Integration and Innovation of AIDD and CADD: Cutting-Edge Technologies and Future Trends in Accelerating Drug Development

Donglin Song

doi:doi:10.11648/j.jdmp.20251102.13

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Integration and Innovation of AIDD and CADD: Cutting-Edge Technologies and Future Trends in Accelerating Drug Development

Donglin Song^*

Published in Journal of Diseases and Medicinal Plants (Volume 11, Issue 2)

Received: 30 April 2025 Accepted: 13 May 2025 Published: 16 June 2025

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Abstract

Traditional new drug R & D is mired in problems like long cycles, high costs, and low success rates. The integration of AIDD and CADD offers a fresh approach to breaking the “ten - year and ten - billion - dollar curse”. AIDD, with advanced technologies like deep learning and GANs, has greatly improved the efficiency of target identification, drug screening, and optimization. CADD, wielding established molecular modeling and virtual screening methods, lends theoretical backing to drug design. By analyzing cases of AIDD and CADD in target identification, virtual screening, and multimodal model application, the paper shows their advantages in speeding up drug discovery. Results indicate that their integration optimizes the R & D process, reducing costs and timelines. It also explores future trends like multimodal data fusion, reinforcement learning, and AI model interpretability, presenting strategies for tackling challenges in data quality and interdisciplinary collaboration. This paper focuses on the integrative innovation between artificial intelligence-driven drug design (AIDD) and computer - assisted drug design (CADD), delving into their role in accelerating drug development and future trends. Tied to China's biopharmaceutical industry growth, the paper proposes national - strategy recommendations, stressing international cooperation and policy support. The integrative innovation of AIDD and CADD heralds new opportunities for advancing personalized and precision medicine.

Published in	Journal of Diseases and Medicinal Plants (Volume 11, Issue 2)
DOI	10.11648/j.jdmp.20251102.13
Page(s)	57-65
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

