Artificial Intelligence (AI) is revolutionizing the drug development pipeline, significantly improving research and development (R&D) efficiency and success rates. AI's innovative applications span target identification, virtual screening, data integration, and molecular design. By utilizing advanced technologies such as deep learning, graph neural networks, and multimodal learning, AI facilitates the identification of disease targets, prediction of molecular binding modes, and integration of multi-omics data to construct dynamic models. Notable examples include AlphaFold-Multimer for protein structure prediction and Deep Docking for molecular docking. Despite these remarkable advancements, several formidable challenges persist and hinder the widespread adoption of AI in drug development. These include the "black-box" nature of AI models, inconsistent data quality, limited simulation of dynamic biological environments, and fragmented interdisciplinary knowledge. To overcome these obstacles, future developments should focus on three key areas: enhancing model interpretability through the strategic integration of physicochemical constraints, optimizing data sharing via the utilization of federated learning and differential privacy techniques, and constructing highly dynamic prediction frameworks by incorporating molecular dynamics simulations. With continued interdisciplinary collaboration and continuous technological innovations, AI holds the immense potential to reshape drug development, driving the progress of precision medicine, reducing R&D costs, and offering new approaches to addressing complex diseases.
| Published in | Advances in Sciences and Humanities (Volume 11, Issue 2) |
| DOI | 10.11648/j.ash.20251102.12 |
| Page(s) | 36-41 |
| 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 |
AI-driven Drug Discovery, Multimodal Learning, Molecular Dynamics Simulation, Interpretable AI
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APA Style
Lai, S. (2025). Artificial Intelligence Reshapes Drug Development: Technological Breakthroughs, Challenges, and Future Pathways. Advances in Sciences and Humanities, 11(2), 36-41. https://doi.org/10.11648/j.ash.20251102.12
ACS Style
Lai, S. Artificial Intelligence Reshapes Drug Development: Technological Breakthroughs, Challenges, and Future Pathways. Adv. Sci. Humanit. 2025, 11(2), 36-41. doi: 10.11648/j.ash.20251102.12
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Download@article{10.11648/j.ash.20251102.12,
author = {Shinuo Lai},
title = {Artificial Intelligence Reshapes Drug Development: Technological Breakthroughs, Challenges, and Future Pathways
},
journal = {Advances in Sciences and Humanities},
volume = {11},
number = {2},
pages = {36-41},
doi = {10.11648/j.ash.20251102.12},
url = {https://doi.org/10.11648/j.ash.20251102.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ash.20251102.12},
abstract = {Artificial Intelligence (AI) is revolutionizing the drug development pipeline, significantly improving research and development (R&D) efficiency and success rates. AI's innovative applications span target identification, virtual screening, data integration, and molecular design. By utilizing advanced technologies such as deep learning, graph neural networks, and multimodal learning, AI facilitates the identification of disease targets, prediction of molecular binding modes, and integration of multi-omics data to construct dynamic models. Notable examples include AlphaFold-Multimer for protein structure prediction and Deep Docking for molecular docking. Despite these remarkable advancements, several formidable challenges persist and hinder the widespread adoption of AI in drug development. These include the "black-box" nature of AI models, inconsistent data quality, limited simulation of dynamic biological environments, and fragmented interdisciplinary knowledge. To overcome these obstacles, future developments should focus on three key areas: enhancing model interpretability through the strategic integration of physicochemical constraints, optimizing data sharing via the utilization of federated learning and differential privacy techniques, and constructing highly dynamic prediction frameworks by incorporating molecular dynamics simulations. With continued interdisciplinary collaboration and continuous technological innovations, AI holds the immense potential to reshape drug development, driving the progress of precision medicine, reducing R&D costs, and offering new approaches to addressing complex diseases.
},
year = {2025}
}
TY - JOUR T1 - Artificial Intelligence Reshapes Drug Development: Technological Breakthroughs, Challenges, and Future Pathways AU - Shinuo Lai Y1 - 2025/06/16 PY - 2025 N1 - https://doi.org/10.11648/j.ash.20251102.12 DO - 10.11648/j.ash.20251102.12 T2 - Advances in Sciences and Humanities JF - Advances in Sciences and Humanities JO - Advances in Sciences and Humanities SP - 36 EP - 41 PB - Science Publishing Group SN - 2472-0984 UR - https://doi.org/10.11648/j.ash.20251102.12 AB - Artificial Intelligence (AI) is revolutionizing the drug development pipeline, significantly improving research and development (R&D) efficiency and success rates. AI's innovative applications span target identification, virtual screening, data integration, and molecular design. By utilizing advanced technologies such as deep learning, graph neural networks, and multimodal learning, AI facilitates the identification of disease targets, prediction of molecular binding modes, and integration of multi-omics data to construct dynamic models. Notable examples include AlphaFold-Multimer for protein structure prediction and Deep Docking for molecular docking. Despite these remarkable advancements, several formidable challenges persist and hinder the widespread adoption of AI in drug development. These include the "black-box" nature of AI models, inconsistent data quality, limited simulation of dynamic biological environments, and fragmented interdisciplinary knowledge. To overcome these obstacles, future developments should focus on three key areas: enhancing model interpretability through the strategic integration of physicochemical constraints, optimizing data sharing via the utilization of federated learning and differential privacy techniques, and constructing highly dynamic prediction frameworks by incorporating molecular dynamics simulations. With continued interdisciplinary collaboration and continuous technological innovations, AI holds the immense potential to reshape drug development, driving the progress of precision medicine, reducing R&D costs, and offering new approaches to addressing complex diseases. VL - 11 IS - 2 ER -
School of Pharmacy, China Pharmaceutical University, Nanjing, China
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