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Updated: Aug 14, 2026

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
Published on: April 3, 2026
Coordination-informed machine learning enables virtual screening of phenanthroline ligands by predicting Am/Eu
Zhiyuan Zhang1, Dongsheng Yang1, Yulong Que1
1School of Chemical Engineering, Sichuan University, Chengdu, 610065, China. liuchong@scu.edu.cn.
None:
A coordination-informed machine-learning workflow reconstructs metal-ligand graphs from design-stage ligands for log β1 prediction. Privileged 3D/quantum-chemical pretraining enables transfer to f-element-rich data and virtual screening of 300 000 phenanthroline-derived ligands for predicted Am(III)-over-Eu(III) binding preference, with Δlog β1 used as a thermodynamic ranking metric rather than a complete extraction-selectivity descriptor.
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