Chemi-net: a graph convolutional network for accurate drug property prediction

Understand

Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery.

• Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints.
• Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction.
• To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen.