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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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AI-Enhanced Adaptive Virtual Screening Platform Enabling Exploration of 69 Billion Molecules Discovers Structurally

Domiziana Cecchini1, AkshatKumar Nigam2,3, Ming Tang4,5,6

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AdaptiveFlow accelerates drug discovery by enabling ultra-large virtual screenings (ULVSs) of billions of molecules. This open-source platform uses a novel grid of molecular properties to efficiently identify potent lead compounds for specific protein targets.

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Area of Science:

  • Computational chemistry and drug discovery.
  • Development of open-source platforms for large-scale molecular screening.

Background:

  • Drug discovery is hindered by the challenge of identifying potent lead molecules for specific targets.
  • Ultra-large virtual screenings (ULVSs) offer a promising approach to accelerate early-stage drug discovery by computationally evaluating billions of compounds.

Purpose of the Study:

  • To introduce AdaptiveFlow, an open-source platform designed to enhance the accessibility, scalability, and efficiency of ULVSs.
  • To provide researchers with a powerful tool for exploring and prioritizing vast chemical spaces.

Main Methods:

  • Development of AdaptiveFlow, an open-source platform for ULVSs.
  • Utilizing a multi-dimensional grid of molecular properties to guide chemical space exploration and reduce computational costs.
  • Incorporating an optional active learning component to adaptively steer the search for potential drug candidates.
  • Ensuring compatibility with over 1,500 docking methods and demonstrating near-linear scaling on large cloud computing resources.

Main Results:

  • AdaptiveFlow provides access to 69 billion screening-ready molecules from the Enamine REAL Space.
  • The platform reduces computational costs by approximately 1000-fold through efficient chemical space exploration.
  • Near-linear scaling was achieved on up to 5.6 million CPUs in the AWS Cloud.
  • Nanomolar inhibitors were identified for ferroptosis suppressor protein 1 (FSP1) and poly(ADP-ribose) polymerase 1 (PARP-1).
  • First co-crystal structures of FSP1 bound to small-molecule inhibitors were determined, providing novel insights into binding mechanisms.

Conclusions:

  • AdaptiveFlow significantly enhances the efficiency and scalability of ULVSs, making them more accessible to researchers.
  • The platform enables the rapid identification and optimization of drug candidates at an unprecedented scale.
  • Experimental validation and structural studies confirm the platform's ability to discover potent inhibitors for disease-relevant targets.