ADMET Prediction

ADMET prediction focuses on computationally estimating the absorption, distribution, metabolism, excretion, and toxicity of drug molecules, crucial for efficient drug discovery and development. Recent research emphasizes the use of large pre-trained foundation models, often employing graph neural networks or other deep learning architectures, trained on massive datasets of molecular structures and properties, and fine-tuned for specific ADMET endpoints. These advancements, along with the development of improved feature representations like combined molecular fingerprints, aim to enhance prediction accuracy and reduce reliance on extensive experimental data, ultimately accelerating the drug development process and improving patient safety.