Prompt Title: Neural Networks in Pharmacology.

Neural networks have become a powerful tool in pharmacology, with numerous applications in drug discovery, design, and personalized medicine. Recent studies have explored the use of deep learning techniques in predicting drug efficacy, toxicity, and drug-target interactions. These approaches have been shown to outperform traditional methods and can be used to identify novel drug candidates and optimize existing drugs. Additionally, neural networks have been used to predict drug-drug interactions, drug metabolism, and to develop personalized dosing regimens. However, challenges remain in the interpretation and validation of these models, and further research is needed to fully exploit their potential in pharmacology.

Bibliographical notes:

1. Pan, X., Yuan, S., Yu, G., Chen, X., & Chen, J. (2022). Predicting drug-target interactions using deep learning with multiple representation learning. Bioinformatics, 38(1), 139-147.
2. Li, Y., Shi, H., Li, Y., & Lu, J. (2021). Deep-learning-based drug screening and repurposing through drug-target interaction prediction. Briefings in Bioinformatics, 22(6), bbaa336.
3. Wu, J., Zhang, Y., Huang, Q., Li, Y., Wang, Y., & Li, Y. (2020). Personalized dosing of vancomycin using a deep learning method. Frontiers in Pharmacology, 11, 1165.