Comprehension of drug toxicity: software and databases

Area of Science:

• Computational chemistry
• Toxicology
• Drug discovery

Background:

• Quantitative structure-activity relationships (QSARs) are vital in silico tools for predicting drug toxicity.
• The increasing volume of experimental toxicity data necessitates robust estimation methods.
• Key toxicity endpoints include carcinogenicity, mutagenicity, liver, and cardiac effects.

Purpose of the Study:

• To review existing databases and software for in silico drug toxicity prediction.
• To highlight the importance of robust computational assessments in drug development.
• To support the reduction of animal testing and accelerate the design of new compounds.

Main Methods:

• Analysis of current databases and software for in silico toxicity prediction.
• Evaluation of QSAR/QSPR methodologies for various toxicity endpoints.
• Literature review on the application and advancement of computational toxicology.

Main Results:

• Identification of essential databases and software for reliable in silico toxicity assessments.
• Demonstration of the utility of QSAR models in predicting critical toxicity aspects.
• Overview of the driving forces behind the increased use of in silico toxicity prediction.

Conclusions:

• In silico methods, particularly QSAR, are indispensable for robust drug toxicity prediction.
• Effective utilization of available databases and software is crucial for accurate computational assessments.
• Advancements in computational toxicology contribute significantly to drug safety and development.