Artificial intelligence empowering the full spectrum of drug discovery

Tingting Fu; Kuo Zhang; Tingjun Hou; Caisheng Wu; Feng Zhu

doi:10.1016/j.jpha.2025.101438

Volume 15 Issue 8

Sep. 2025

Turn off MathJax

Article Contents

Article Navigation > Journal of Pharmaceutical Analysis > 2025 > 15(8): 101438

Tingting Fu, Kuo Zhang, Tingjun Hou, Caisheng Wu, Feng Zhu. Artificial intelligence empowering the full spectrum of drug discovery[J]. Journal of Pharmaceutical Analysis, 2025, 15(8): 101438. doi: 10.1016/j.jpha.2025.101438

Citation:

Tingting Fu, Kuo Zhang, Tingjun Hou, Caisheng Wu, Feng Zhu. Artificial intelligence empowering the full spectrum of drug discovery[J]. Journal of Pharmaceutical Analysis, 2025, 15(8): 101438. doi: 10.1016/j.jpha.2025.101438

Citation:

PDF( 1653 KB)

Artificial intelligence empowering the full spectrum of drug discovery

doi: 10.1016/j.jpha.2025.101438

Tingting Fu¹,
Kuo Zhang^2,3,
Tingjun Hou^{4
,
,},
Caisheng Wu^{5
,
,},
Feng Zhu^{6
,
,}

1. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China;
2. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China;
3. School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China;
4. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China;
5. Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cell Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, 361102, China;
6. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China

Available Online: Sep. 23, 2025
Publish Date: Sep. 17, 2025

Abstract

FullText(HTML)

References(21)

References

[1]	K. Zhang, X. Yang, Y. Wang, et al., Artificial intelligence in drug development, Nat. Med. 31 (2025) 45−59.
[2]	C. Fu, Q. Chen, The future of pharmaceuticals: Artificial intelligence in drug discovery and development, J. Pharm. Anal. 15 (2025), 101248.
[3]	J. Zhang, J. Peng, C. Yu, et al., Prioritization of potential drug targets for diabetic kidney disease using integrative omics data mining and causal inference, J. Pharm. Anal. 2025. 15 (2025), 101265.
[4]	Z. Zhou, Y. Yu, C. Yang, et al., GPT2-ICC: A data-driven approach for accurate ion channel identification using pre-trained large language models, J. Pharm. Anal. 15 (2025), 101302.
[5]	Y. Zhang, L. Zheng, N. You, et al., LocPro: A deep learning-based prediction of protein subcellular localization for promoting multi-directional pharmaceutical research, J. Pharm. Anal. 15 (2025), 101255.
[6]	X. Liu, H. Yang, X. Liu, et al., Discovery of selective HDAC6 inhibitors driven by artificial intelligence and molecular dynamics simulation approaches, J. Pharm. Anal. 15 (2025), 101338.
[7]	Y. Zhang, J. Wu, Y. Kang, et al., A multimodal contrastive learning framework for predicting P-glycoprotein substrates and inhibitors, J. Pharm. Anal. 15 (2025), 101313.
[8]	D. Luo, Z. Sha, J. Mao, et al., Repurposing drugs for the human dopamine transporter through WHALES descriptors-based virtual screening and bioactivity evaluation, J. Pharm. Anal. 15 (2025), 101368.
[9]	X. Sheng, Y. Gui, J. Yu, et al., Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach, J. Pharm. Anal. 15 (2025), 101337.
[10]	J. Yu, C. Shi, Y. Zhou, et al., DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction, J. Pharm. Anal. 15 (2025), 101315.
[11]	X. Yu, Y. Wang, L. Chen, et al., ACtriplet: An improved deep learning model for activity cliffs prediction by integrating triplet loss and pre-training, J. Pharm. Anal. 15 (2025), 101317.
[12]	J. He, Y. Wu, L. Yuan, et al., An inductive learning-based method for predicting drug-gene interactions using a multi-relational drug-disease-gene graph, J. Pharm. Anal. 15 (2025), 101347.
[13]	W. Wang, Q. Yan, Q. Liao, et al., Multi-scale information fusion and decoupled representation learning for robust microbe-disease interaction prediction, J. Pharm. Anal. 15 (2025), 101134.
[14]	K. Liu, H. Cui, X. Yu, et al., Predicting cardiotoxicity in drug development: A deep learning approach, J. Pharm. Anal. 15 (2025), 101263.
[15]	Y. He, X. Lv, W. Long, et al., ToxBERT: An explainable AI framework for enhancing prediction of adverse drug reactions and structural insights, J. Pharm. Anal. 15 (2025), 101387.
[16]	M.Z. Zhang, J. Wang, S.L. Li, et al., Artificial intelligence and computational methods in human metabolism research: A comprehensive survey, J. Pharm. Anal. 15 (2025), 101437.
[17]	D.T. Tran, N.T. Pham, N.D. Hieu Nguyen, et al., HyPepTox-Fuse: An interpretable hybrid framework for accurate peptide toxicity prediction fusing protein language model-based embeddings with conventional descriptors, J. Pharm. Anal. 15 (2025), 101410.
[18]	L. Weng, H. Wang, C. Zhai, et al., Spatial metabolomics combined with machine learning in colon cancer diagnosis research, J. Pharm. Anal. 15 (2025), 101367.
[19]	O. Peterfi, N. Kallai-Szabo, K.R. Demeter, et al., Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process, J. Pharm. Anal. 15 (2025), 101227.
[20]	Y. Hong, S. Zhu, Y. Liu, et al., The integration of machine learning into traditional Chinese medicine, J. Pharm. Anal. 15 (2025), 101157.
[21]	J. Zeng, X. Jia, Quantifying compatibility mechanisms in traditional Chinese medicine with interpretable graph neural networks, J. Pharm. Anal. 15 (2025), 101342.