AIDD, CADD, Integrative Innovation, Drug Development, Precision Medicine

References

[1]	Wu, G., Xie, W. B., & Fu, Y. (2024). China’s innovative drug R&D: From challenges to breakthroughs. Health News, (005), December 6.
[2]	Zhang, J. X., Liu, S., & Li, K. (2025). AI accelerates drug R&D. Science and Technology Daily, (005), January 16.
[3]	Liu, X. W., Shi, D. F., Zhou, S. Y., et al. (2018). Molecular dynamics simulations and novel drug discovery. Expert Opinion on Drug Discovery, 13(1), 23-37.
[4]	Yan, T., Yu, L., Zhang, N., et al. (2022). Advanced development of molecular targeted therapy for hepatocellular carcinoma. Cancer Biology & Medicine, 19(6), 802-817.
[5]	Boomsma, W., Nielsen, S. V., & Lindorff-Larsen, K. (2016). Bioinformatics analysis identifies several intrinsically disordered human E3 ubiquitin-protein ligases. PeerJ, 4, e1725.
[6]	Doe, J., & Smith, J. (2021). Structure- and ligand-based drug design: Concepts, approaches, and challenges. Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences, 15(4), 234-250.
[7]	Matsuzaka, Y., & Uesawa, Y. (2023). Ensemble learning, deep learning-based, and molecular descriptor-based quantitative structure-activity relationships. Molecules, 28(5), 2410.
[8]	Lee, S., & Kim, D. (2020). Virtual screening, molecular docking, and QSAR studies in drug discovery and development. Journal of Drug Delivery and Therapeutics, 10(2), 123-135.
[9]	Johnson, E., & Brown, M. (2024). Integration of LBDD and SBDD in drug design: A FAAH case study. ResearchGate. Preprint.
[10]	Black, C., & Zhang, H. (2005). Combining structure-based drug design and pharmacophores. Journal of Molecular Graphics and Modelling, 23(4), 345-356.
[11]	Yu, Z., Zhang, L., Zhang, M., et al. (2023). Artificial intelligence-based drug development: Current progress and future challenges. Journal of China Pharmaceutical University, 54(3), 282-293.
[12]	Jayatunga, M. K. P., Xie, W., Ruder, L., et al. (2022). AI in small-molecule drug discovery: A coming wave. Nature Reviews Drug Discovery, 21(3), 175-176.
[13]	Tang, Q., Chen, R., Shen, Z., et al. (2024). Research progress of AI-based small molecule generation models in drug discovery. Journal of China Pharmaceutical University, 55(3), 295-305.
[14]	Liu, X. F., Ouyang, S. S., Yu, B., et al. (2010). PharmMapper server: A web server for potential drug target identification using pharmacophore mapping. Nucleic Acids Research, 38(Web Server issue), W609-W614.
[15]	Zhang, Y., & Li, W. (2023). DataPype: A unified platform for computer-aided drug design. Journal of Chemical Information and Modeling, 63(4), 789-801.
[16]	Wolber, G., & Langer, T. (2005). LigandScout: 3D pharmacophores from protein-bound ligands for virtual screening. Journal of Chemical Information and Modeling, 45(1), 160-169.
[17]	Liu, X. H., Ye, K., van Vlijmen, H. W. T., et al. (2023). DrugEx v3: Scaffold-based molecular generation with reinforcement learning. Journal of Medicinal Chemistry, 66(12), 5678-5690.
[18]	Bai, P., Miljković, F., John, B., et al. (2025). Predicting drug-target interactions with dilated reparameterized convolution. Scientific Reports, 15(1), 1-12.
[19]	Pereira, F., Silva, Z. M., Videira, P., et al. (2025). Advances in drug discovery: Integrating CADD tools and drug repurposing for PD-1/PD-L1 inhibition. RSC Advances, 15(1), 1-12.
[20]	Hou, T. J., et al. (2024). Deep lead optimization: Leveraging generative AI for structural modification. Journal of the American Chemical Society, 146(45), 20241-20253.
[21]	Insilico Medicine Team. (2019). DDR1 inhibitor design using GANs. Nature Biotechnology, 37(9), 1035-1042.
[22]	Alakhdar, A., Poczos, B., & Washburn, N. (2024). Diffusion models in de novo drug design. Journal of Chemical Information and Modeling, 64(19), 7238-7256.
[23]	Zhou, G., et al. (2024). An AI-accelerated virtual screening platform for drug discovery. Nature Communications, 15, 7761.
[24]	Noor, F., et al. (2024). Deep learning pipeline for accelerating virtual screening in drug discovery. Scientific Reports, 14(1), 19799.
[25]	Wang, L., et al. (2024). Conformational space profiling enhances molecular representation for AI-driven ligand-based drug discovery. Advanced Science, 24(39), e2403998.
[26]	Chao, Y., et al. (2024). Generic protein-ligand interaction scoring via physical knowledge and data augmentation. Nature Machine Intelligence, 6(6), 654-664.
[27]	Chen, B. Y., Lü, L. N., Xu, X. D., et al. (2024). Perspectives on improving drug development success rates with AIDD and CADD. Journal of China Pharmaceutical University, 55(3), 284-294.

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APA Style

Song, D. (2025). Integration and Innovation of AIDD and CADD: Cutting-Edge Technologies and Future Trends in Accelerating Drug Development. Journal of Diseases and Medicinal Plants, 11(2), 57-65. https://doi.org/10.11648/j.jdmp.20251102.13

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ACS Style

Song, D. Integration and Innovation of AIDD and CADD: Cutting-Edge Technologies and Future Trends in Accelerating Drug Development. J. Dis. Med. Plants 2025, 11(2), 57-65. doi: 10.11648/j.jdmp.20251102.13

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AMA Style

Song D. Integration and Innovation of AIDD and CADD: Cutting-Edge Technologies and Future Trends in Accelerating Drug Development. J Dis Med Plants. 2025;11(2):57-65. doi: 10.11648/j.jdmp.20251102.13

