was utilized to probe the relationship between the chemical hardness of neurochemicals and their affinities for neuroreceptors. The GCN-ANN model was derived using a training set of B3LYP-calculated HOMO and LUMO energies of >110,000 molecules. A benchmark study of 45 neurochemicals produced consistent hardness and electronegativity values across the three density functionals, namely, B3LYP, ωB97XD, and M06-2X. However, the computed energetics varied significantly when the Hartree-Fock theory was used. The scrutiny of

SUBMITTER: Monsia R 

PROVIDER: S-EPMC12308792 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature