Harnessing artificial intelligence for genomic variant prediction: advances, challenges, and future directions

Accurate genetic variant interpretation is crucial for disease research and the development of targeted therapies. Artificial intelligence is transforming this field by integrating computational methodologies across structural biology, evolutionary analysis, and multimodal genomic data. This review examines the evolution from traditional rule-based systems and statistical models to contemporary machine learning, deep learning, and protein language models, while addressing critical challenges in variant classification. Key obstacles include data heterogeneity, interpretability, and the persistence of variants of uncertain significance, emphasizing the critical need for explainable artificial intelligence frameworks and more inclusive genomic databases to improve predictive accuracy across diverse populations. Based on the assessment of current variant impact predictors, we propose strategies for enhanced predictor selection, effective multi-omics data integration, and optimized computational workflows. These recommendations aim to enhance variant interpretation accuracy in both research settings and clinical practice, ultimately contributing to advances in personalized medicine.