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Related Concept Videos

Bioavailability Enhancement: Drug Permeability Enhancement01:27

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Body:After oral administration, poor permeability often limits the rate at which drugs are absorbed through the intestinal epithelium. Enhancing drug permeability is crucial for effective therapy, and several strategies have been developed to overcome this challenge.One effective strategy involves the use of lipid-based formulations. These formulations enhance dissolution and solubility, targeting physiological mechanisms to increase drug absorption. This includes stimulating bile salt...
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Enhancing Permeability Prediction of Heterobifunctional Degraders Using Machine Learning and Metadynamics-Informed 3D

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Three-dimensional descriptors significantly improve passive permeability predictions for heterobifunctional degraders in beyond-rule-of-five chemical space. Molecular compactness, spatial polarity, and hydrogen bonding are key predictors for designing permeable compounds.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Heterobifunctional degraders (a class of targeted protein degraders) often exist in beyond-rule-of-five (bRo5) chemical space.
  • Traditional passive permeability models using 2D descriptors have limited applicability for these molecules.
  • Accurate prediction of passive membrane permeability is crucial for drug design and development.

Purpose of the Study:

  • To explore the predictive value of three-dimensional (3D) molecular descriptors for passive membrane permeability of heterobifunctional degraders in bRo5 space.
  • To compare the performance of machine learning models using 2D, 3D, and combined 2D+3D descriptor sets.
  • To identify key 3D features that govern passive permeability in this challenging chemical space.

Main Methods:

  • Conformational ensembles were generated using well-tempered metadynamics in explicit chloroform.
  • Ensembles were refined and Boltzmann-weighted using ANI-2x neural network potentials.
  • Three machine learning models (Random Forest, PLS, LSVM) were trained and evaluated using 2D, 3D, and combined descriptor sets.

Main Results:

  • Inclusion of 3D descriptors consistently improved predictive performance across all models, especially in bRo5 space.
  • Partial Least Squares (PLS) model showed the best performance, with cross-validated R-squared improving from 0.29 to 0.48 with 3D features.
  • Feature importance analysis identified radius of gyration (Rgyr) as the dominant 3D descriptor, followed by 3D polar surface area (3D-PSA) and intramolecular hydrogen bonds (IMHBs).

Conclusions:

  • Physically meaningful, ensemble-derived 3D descriptors significantly enhance passive permeability prediction for heterobifunctional degraders in bRo5 chemical space.
  • 3D descriptors like Rgyr, 3D-PSA, and IMHBs reflect molecular compactness, spatial polarity, and internal hydrogen bonding, which are key determinants of permeability.
  • The developed Amber-based molecular dynamics workflow is broadly applicable for evaluating permeability properties of heterobifunctional degraders.