Advancing Drug Discovery via Artificial Intelligence


  • AI has enormous potential to revolutionize drug discovery.
  • Computational prediction of atomic and molecular properties is the foundation of most de novo design strategies.
  • Machine learning, a branch of AI, can now predict the physical and chemical properties of small molecules at quantum mechanics-level accuracy with much lower time-cost.
  • AI is also able to search for correlations between molecular representations and biological and toxicological activities.
  • AI-based algorithms are also being developed to efficiently probe the pathways of synthesis of novel drug candidates.
  • In combination with robotic platforms, the chemical space for novel reactions can be explored by learning from automated analysis of reaction feasibility.
Drug discovery and development are among the most important translational science activities that contribute to human health and wellbeing. However, the development of a new drug is a very complex, expensive, and long process which typically costs 2.6 billion USD and takes 12 years on average. How to decrease the costs and speed up new drug discovery has become a challenging and urgent question in industry. Artificial intelligence (AI) combined with new experimental technologies is expected to make the hunt for new pharmaceuticals quicker, cheaper, and more effective. We discuss here emerging applications of AI to improve the drug discovery process.