Graph Neural Network and Artificial Based Models in Preclinical Pharmacology: Predictivity, Opportunities and Challenges
Major challenges in preclinical pharmacology, such as low predictivity, high rates of attrition, prolonged development cycles, and high costs remain significant issues as they largely relate to how translational relevance of past experimental models. This work aims to assess the potential of artificial intelligence-based models, especially graph neural networks in enhancing the predictivity, opportunity, discovery and challenges solving in preclinical pharmacology. This is a synthesis review on machine learning, deep learning and graph-based architectures being used to predict molecular properties, toxicity, drug-target and drug-drug interaction, drug repurposing and de-novo molecule design and their methodological basis and application in preclinical workflows. The reviewed literature reveals that AI models, particularly graph neural networks, may be useful in learning complex chemical and biological relationships, leading to improved prediction of ADMET properties, toxicity endpoints, pharmacokinetics, and therapeutic efficacy, while reducing reliance on animal models and accelerating candidate prioritization. Despite these advances, significant limitations persist, including data scarcity and heterogeneity, limited interpretability, bias, scalability constraints, and challenges in generalization across biological systems and regulatory acceptance. In conclusion, artificial intelligence and graph neural network–based approaches represent a transformative paradigm for preclinical pharmacology by enhancing predictivity, efficiency, and translational relevance; however, their successful integration into drug development will depend on advances in high-quality data generation, explainable and hybrid modeling strategies, standardized validation practices, and ethical and regulatory alignment