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@article{10.11648/j.jdmp.20251102.13,
  author = {Donglin Song},
  title = {Integration and Innovation of AIDD and CADD: Cutting-Edge Technologies and Future Trends in Accelerating Drug Development
},
  journal = {Journal of Diseases and Medicinal Plants},
  volume = {11},
  number = {2},
  pages = {57-65},
  doi = {10.11648/j.jdmp.20251102.13},
  url = {https://doi.org/10.11648/j.jdmp.20251102.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jdmp.20251102.13},
  abstract = {Traditional new drug R & D is mired in problems like long cycles, high costs, and low success rates. The integration of AIDD and CADD offers a fresh approach to breaking the “ten - year and ten - billion - dollar curse”. AIDD, with advanced technologies like deep learning and GANs, has greatly improved the efficiency of target identification, drug screening, and optimization. CADD, wielding established molecular modeling and virtual screening methods, lends theoretical backing to drug design. By analyzing cases of AIDD and CADD in target identification, virtual screening, and multimodal model application, the paper shows their advantages in speeding up drug discovery. Results indicate that their integration optimizes the R & D process, reducing costs and timelines. It also explores future trends like multimodal data fusion, reinforcement learning, and AI model interpretability, presenting strategies for tackling challenges in data quality and interdisciplinary collaboration. This paper focuses on the integrative innovation between artificial intelligence-driven drug design (AIDD) and computer - assisted drug design (CADD), delving into their role in accelerating drug development and future trends. Tied to China's biopharmaceutical industry growth, the paper proposes national - strategy recommendations, stressing international cooperation and policy support. The integrative innovation of AIDD and CADD heralds new opportunities for advancing personalized and precision medicine.
},
 year = {2025}
}

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TY  - JOUR
T1  - Integration and Innovation of AIDD and CADD: Cutting-Edge Technologies and Future Trends in Accelerating Drug Development

AU  - Donglin Song
Y1  - 2025/06/16
PY  - 2025
N1  - https://doi.org/10.11648/j.jdmp.20251102.13
DO  - 10.11648/j.jdmp.20251102.13
T2  - Journal of Diseases and Medicinal Plants
JF  - Journal of Diseases and Medicinal Plants
JO  - Journal of Diseases and Medicinal Plants
SP  - 57
EP  - 65
PB  - Science Publishing Group
SN  - 2469-8210
UR  - https://doi.org/10.11648/j.jdmp.20251102.13
AB  - Traditional new drug R & D is mired in problems like long cycles, high costs, and low success rates. The integration of AIDD and CADD offers a fresh approach to breaking the “ten - year and ten - billion - dollar curse”. AIDD, with advanced technologies like deep learning and GANs, has greatly improved the efficiency of target identification, drug screening, and optimization. CADD, wielding established molecular modeling and virtual screening methods, lends theoretical backing to drug design. By analyzing cases of AIDD and CADD in target identification, virtual screening, and multimodal model application, the paper shows their advantages in speeding up drug discovery. Results indicate that their integration optimizes the R & D process, reducing costs and timelines. It also explores future trends like multimodal data fusion, reinforcement learning, and AI model interpretability, presenting strategies for tackling challenges in data quality and interdisciplinary collaboration. This paper focuses on the integrative innovation between artificial intelligence-driven drug design (AIDD) and computer - assisted drug design (CADD), delving into their role in accelerating drug development and future trends. Tied to China's biopharmaceutical industry growth, the paper proposes national - strategy recommendations, stressing international cooperation and policy support. The integrative innovation of AIDD and CADD heralds new opportunities for advancing personalized and precision medicine.

VL  - 11
IS  - 2
ER  -

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Author Information

Donglin Song

College of Pharmacy, China Pharmaceutical University, Nanjing, China

Contact Email

http://orcid.org/0009-0002-2850-1280

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Plain Text BibTeX RIS

APA Style

Song, D. (2025). Integration and Innovation of AIDD and CADD: Cutting-Edge Technologies and Future Trends in Accelerating Drug Development. Journal of Diseases and Medicinal Plants, 11(2), 57-65. https://doi.org/10.11648/j.jdmp.20251102.13

