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Advances in artificial intelligence for predictive toxicology: From QSAR and omics integration to clinical safety

Pranay Wal1, Jyotsana Dwivedi1, Kanika Pandey1

  • 1PSIT, Pranveer Singh Institute of Technology (Pharmacy), Kanpur, India.

Computational Biology and Chemistry
|May 16, 2026
PubMed

Insights

Artificial intelligence (AI) enhances predictive toxicology in drug discovery by analyzing big data for early toxicity detection and adverse drug reaction prediction, leading to safer medications. Explainable AI and digital twins further improve model credibility and reduce animal testing.

Area of Science:

  • Pharmacology and Toxicology
  • Computational Biology
  • Drug Discovery

Background:

  • Traditional toxicology methods (in vitro, in vivo) are time-consuming, costly, and poorly correlate with human responses.
  • High clinical trial attrition due to unexpected toxicities complicates drug discovery and necessitates improved predictive models.
  • Machine learning (ML), deep learning, and artificial intelligence (AI) offer novel approaches to predictive toxicology.

Purpose of the Study:

  • To review the application of AI in redefining predictive toxicology for drug discovery.
  • To highlight AI's role in early toxicity detection, adverse drug reaction (ADR) prediction, and enhancing drug safety.
  • To discuss the potential of explainable AI (XAI), digital twins, and in silico models in transforming drug development and regulation.

Main Methods:

  • Review of current literature on AI applications in predictive toxicology.
  • Analysis of how AI models mine big data to identify complex associations related to drug safety.
  • Exploration of emerging technologies like XAI and digital twins for enhanced predictive capabilities.

Main Results:

  • AI enables more efficient big data mining for uncovering complex associations relevant to drug safety.
  • AI models are applicable to early toxicity detection and adverse drug reaction (ADR) prediction.
  • Emerging AI technologies promise improved transparency, credibility, and personalization in drug discovery.

Conclusions:

  • AI significantly enhances predictive toxicology, leading to the development of safer drugs.
  • Explainable AI (XAI) can improve the trustworthiness of AI-driven toxicity predictions.
  • Digital twins and in silico models offer a future with reduced reliance on animal studies and personalized drug development.
Keywords:
Artificial IntelligenceDrug SafetyExplainable AIMachine LearningPharmacovigilancePredictive Toxicology

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