SENSAAS-Bioisostere: A computational method for 3D shape-guided bioisosteric replacements and scaffold-hopping

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Drug discovery

Background:

• Identifying novel drug candidates often involves exploring bioisosteric replacements and scaffold-hopping.
• Existing methods may lack the precision to effectively compare molecular fragments for similarity.

Purpose of the Study:

• To introduce SENSAAS-Bioisostere, a computational method for identifying bioisosteric replacements and scaffold-hopping.
• To enable the design of new molecules with similar shapes and pharmacophores to existing drug fragments.

Main Methods:

• Utilizes the SENSAAS method's matching and sub-matching properties to compare molecular fragments.
• Represents fragments as colored 3D point clouds capturing shape, pharmacophore, and substituent information.
• Employs 3D point set registration for aligning molecular fragments by minimizing point distances.

Main Results:

• SENSAAS-Bioisostere successfully identifies fragments with similar shape and pharmacophore properties.
• The method builds and aligns new molecules based on query fragments.
• Retrospective analysis confirmed the effectiveness of the approach in scaffold-hopping for new molecule discovery.

Conclusions:

• SENSAAS-Bioisostere is an effective computational tool for bioisosteric replacement and scaffold-hopping.
• The method facilitates the search for novel drug candidates by leveraging fragment similarity.
• The unique representation and alignment approach enhance the accuracy of molecular comparison.