Work

Integrating equivariant architectures and charge supervision for data-efficient molecular property prediction

Year: 2025

Type: article

Abstract: Molecular equivariant transformer (MET) learns symmetry-aware, charge-guided representations that capture geometric and electronic structure, enabling data-efficient and interpretable molecular property prediction across diverse tasks.

Source: Molecular Systems Design & Engineering

DOI: 10.1039/d5me00173k

Authors: Zixiao Yang, Hanyu Gao, Xian Kong

Institutions: South China University of Technology, Hong Kong University of Science and Technology

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Guangdong Science and Technology Department, Guangzhou Municipal Science and Technology Bureau, Major Research Plan, Fundamental Research Funds for the Central Universities, Basic and Applied Basic Research Foundation of Guangdong Province

Awards: G1570869426, G2423843208, G3170767724, G4372068080, G4867286092, G5042532307, G5842216219, G6911006596

OpenAlex ID: W7117147132

FWCI: 0.477

Cites: 22

Cited by: 1

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