Evaluation of various inverse docking schemes in multiple targets identification

Area of Science:

• Computational chemistry
• Drug discovery
• Bioinformatics

Background:

• Target identification is crucial for drug discovery but faces challenges with current methods.
• Inverse docking has emerged as a promising strategy for target identification.
• The efficacy of various inverse docking strategies for identifying multiple targets remains unclear.

Purpose of the Study:

• To evaluate and compare the effectiveness of five different inverse docking schemes.
• To identify the most efficient inverse docking approach for multiple target identification.
• To assess the utility of inverse docking in predicting drug toxicity.

Main Methods:

• A comprehensive target database was curated, containing 1714 entries from 1594 known drug targets across 18 biochemical functions.
• Five inverse docking engines were tested: GOLD, FlexX, Tarfisdock, TarSearch-X, and TarSearch-M.
• Performance was evaluated using eight multiple target systems within the dataset.

Main Results:

• TarSearch-X demonstrated superior effectiveness in multiple target identification and validation compared to the other four schemes.
• The GOLD docking engine also exhibited acceptable performance in multiple target identification.
• Both TarSearch-X and GOLD show potential for predicting adverse drug effects, including toxicity.

Conclusions:

• TarSearch-X is the recommended inverse docking strategy for identifying multiple drug targets.
• GOLD provides a viable alternative for multiple target identification in drug discovery.
• These validated inverse docking methods can aid in predicting drug safety profiles and potential toxicities.