Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?

Area of Science:

• Computational Chemistry
• Toxicology
• Drug Discovery

Background:

• Quantum mechanics (QM) is standard in chemistry and drug discovery for studying molecular interactions.
• Predictive toxicology has not widely adopted QM, despite its relevance to metabolism and adverse outcome pathways.
• Advances in QM methods and computing power now enable practical toxicological applications.

Purpose of the Study:

• To advocate for the integration of QM into toxicological assessments.
• To highlight QM's complementary role to existing methods like QSARs for chemical safety.
• To propose advancements in in silico toxicology through QM and AI integration.

Main Methods:

• Reviewing the current state of QM application in chemistry and toxicology.
• Analyzing roadblocks to QM adoption in toxicology, including training and infrastructure.
• Presenting examples of successful QM implementation in hazard assessment.

Main Results:

• QM offers unique insights orthogonal to traditional toxicology methods.
• Successful QM applications in hazard assessment have been demonstrated.
• The pharmaceutical industry already utilizes QM extensively in drug discovery.

Conclusions:

• QM should be an essential tool for toxicologists, enhancing predictive capabilities.
• Integrating QM with AI can significantly advance in silico toxicology and safety testing.
• Embracing QM supports the development of animal-free testing strategies for chemical safety.