Advancing Drug Discovery with Deep Learning: Harnessing Reinforcement Learning and One-Shot Learning for Molecular Design in Low-Data Situations
Liang Feng Dong, Yuchen Qian, Paulina Gonzalez, Orhan K. Öz, Xiankai Sun · ACM SIGAPP Applied Computing Review · 2023
Drug discovery is a complex process that involves exploring vast chemical spaces to identify potential candidates for the development of effective drugs. While deep learning techniques have shown significant promise in data mining and can be used for molecular design, most drug discovery projects face limitations in low-data situations, making it difficult to train deep learning neural networks. In response to this challenge, this paper proposes a novel drug design system based on deep learning that adopts one-shot learning and reinforcement learning to operate in low-data conditions and generate new molecules with desired properties. Numerical experimental results show that our system can produce valid molecules with desired properties, including high negative logarithm of the half maximal inhibitory concentration (pIC50) values and logarithmic partition co-efficients (log P ) values between 0 and 5. This model is applicable to other molecular design projects with limited data sets, thereby enhancing drug discovery efficiency and effectiveness.