Exploring the future of biopharmaceutical drug discovery: can advanced AI platforms overcome current challenges?

被引：0
作者：
Alberto Bettanti [1 ]
Andrea Rosario Beccari [2 ]
Marco Biccarino [1 ]
机构：
[1] University of Genoa, Genoa
[2] Dompé (Italy), Naples
来源：
Discover Artificial Intelligence | / 4卷 / 1期
关键词：
Artificial intelligence (AI); Deep learning (DL); Drug development; Drug discovery (DD); Machine learning (ML);
D O I：
10.1007/s44163-024-00188-3
中图分类号：
学科分类号：
摘要：
Artificial intelligence (AI)-based drug discovery has not yet completely addressed the numerous and varied challenges posed by the inherent complexities and demands of achieving breakthroughs in drug discovery (DD). This review explores the promising role of advanced AI platforms in navigating the complex landscape of biopharmaceutical DD. A thorough examination of challenges, methodologies, and the pivotal role of AI outlines a vision for a more efficient, innovative, and transformative DD process. Using the cutting-edge Dompé Exscalate platform as a case study, this review describes how AI can address the diverse challenges that have long hampered progress in the field. By adopting a transformative approach in DD, the Exscalate platform exemplifies an AI-driven paradigm capable of not only streamlining the identification of promising molecular candidates but also improving prediction accuracy and the development of targeted therapeutics. Exploring the convergence of machine learning, deep learning, and high-performance computing, the article describes a landscape in which AI-integrated platforms blend technological and biological disciplines seamlessly. This synergy, encompassing physics-based simulations, generative modeling, and extensive molecular databases, has significant implications for both the scientific community and the pharmaceutical industry and will lead to notable improvements in both efficiency and innovation in biopharmaceutical DD. © The Author(s) 2024.
引用
收藏
相关论文
共 138 条
  • [1] Newman D.J., Cragg G.M., Snader K.M., The influence of natural products upon drug discovery, Nat Prod Rep, 17, 3, pp. 215-234, (2000)
  • [2] Achan J., Et al., Quinine, an old anti-malarial drug in a modern world: role in the treatment of malaria, Malar J, 10, pp. 1-12, (2011)
  • [3] Li J.W.-H., Vederas J.C., Drug discovery and natural products: end of an era or an endless frontier?, Science, 325, 5937, pp. 161-165, (2009)
  • [4] Blanco-Gonzalez A., Et al., The role of AI in drug discovery: challenges, opportunities, and strategies, Pharmaceuticals, 16, 6, (2023)
  • [5] Leite M.L., Et al., Artificial intelligence and the future of life sciences, Drug Discovery Today, 26, 11, (2021)
  • [6] Chen H., Engkvist O., Wang Y., Olivecrona M., Blaschke T., The rise of deep learning in drug discovery, Drug Discovery Today, 23, 6, (2018)
  • [7] Lou B., Wu L., MISQ Forthcoming, (2021)
  • [8] After years of hype, the first AI-designed drugs fall short in the clinic,”
  • [9] Sarkar C., Et al., Artificial intelligence and machine learning technology driven modern drug discovery and development, Int J Mol Sci, 24, 3, (2023)
  • [10] Augen J., The evolving role of information technology in the drug discovery process, Drug Discovery Today, 7, 5, (2002)
12345678910