High-throughput electronic property prediction of cyclic molecules with 3D-enhanced machine learning.

ABSTRACT: Complex organic molecules play a pivotal role in bioactive compounds and organic functional materials, yet existing molecular datasets lack structural diversity for such systems, limiting the generalizability of machine learning (ML) models. This study introduces a high-quality dataset, Ring Vault, comprising 201 546 cyclic molecules, including monocyclic, bicyclic, and tricyclic systems, spanning 11 non-metallic elements. This dataset covers a wide chemical space and provides a robust foundation for molecular property prediction. Leveraging quantum mechanical (QM) calculations on a subset (36 000 molecules), we trained three ML models (Graph Attention Network, Chemprop, and AIMNet2) to predict five key electronic properties: HOMO-LUMO gap, ionization potential (IP), electron affinity (EA)

SUBMITTER: Zheng P 

PROVIDER: S-EPMC12502961 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature