Privileged substructures for anti-sickling activity via cheminformatic analysis.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Genetics

Background:

• Sickle Cell Disease (SCD) is a major global health problem affecting over 300,000 individuals.
• SCD complications include anemia, pain, stroke, and organ damage, necessitating effective therapeutic strategies.
• Anti-sickling agents that prevent HbS polymerization are a promising treatment avenue.

Purpose of the Study:

• To identify privileged substructures responsible for the anti-sickling activity of chemical compounds.
• To develop robust Classification Structure-Activity Relationship (CSAR) models for predicting anti-sickling potential.
• To guide the rational design of novel anti-sickling agents.

Main Methods:

• Employed cheminformatic approaches, including fingerprint descriptors and CSAR modeling with machine learning algorithms.
• Assessed dataset modelability using the MODI index (0.70-0.84).
• Evaluated predictive performance using accuracy, sensitivity, specificity, and Matthews correlation coefficient.

Main Results:

• Developed statistically robust CSAR models with high predictive performance (accuracy, sensitivity, specificity > 0.7; MCC > 0.5).
• Identified key substructures associated with anti-sickling activity, including aromatic/conjugation, carbonyl, and miscellaneous groups.
• Determined that alkyl chain length, functional moiety, and substitution position influence anti-sickling activity.

Conclusions:

• The study successfully identified critical molecular features for anti-sickling activity.
• The developed CSAR models provide a data-driven approach for designing effective anti-sickling compounds.
• These findings offer valuable insights for developing new therapeutics against HbS gelling and SCD.