论文标题
用于分子模拟和药物发现的混合量子生成对抗网络
Hybrid Quantum Generative Adversarial Networks for Molecular Simulation and Drug Discovery
论文作者
论文摘要
在分子研究中，分子的模拟\＆设计是对药物开发，材料科学和其他领域的重要影响的关键领域。当前的经典计算功率不足以模拟除小分子外，更不用说数百种肽上的蛋白质链了。因此，这些实验是在湿的实验中进行的，但是由于搜索区域的大小，需要大量时间检查每个分子，这些研究实验每年都花费数万美元。分子仿真\＆设计最近通过机器学习模型显着提高，对化学合成问题的新观点是由深层生成模型为图形结构化数据提供的。通过优化直接产生分子图的可区分模型，可以避免在化学结构的离散和巨大空间中避免昂贵的搜索技术。但是，当尺寸变得巨大并消耗大量资源时，这些模型也遭受了计算限制。近年来，量子生成的机器学习显示了一些经验结果，有望比经典的同行具有显着优势。
In molecular research, simulation \& design of molecules are key areas with significant implications for drug development, material science, and other fields. Current classical computational power falls inadequate to simulate any more than small molecules, let alone protein chains on hundreds of peptide. Therefore these experiment are done physically in wet-lab, but it takes a lot of time \& not possible to examine every molecule due to the size of the search area, tens of billions of dollars are spent every year in these research experiments. Molecule simulation \& design has lately advanced significantly by machine learning models, A fresh perspective on the issue of chemical synthesis is provided by deep generative models for graph-structured data. By optimising differentiable models that produce molecular graphs directly, it is feasible to avoid costly search techniques in the discrete and huge space of chemical structures. But these models also suffer from computational limitations when dimensions become huge and consume huge amount of resources. Quantum Generative machine learning in recent years have shown some empirical results promising significant advantages over classical counterparts.