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ACS Style

Song, D. Integration and Innovation of AIDD and CADD: Cutting-Edge Technologies and Future Trends in Accelerating Drug Development. J. Dis. Med. Plants 2025, 11(2), 57-65. doi: 10.11648/j.jdmp.20251102.13

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AMA Style

Song D. Integration and Innovation of AIDD and CADD: Cutting-Edge Technologies and Future Trends in Accelerating Drug Development. J Dis Med Plants. 2025;11(2):57-65. doi: 10.11648/j.jdmp.20251102.13

Copy | Download

@article{10.11648/j.jdmp.20251102.13,
  author = {Donglin Song},
  title = {Integration and Innovation of AIDD and CADD: Cutting-Edge Technologies and Future Trends in Accelerating Drug Development
},
  journal = {Journal of Diseases and Medicinal Plants},
  volume = {11},
  number = {2},
  pages = {57-65},
  doi = {10.11648/j.jdmp.20251102.13},
  url = {https://doi.org/10.11648/j.jdmp.20251102.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jdmp.20251102.13},
  abstract = {Traditional new drug R & D is mired in problems like long cycles, high costs, and low success rates. The integration of AIDD and CADD offers a fresh approach to breaking the “ten - year and ten - billion - dollar curse”. AIDD, with advanced technologies like deep learning and GANs, has greatly improved the efficiency of target identification, drug screening, and optimization. CADD, wielding established molecular modeling and virtual screening methods, lends theoretical backing to drug design. By analyzing cases of AIDD and CADD in target identification, virtual screening, and multimodal model application, the paper shows their advantages in speeding up drug discovery. Results indicate that their integration optimizes the R & D process, reducing costs and timelines. It also explores future trends like multimodal data fusion, reinforcement learning, and AI model interpretability, presenting strategies for tackling challenges in data quality and interdisciplinary collaboration. This paper focuses on the integrative innovation between artificial intelligence-driven drug design (AIDD) and computer - assisted drug design (CADD), delving into their role in accelerating drug development and future trends. Tied to China's biopharmaceutical industry growth, the paper proposes national - strategy recommendations, stressing international cooperation and policy support. The integrative innovation of AIDD and CADD heralds new opportunities for advancing personalized and precision medicine.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Integration and Innovation of AIDD and CADD: Cutting-Edge Technologies and Future Trends in Accelerating Drug Development

AU  - Donglin Song
Y1  - 2025/06/16
PY  - 2025
N1  - https://doi.org/10.11648/j.jdmp.20251102.13
DO  - 10.11648/j.jdmp.20251102.13
T2  - Journal of Diseases and Medicinal Plants
JF  - Journal of Diseases and Medicinal Plants
JO  - Journal of Diseases and Medicinal Plants
SP  - 57
EP  - 65
PB  - Science Publishing Group
SN  - 2469-8210
UR  - https://doi.org/10.11648/j.jdmp.20251102.13
AB  - Traditional new drug R & D is mired in problems like long cycles, high costs, and low success rates. The integration of AIDD and CADD offers a fresh approach to breaking the “ten - year and ten - billion - dollar curse”. AIDD, with advanced technologies like deep learning and GANs, has greatly improved the efficiency of target identification, drug screening, and optimization. CADD, wielding established molecular modeling and virtual screening methods, lends theoretical backing to drug design. By analyzing cases of AIDD and CADD in target identification, virtual screening, and multimodal model application, the paper shows their advantages in speeding up drug discovery. Results indicate that their integration optimizes the R & D process, reducing costs and timelines. It also explores future trends like multimodal data fusion, reinforcement learning, and AI model interpretability, presenting strategies for tackling challenges in data quality and interdisciplinary collaboration. This paper focuses on the integrative innovation between artificial intelligence-driven drug design (AIDD) and computer - assisted drug design (CADD), delving into their role in accelerating drug development and future trends. Tied to China's biopharmaceutical industry growth, the paper proposes national - strategy recommendations, stressing international cooperation and policy support. The integrative innovation of AIDD and CADD heralds new opportunities for advancing personalized and precision medicine.

VL  - 11
IS  - 2
ER  -

Copy | Download