AI in drug discovery and its clinical relevance

Design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes…

• AS
  American Society of Clinical Oncology
• HB
  Hamad Bin Khalifa University
• FN
  Fonds National de la Recherche Luxembourg
• RG
  Research Grants Council, University Grants Committee

  Award: 11204821
• IA
  Innovation and Technology Commission
• KU
  Khalifa University of Science, Technology and Research
• IA
  Innovation and Technology Commission - Hong Kong
• CO
  College of Science and Engineering, University of Minnesota
• QN
  Qatar National Research Fund

  Awards: TDF 03-1206-210011, RRC02-0805-210019
• QN
  Qatar National Library
• NC
  National Cancer Institute