arXiv cs.AI· Julien Baglio, Yacine Haddad, Richard Polifka·· 14 小时前AI 评分18
基于潜在风格的量子 Wasserstein GAN 用于药物设计
Latent Style-based Quantum Wasserstein GAN for Drug Design
AI 导读
研究提出一种潜在风格量子 Wasserstein GAN（QGAN），将变分自编码器与混合量子 GAN 结合用于药物设计，默认生成器仅 110 个可训练参数，在 MOSES 基准上整体性能与约 70 万参数的经典潜在 GAN 基线统计相当，并在部分药物相关指标上取得统计显著提升。
正文 · 原文
Abstract:The development of new drugs is a time-consuming and expensive process, with average costs estimated to be up to around $2.5 billion. The first step in this process is the design of the new drug, for which de novo drug design, assisted by artificial intelligence, has blossomed in recent years and has revolutionized the field. In particular, generative artificial intelligence has delivered promising results in drug discovery and development, reducing costs and time to solution. However, classical generative models, such as generative adversarial networks (GANs), can be difficult to train and are prone to mode collapse. Quantum computing may be an avenue to overcome some of these issues, providing compact models with far fewer trainable parameters, thereby enhancing the generalizability of GANs. We propose a new latent style-based quantum Wasserstein GAN (QGAN) for drug design, combining a variational autoencoder that maps molecular structures into a latent space with a hybrid quantum GAN. Our pipeline is benchmarked against classical models of the industry-relevant MOSES suite. Style-based noise reuploading at every rotational gate and a Wasserstein gradient penalty are used to improve trainability and to mitigate mode collapse. Training and large-scale sampling with circuits of up to 15 qubits are performed on noiseless quantum simulators to validate our architecture. We present an inference-only demonstration on an IBM Heron quantum computer using 5 qubits, sampling 2,500 new molecules, to investigate quantum noise effects. Our default QGAN, using 110 trainable generator parameters, reaches statistically comparable overall performance and statistically significant gains on selected drug-relevant metrics relative to both the MOSES classical latent GAN baseline, which contains about 700,000 trainable parameters for the generator, and a classical latent GAN with matched generator capacity.
| Comments: | Main part: 26 pages, 11 figures, 6 tables. Supplementary material: 24 pages, 20 figures, 21 tables. v2: revision containing matched-capacity analysis of the classical GAN and extensive details on the hardware inference run, plus bootstrap error analysis of the hardware results. Matches published version |
| Subjects: | Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Biomolecules (q-bio.BM) |
| Cite as: | arXiv:2603.22399 [quant-ph] |
| (or arXiv:2603.22399v2 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2603.22399 arXiv-issued DOI via DataCite |
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| Journal reference: | J. Chem. Inf. Model. (2026) 66 (20): 13136-13154 |
| Related DOI: | https://doi.org/10.1021/acs.jcim.6c01249
DOI(s) linking to related resources |
Submission history
From: Julien Baglio [view email]
[v1]
Mon, 23 Mar 2026 18:00:12 UTC (1,108 KB)
[v2]
Wed, 28 Oct 2026 08:58:53 UTC (1,373 KB)
来源：arXiv cs.AI · arxiv.org