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Accelerated Discovery of Carbamate Cbl-b Inhibitors Using Generative AI Models and Structure-Based Drug Design.

Taylor R QuinnKathryn A GiblinClare ThomsonJeffrey A BoerthGayathri BommakantiErin BraybrookeChristina ChanAlex J ChinnErin CodeCaifeng CuiYukai FanNeil P GrimsterKeishi KoharaMichelle L LambLina MaAdelphe M MfuhGraeme R RobbKevin J RobbinsMarianne SchimplHaoran TangJamie WareGail L WrigleyLin XueYun ZhangHuimin ZhuSamantha J Hughes
Published in: Journal of medicinal chemistry (2024)
Casitas B-lymphoma proto-oncogene-b (Cbl-b) is a RING finger E3 ligase that has an important role in effector T cell function, acting as a negative regulator of T cell, natural killer (NK) cell, and B cell activation. A discovery effort toward Cbl-b inhibitors was pursued in which a generative AI design engine, REINVENT, was combined with a medicinal chemistry structure-based design to discover novel inhibitors of Cbl-b. Key to the success of this effort was the evolution of the "Design" phase of the Design-Make-Test-Analyze cycle to involve iterative rounds of an in silico structure-based drug design, strongly guided by physics-based affinity prediction and machine learning DMPK predictive models, prior to selection for synthesis. This led to the accelerated discovery of a potent series of carbamate Cbl-b inhibitors.
Keyphrases
  • machine learning
  • artificial intelligence
  • high throughput
  • emergency department
  • diffuse large b cell lymphoma
  • magnetic resonance imaging
  • molecular dynamics simulations
  • single cell
  • capillary electrophoresis