Chemistry-driven Hit-to-lead Optimization Guided by Structure-based Approaches.

Area of Science:

• Medicinal Chemistry
• Computational Drug Discovery
• Chemical Biology

Background:

• Fragment-based and high-throughput screening have advanced hit identification in drug discovery.
• Optimizing initial screening hits into viable lead compounds remains a significant challenge in drug development.
• Computational chemistry and molecular modeling offer potential solutions to streamline the hit-to-lead (H2L) process.

Purpose of the Study:

• To review chemistry-driven and structure-based computational methods for hit-to-lead (H2L) optimization.
• To emphasize strategies for improving the efficiency and success rate of drug discovery projects.
• To highlight the importance of synthetic feasibility and medicinal relevance in computational drug design.

Main Methods:

• Focus on chemistry-driven computational approaches for H2L optimization.
• Utilize structure-based computational methods to guide molecular design.
• Integrate considerations for synthetic accessibility and drug-likeness in virtual screening.
• Emphasize a specific laboratory-developed strategy for H2L optimization.

Main Results:

• Computational methods can suggest molecular optimizations and reduce the number of compounds needing experimental synthesis.
• Successful H2L optimization requires balancing virtual design with synthetic feasibility and medicinal relevance.
• Structure-based and chemistry-driven approaches are crucial for efficient lead compound generation.

Conclusions:

• Computational chemistry is pivotal in overcoming the hit-to-lead bottleneck in drug discovery.
• Integrating synthetic feasibility and medicinal relevance into computational strategies enhances lead optimization.
• The reviewed methods, particularly the laboratory's strategy, offer a robust framework for advancing drug discovery pipelines.