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Iterative Interaction Fingerprints-Guided Multiobjective Molecular Generation.

Yao-Geng Wang1, Can Dong1, Yu-Ting Chen1

  • 1Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China.

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IFPGen is a new framework for molecular generation that optimizes multiple objectives simultaneously. It guides molecule design using interaction fingerprints, successfully identifying potent inhibitors for human glutaminyl cyclases.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Molecular modeling

Background:

  • Structure-based molecular generation has advanced significantly.
  • Existing methods lack robust multiobjective optimization capabilities.

Purpose of the Study:

  • Introduce IFPGen, a novel framework for interaction fingerprints-guided multiobjective molecular generation.
  • Address the need for methods balancing multiple objectives in molecular design.

Main Methods:

  • IFPGen integrates a conditional diffusion model with an iterative optimization loop.
  • Interaction fingerprints guide molecular generation and refinement.
  • Dynamic updating of reference ligands and interaction patterns facilitates optimization.

Main Results:

  • IFPGen outperforms baseline models in generating molecules with desired interaction patterns.
  • The framework successfully achieved multiobjective optimization.
  • Applied to lead optimization of human glutaminyl cyclase inhibitors, identifying novel compounds.

Conclusions:

  • IFPGen enables efficient multiobjective molecular generation guided by interaction fingerprints.
  • The framework led to the discovery of potent human glutaminyl cyclase inhibitors.
  • Optimized inhibitors show improved potency, potentially via specific hydrogen-bonding interactions.