Current Approaches and Strategies Applied in First-in-class Drug Discovery.

Area of Science:

• Pharmacology and Drug Discovery
• Medicinal Chemistry
• Computational Biology

Background:

• First-in-class drug discovery (FICDD) is crucial for novel therapies targeting unmet diseases.
• Understanding unexplored biology and identifying new mechanisms of action (MOAs) are key FICDD goals.
• Current drug discovery relies on established screening methods and advanced technologies.

Purpose of the Study:

• To review current screening approaches in FICDD, including phenotypic drug discovery (PDD) and target-based drug discovery (TBDD).
• To highlight recent advancements and applications of functional genomics, structural biology, AI, and ML in FICDD.
• To overview medicinal chemistry strategies for hit identification and optimization, such as hit-to-lead (HTL) and lead optimization (LO).

Main Methods:

• Literature review and classification of screening approaches in small molecule drug discovery (SMDD).
• Discussion of PDD and TBDD methodologies with relevant examples.
• Analysis of advanced techniques including functional genomics, structural biology, AI, and ML in FICDD.

Main Results:

• FICDD employs diverse screening strategies, broadly categorized into PDD and TBDD.
• Advanced methods like AI, ML, and functional genomics are increasingly integrated into FICDD.
• Medicinal chemistry plays a vital role through hit-to-lead (HTL) and lead optimization (LO) processes.

Conclusions:

• FICDD is essential for developing innovative treatments and understanding complex diseases.
• A combination of traditional and advanced screening methods, alongside robust medicinal chemistry, drives successful FICDD.
• The integration of AI, ML, and genomics promises to accelerate the discovery of novel therapeutics.