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

Gary W Caldwell1

  • 1Janssen Research & Development LLC, Discovery Sciences , Spring House, PA , USA gcaldwel@its.jnj.com.

Expert Opinion on Drug Discovery
|May 9, 2015
PubMed
Summary
This summary is machine-generated.

Integrating in silico tools with experimental data in drug discovery can improve R&D productivity. However, inconsistent application, especially at the hit-to-lead (H2L) stage, hinders effective lead compound selection.

Keywords:
high-throughput screeninghit-to-leadin silico toolslead optimizationpharmaceutical crisistarget selectiontarget-based

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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.