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Computational Approaches and Structure-Based Drug Design of CAIs.

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Developing selective inhibitors for human carbonic anhydrases (hCAs) is crucial for treating various diseases. Computational chemistry, AI, and machine learning accelerate the discovery of targeted carbonic anhydrase inhibitors (CAIs).

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Area of Science:

  • Biochemistry and Medicinal Chemistry
  • Computational Drug Discovery

Background:

  • Human carbonic anhydrases (hCAs) are implicated in numerous diseases, including glaucoma, epilepsy, cancer, and metabolic disorders.
  • Developing selective inhibitors (CAIs) is challenging due to 15 hCA isoforms with diverse functions and tissue distribution.

Purpose of the Study:

  • To provide a comprehensive overview of in silico strategies for designing selective carbonic anhydrase inhibitors (CAIs).
  • To highlight the role of computational chemistry, AI, and machine learning in accelerating the discovery of novel CAIs.

Main Methods:

  • Utilized pharmacophore modeling, Virtual Screening, docking, molecular dynamics (MD), MM-GBSA, and AI/ML-driven predictive modeling.
  • Emphasized structure-based drug design (SBDD) and ligand-based approaches, focusing on structure-function relationships and active-site features for isoform differentiation.

Main Results:

  • Demonstrated successful application of in silico strategies in identifying selective hCA inhibitors through case studies.
  • Highlighted ML/DL frameworks achieving high accuracy in predicting CAI potency and selectivity using large biochemical datasets.

Conclusions:

  • Computational methodologies and AI significantly enhance the identification and optimization of selective CAIs.
  • The integration of explainable AI with experimental validation offers a powerful pipeline for rational drug design against hCAs.