Enhancing multifunctional drug screening via artificial intelligence

Abstract

Computational drug screening is of fundamental importance in early-stage drug discovery. The current computational methods predict compound affinities to their targets based on docking or pharmacophore (PH4) hypotheses. Here, we develop Alpha-Pharm3D, a versatile deep learning method that predicts ligand–protein interactions using 3D PH4 fingerprints by explicitly incorporating geometric constraints. This comprehensive new algorithm enhances substantially not only the prediction interpretability and accuracy of binding affinities of ligands against the target protein, but also the PH4 potential for screening large compound libraries efficiently. Alpha-Pharm3D outperforms state-of-the-art scoring methods in bioactivity prediction and achieves considerable improvements in both accuracy and success rate, irrespective of data scarcity. We demonstrate the superior applicability of Alpha-Pharm3D for compound screening against the NK1R, a cancer growth and metastasis related G-protein coupled receptor, yielding nanomolar active compounds. This opens up attractive possibilities for applying PH4 fingerprints to efficiently promote scaffold hopping and ultimately accelerate ligand-based drug discovery.

Graphical abstract: Enhancing multifunctional drug screening via artificial intelligence

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Article information

Article type
Paper
Submitted
02 mar. 2025
Accepted
17 jun. 2025
First published
20 jun. 2025
This article is Open Access
Creative Commons BY-NC license

Digital Discovery, 2025, Advance Article

Enhancing multifunctional drug screening via artificial intelligence

J. Dong, C. Wu, T. Lu, S. Wang, W. Zhan, M. Xu, B. Wang, Z. Hu, H. Vogel and S. Yuan, Digital Discovery, 2025, Advance Article , DOI: 10.1039/D5DD00082C

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