Spectra-descriptor-based machine learning for predicting protein-ligand interactions.

Cheng Chen, Ledu Wang, Yi Feng, Wencheng Yao, Jiahe Liu, Zifan Jiang, Luyuan Zhao, Letian Zhang, Jun Jiang, Shuo Feng

Abstract read

In one paragraph

Article in Chemical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Cheng ChenState Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China Hefei Anhui 230026 China sfeng18@ustc.edu.cn.

Ledu WangState Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China Hefei Anhui 230026 China sfeng18@ustc.edu.cn.

Yi FengState Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China Hefei Anhui 230026 China sfeng18@ustc.edu.cn.

Wencheng YaoMOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University Beijing 102206 China.

Jiahe LiuState Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China Hefei Anhui 230026 China sfeng18@ustc.edu.cn.

Zifan JiangState Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China Hefei Anhui 230026 China sfeng18@ustc.edu.cn.

Luyuan ZhaoState Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China Hefei Anhui 230026 China sfeng18@ustc.edu.cn.

Letian ZhangState Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China Hefei Anhui 230026 China sfeng18@ustc.edu.cn.

Jun JiangState Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China Hefei Anhui 230026 China sfeng18@ustc.edu.cn.ORCID https://orcid.org/0000-0002-6116-5605

Shuo FengState Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China Hefei Anhui 230026 China sfeng18@ustc.edu.cn.ORCID https://orcid.org/0000-0003-0530-8381

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machine learning models have emerged as powerful tools for drug discovery of lead compounds. Nevertheless, despite notable advances in model architectures, research on more reliable and physicochemical-based descriptors for molecules and proteins remains limited. To address this gap, we introduce the Fragment Integral Spectrum Descriptor (FISD), aimed at utilizing the spatial configuration and electronic structure information of molecules and proteins, as a novel physicochemical descriptor for virtual screening models. Validation demonstrates that the combination of FISD and a classical neural network model achieves performance comparable to that of complex models paired with conventional structural descriptors. Furthermore, we successfully predict and screen potential binding ligands for two given protein targets, showcasing the broad applicability and practicality of FISD. This research enriches the molecular and protein representation strategies of machine learning and accelerates the process of drug discovery.

Identifiers

PMID40092599

DOI10.1039/d5sc00451a

PMCPMC11905448

What OpenQuestion holds

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LicenceCC BY-NC

Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.