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MAI-TargetFisher: A proteome-wide drug target prediction method synergetically enhanced by artificial intelligence and physical modeling | Acta Pharmacologica Sinica

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Article Published: 27 January 2025 MAI-TargetFisher: A proteome-wide drug target prediction method synergetically enhanced by artificial intelligence and physical modeling Shi-wei Li, Peng-xuan Ren, Lin Wang, Qi-lei Han, Feng-lei Li, Hong-lin Li & Fang Bai 

Acta Pharmacologica Sinica volume  46,  pages 1462–1475 (2025)Cite this article

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Abstract

Computational target identification plays a pivotal role in the drug development process. With the significant advancements of deep learning methods for protein structure prediction, the structural coverage of human proteome has increased substantially. This progress inspired the development of the first genome-wide small molecule targets scanning method. Our method aims to localize drug targets and detect potential off-target effects early in the drug discovery process, thereby improving the success rate of drug development. We have constructed a high-quality database of protein structures with annotated potential binding sites, covering 82% of the protein-coding genome. On the basis of this database, to enhance our search capabilities, we have integrated computational techniques, including both artificial intelligence-based and biophysical model-based methods. This integration led to the development of a target identification method called Multi-Algorithm Integrated Target Fisher (MAI-TargetFisher). MAI-TargetFisher leverages the complementary strengths of various methods while minimizing their weaknesses, enabling precise database navigation to generate a reliably ranked set of candidate targets for an active query molecule. Importantly, our work is the first comprehensive scan of protein surfaces across the entire human genome, aimed at evaluating potential small molecule binding sites on each protein. Through a series of evaluations on benchmark and a target identification task, the results demonstrate the high hit rates and good reliability of our method under the validation of wet experiments. We have also made available a freely accessible web server at https://bailab.siais.shanghaitech.edu.cn/mai-targetfisher for non-commercial use.

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Additional access options: Log in Learn about institutional subscriptions Read our FAQs Contact customer support Similar content being viewed by others Advancing active compound discovery for novel drug targets: insights from AI-driven approaches Article 17 June 2025 Inferring molecular inhibition potency with AlphaFold predicted structures Article Open access 08 April 2024 A novel approach for target deconvolution from phenotype-based screening using knowledge graph Article Open access 18 January 2025 Data availability

The raw protein structural data and target-ligand interaction data could be obtained from RCSB PDB [25], AlphaFold Protein Structure Database [24], CovInDB [36] and BindingDB [14]. The data that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.13636118, reference number 13636118, including experimental resolved part of hhPDL, the raw data and code for results replication. Any additional information and materials in this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported by the National Key R&D Program of China (Grant IDs: 2022YFC3400501 & 2022YFC3400500), Shanghai Science and Technology Development Funds (Grant ID: 22ZR1441400), the National Natural Science Foundation of China (No 82003654), ShanghaiTech AI4S Initiative SHTAI4S202404, start-up package from ShanghaiTech University, and Shanghai Frontiers Science Center for Biomacromolecules and Precision Medicine at ShanghaiTech University. We also thank the HPC Platform of ShanghaiTech University and Baizhen Biotechnologies.

Author information Authors and Affiliations

Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China

Shi-wei Li, Peng-xuan Ren, Lin Wang, Qi-lei Han, Feng-lei Li & Fang Bai

School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China

Feng-lei Li & Fang Bai

Innovation Center for AI and Drug Discovery, East China Normal University, Shanghai, 200062, China

Hong-lin Li

Shanghai Clinical Research and Trial Center, Shanghai, 201210, China

Fang Bai

Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. SWL and FB wrote the manuscript and designed the framework. QLH conducted the LIP-MS experiment. SWL, PXR, and LW constructed the databases. SWL and FLL implemented the web server. LW and HLL provided the ligand similarity comparison algorithms. FB designed the project and revised the manuscript.

Corresponding author

Correspondence to Fang Bai.

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The authors declare no competing interests.

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Li, Sw., Ren, Px., Wang, L. et al. MAI-TargetFisher: A proteome-wide drug target prediction method synergetically enhanced by artificial intelligence and physical modeling. Acta Pharmacol Sin 46, 1462–1475 (2025). https://doi.org/10.1038/s41401-024-01444-z

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Received 04 October 2024

Accepted 24 November 2024

Published 27 January 2025

Version of record 27 January 2025

Issue date May 2025

DOI https://doi.org/10.1038/s41401-024-01444-z

Keywords target identification deep-learning binding site identification ligand-based searching reverse docking This article is cited by Integration of machine learning and experimental validation reveals new lipid-lowering drug candidates Jing-hong ChenKe-xin LiPeng Luo

Acta Pharmacologica Sinica (2025)