Drug and drug candidate building block analysis

Area of Science:

• Medicinal Chemistry
• Computational Drug Design

Background:

• Traditional drug likeness filters like Lipinski's Rule of 5 rely on basic structural and physicochemical properties.
• A need exists for more comprehensive methods to identify drug-like molecules in drug discovery pipelines.

Purpose of the Study:

• To identify and rank common molecular building blocks across large datasets of approved and experimental drugs.
• To provide insights for medicinal chemists in designing novel drug candidates and optimizing screening libraries.

Main Methods:

• Enumeration of all possible molecular fragments from two drug datasets (ADDS and EDDS) using a brute-force approach.
• Identification and frequency-based ranking of drug scaffolds, ring systems, and small fragments.
• Ranking of building blocks by hit rates in drug and screening compound datasets.

Main Results:

• A significant overlap of top-ranked fragments was observed between the approved drug dataset (ADDS) and the experimental drug dataset (EDDS).
• The top 50 fragments collectively represent over 48% of the molecules in both datasets.
• Numerous high-quality building blocks were identified, surpassing previous reports.

Conclusions:

• The identified common building blocks are crucial for drug-likeness and can guide medicinal chemists in molecular design.
• These findings offer a novel approach to prioritize screening libraries and identify molecules with essential functional components.