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Related Concept Videos

Drug Discovery: Overview01:26

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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An Artificial Intelligence Approach to Proactively Inspire Drug Discovery with Recommendations.

Steven L Rohall1, Lydia Auch2, Jonathan Gable2

  • 1Novartis Institutes for BioMedical Research, Cambridge, Massachusetts 02139, United States.

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Artificial intelligence (AI) enhances drug discovery by augmenting human intelligence. Recommendation systems leverage AI with existing lab processes to inspire chemists and accelerate the development of new medicines.

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Area of Science:

  • Biomedical Research
  • Computational Chemistry
  • Drug Discovery

Background:

  • Artificial intelligence (AI) is increasingly utilized in drug discovery for tasks like target identification and compound synthesis optimization.
  • Existing AI applications primarily focus on automating specific research stages.
  • A gap exists in leveraging AI to enhance human decision-making and creativity within the drug discovery pipeline.

Purpose of the Study:

  • To introduce a novel approach using AI to augment human intelligence in drug discovery.
  • To describe a series of AI-powered recommendation systems developed to support chemists.
  • To demonstrate how AI can inspire novel ideas and streamline workflows in pharmaceutical research.

Main Methods:

  • Development of AI-driven recommendation systems integrated with existing wet and computational laboratory processes.
  • Implementation of systems with a common architecture: a trigger for recommendation initiation, AI-powered analysis of existing data, and delivery of actionable suggestions.
  • Deployment of five distinct systems across various stages of the drug discovery pipeline within the Novartis Institutes for BioMedical Research.

Main Results:

  • The recommendation systems provide inspiration to chemists by suggesting potential research directions.
  • These systems offer guidance on the next steps in experimental and computational work.
  • Automation of existing workflows is achieved, leading to increased efficiency.

Conclusions:

  • AI can be effectively used to augment human intelligence, complementing traditional AI applications in drug discovery.
  • The described recommendation systems demonstrate a practical approach to accelerating the drug discovery process.
  • Integrating AI with existing laboratory infrastructure offers a powerful strategy for innovation in pharmaceutical research.