Deep Learning for a Low-Data Drug Design System
Yuchen Qian, Yuan Xing, Liang Feng Dong · 2021
Molecule design is the process of discovering potential compound candidates for drug discovery. Deep learning technique shows significant advantages in data mining and can be used for molecule design. However, most drug discovery projects are limited to low-data situations, and it is difficult to train deep learning neural networks. This paper proposes a novel drug design system that is based on deep learning. It adopts one-shot learning and reinforcement learning, and it can operate under low-data conditions. Once trained, the system can generate new molecules with the desired properties.