Utilizing reinforcement learning for de novo drug design
| العنوان: | Utilizing reinforcement learning for de novo drug design |
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| المؤلفون: | Gummesson Svensson, Hampus, 1996, Tyrchan, Christian, Engkvist, Ola, 1967, Haghir Chehreghani, Morteza, 1982 |
| المصدر: | Machine Learning. 113(7):4811-4843 |
| مصطلحات موضوعية: | Reinforcement learning, De novo drug design, Policy optimization, Replay buffer, Recurrent neural network |
| الوصف: | Deep learning-based approaches for generating novel drug molecules with specific properties have gained a lot of interest in the last few years. Recent studies have demonstrated promising performance for string-based generation of novel molecules utilizing reinforcement learning. In this paper, we develop a unified framework for using reinforcement learning for de novo drug design, wherein we systematically study various on- and off-policy reinforcement learning algorithms and replay buffers to learn an RNN-based policy to generate novel molecules predicted to be active against the dopamine receptor DRD2. Our findings suggest that it is advantageous to use at least both top-scoring and low-scoring molecules for updating the policy when structural diversity is essential. Using all generated molecules at an iteration seems to enhance performance stability for on-policy algorithms. In addition, when replaying high, intermediate, and low-scoring molecules, off-policy algorithms display the potential of improving the structural diversity and number of active molecules generated, but possibly at the cost of a longer exploration phase. Our work provides an open-source framework enabling researchers to investigate various reinforcement learning methods for de novo drug design. |
| وصف الملف: | electronic |
| URL الوصول: | https://research.chalmers.se/publication/540717 https://research.chalmers.se/publication/540717/file/540717_Fulltext.pdf |
| قاعدة البيانات: | SwePub |
| تدمد: | 08856125 15730565 |
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| DOI: | 10.1007/s10994-024-06519-w |