De novo design - hop(p)ing against hope

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Traditional drug discovery methods face challenges in identifying novel chemical entities.
• Scaffold-hopping is a key strategy for discovering new drugs with improved properties.
• Computational approaches are increasingly vital in modern drug discovery.

Purpose of the Study:

• To explore current trends in computational de novo design for drug discovery.
• To highlight the advantages of ligand-based and fragment-based design techniques.
• To demonstrate how advanced algorithms can identify novel drug-like molecules.

Main Methods:

• Utilizing computational de novo design methodologies.
• Employing ligand-based techniques considering multiple properties in parallel.
• Implementing fragment-based and virtual reaction-driven design.
• Applying advanced mathematical models of structure-activity landscapes.
• Leveraging multi-objective design techniques.

Main Results:

• Computational de novo design provides innovative scaffold-hopping approaches.
• Ligand-based methods enable parallel consideration of molecular properties, including synthetic feasibility and polypharmacology.
• Fragment-based and virtual reaction-driven design facilitate rapid de novo compound optimization.
• Algorithms suggest drug-like molecules with scaffold variations based on known drugs.
• Advanced models and multi-objective techniques enhance hit and lead identification.

Conclusions:

• Computational de novo design, particularly ligand-based and fragment-based approaches, offers powerful strategies for drug discovery.
• These methods enable the efficient identification of novel chemical entities with desirable properties and manageable complexity.
• Advanced computational techniques are expanding opportunities for hit and lead finding in pharmaceutical research.