English(EN) Quantum-inspired Reinforcement Learning for Synthesizable Drug Design

人工智能与量子计算通过新颖的分子设计方法推动药物发现

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-07 04:00

研究人员开发了新颖的生成式人工智能框架，集成了量子退火和量子启发强化学习，以增强药物发现的分子设计。这些方法旨在生成具有改进的类药性和可合成性的分子，在某些方面超越了传统的经典模型甚至训练数据。这些方法利用新的目标函数和神经网络架构来更有效地导航复杂的化学空间。 AI

影响这些量子增强的人工智能方法可以通过生成更有效且可合成的候选药物来加速药物发现。

排序理由该集群包含两篇 arXiv 论文，详细介绍了用于分子设计的新颖人工智能方法。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Hayato Kunugi, Mohsen Rahmani, Yosuke Iyama, Yutaro Hirono, Akira Suma, Matthew Woolway, Vladimir Vargas-Calder\'on, William Kim, Kevin Chern, Mohammad Amin, Masaru Tateno · 2026-05-08 04:00

Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer

arXiv:2602.15451v2 Announce Type: replace-cross Abstract: Deep generative modeling to stochastically design small molecules is an emerging technology for accelerating drug discovery and development. However, one major issue in molecular generative models is their lower frequency …
arXiv cs.LG TIER_1 English(EN) · Dannong Wang, Jintai Chen, Yingzhou Lu, Minjie Shen, Lulu Chen, Zhiding Liang, Tianfan Fu, Xiao-Yang Liu · 2026-05-07 04:00

Quantum-inspired Reinforcement Learning for Synthesizable Drug Design

arXiv:2409.09183v2 Announce Type: replace Abstract: Synthesizable molecular design (also known as synthesizable molecular optimization) is a fundamental problem in drug discovery, and involves designing novel molecular structures to improve their properties according to drug-rele…