Advances in Fragment-based Drug Design: Lessons and Innovations from the Post-COVID Drug Discovery Landscape

Fragment-based drug discovery (FBDD) has emerged as a transformative strategy in modern medicinal chemistry, offering a rational and efficient alternative to traditional high-throughput screening (HTS). By utilizing small, low-molecular-weight fragments with moderate binding affinity, FBDD enables systematic optimization into potent lead compounds with improved physicochemical properties. Its modular and ligand-centric nature has proven particularly advantageous in accelerating early-stage drug discovery. The COVID-19 pandemic highlighted the adaptability of FBDD, as fragment screening and computational modeling rapidly identified inhibitors of the SARS-CoV-2 main protease (M^pro). Integration with artificial intelligence (AI) and cloud-based platforms further enhanced the speed and global accessibility of fragment campaigns, setting a precedent for collaborative, open-science initiatives. Beyond infectious diseases, FBDD has demonstrated significant promise in oncology, antibacterial therapy, and neurodegenerative disorders, reflecting its versatility across diverse therapeutic landscapes. Recent technological advances have expanded the scope of FBDD. High-resolution cryo-electron microscopy and AI-driven structural prediction now enable the exploration of previously inaccessible or dynamic protein targets. Emerging modalities, such as PROTACs and RNA-targeted therapeutics, also intersect with fragment-based strategies, opening avenues for addressing so-called “undruggable” proteins. Despite persistent challenges, including the need for sensitive biophysical methods and sophisticated infrastructure, the approach continues to evolve. Looking ahead, the convergence of FBDD with machine learning, open-access fragment libraries, and global research collaboration positions it as a scalable, adaptive platform for drug discovery. As future health threats demand rapid innovation, FBDD is poised to remain a cornerstone of both academic and industrial research pipelines.