Computational/in silico methods in drug target and lead prediction

Area of Science:

• Computational drug discovery
• Pharmacology
• Bioinformatics

Background:

• Drug development faces high attrition rates due to unexpected clinical side effects and cross-reactivity.
• These failures often stem from inadequate understanding of drug targets and unpredicted pharmacokinetic interactions.
• Identifying and validating drug targets, especially for complex polygenic diseases, remains a significant bottleneck.

Purpose of the Study:

• To provide an overview of computational methods and tools for predicting and validating drug targets and drug-like molecules.
• To compare the advantages and effectiveness of various computational approaches in drug discovery.
• To explore common causes of drug failure and identify opportunities for improvement.

Main Methods:

• Review of existing computational methods and tools used in drug target identification and validation.
• Comparative analysis of different computational approaches based on their strengths and weaknesses.
• Exploration of drug failure sources and associated challenges in the drug development pipeline.

Main Results:

• Computational methods offer valuable alternatives and complements to experimental approaches in drug discovery.
• Different computational tools possess unique advantages for specific aspects of target identification and validation.
• Understanding target properties and potential off-target effects is critical for minimizing drug failure.

Conclusions:

• This review guides researchers in selecting efficient computational strategies for drug discovery.
• Effective computational approaches can significantly improve the success rate of drug development by optimizing target selection.
• Addressing challenges in target identification and validation through computational means is key to reducing drug attrition.