The future of in silico toxicology is promising, with ongoing advancements in computational technologies and data science. Key trends include:
Integration with Omics Technologies: Combining in silico models with genomics, proteomics, and other omics data can enhance the accuracy and predictability of toxicological assessments. Advanced Machine Learning: The use of advanced machine learning techniques, such as deep learning, can improve the predictive power of in silico models by analyzing complex datasets more effectively. Personalized Toxicology: In silico studies may enable personalized toxicology assessments, considering individual genetic and environmental factors to predict specific toxicological risks. Regulatory Acceptance: As in silico models become more validated and reliable, they are likely to gain greater acceptance by regulatory agencies, streamlining the safety assessment process for new chemicals and drugs.
