In silico tools used for compound selection during target-based drug discovery and development

Area of Science:

• Drug Discovery
• Computational Chemistry
• Pharmacology

Background:

• The target-based drug discovery process is standard in pharmaceutical R&D.
• Integrating in silico tools with in vitro/in vivo data can enhance R&D productivity.
• Effective selection criteria and drug design strategies are crucial for success.

Purpose of the Study:

• To review the impact of in silico tools in target-based drug discovery over the last five years.
• To understand the application of these tools across different stage gates.
• To identify challenges and opportunities for improving R&D productivity.

Main Methods:

• Literature review focusing on the past 5 years.
• Analysis of in silico tool application in target selection, screening, hit-to-lead (H2L), and lead optimization.
• Evaluation of data integration strategies.

Main Results:

• Numerous in silico tools exist for selection criteria and optimization.
• Inconsistent integration of experimental data with predictive in silico models impacts R&D productivity.
• A notable lack of reliable in silico tools at the H2L stage leads to suboptimal lead compound selection.

Conclusions:

• Further development of in silico multiparameter models is needed.
• Cross-functional teams of biologists and chemists are essential for expanding in silico tool utilization.
• Improved integration and application of in silico tools across all drug discovery phases can boost R&D productivity.