Drug design for ever, from hype to hope

Area of Science:

• Computational chemistry and cheminformatics in drug discovery.
• Translational science and pharmaceutical industry research.

Background:

• The Journal of Chemical and Pharmaceutical Analysis (JCAMD) has published numerous computational techniques for accelerated drug discovery over 25 years.
• Techniques like genetic algorithms, COMFA, QSAR, structure-based design, homology modeling, high-throughput screening, and combinatorial chemistry have been explored.
• Despite advancements, the rate of FDA-approved new molecular entities has stagnated, prompting industry reorganization and outsourcing.

Purpose of the Study:

• To review the evolution of computational techniques in drug discovery over the past 25 years.
• To highlight the challenges in drug development, including stagnating approval rates and industry restructuring.
• To propose future directions for translational science, emphasizing integration and communication across the drug development pipeline.

Main Methods:

• Review of computational and cheminformatics techniques published in JCAMD.
• Analysis of trends in drug discovery and pharmaceutical industry practices.
• Discussion of the need for enhanced communication and data utilization in translational science.

Main Results:

• A wide array of computational methods have been developed and utilized in drug design.
• The number of FDA-approved drugs per year has not significantly increased despite these efforts.
• The pharmaceutical industry is undergoing significant consolidation and outsourcing of research.

Conclusions:

• Improved integration of all research activities, from basic science to clinical application, is crucial.
• Enhanced communication between academia, industry, and all stakeholders is essential for future success.
• A deeper understanding of biological problems and optimal data utilization will drive progress in translational science.