Likun Wang, Na Li, Mengyao Cao, Yun Zhu, Xiewei Xiong, Li Li, Tong Zhu, Hao Pei
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引用次数: 0
Abstract
In this study, a deep learning model based on quantum chemistry is introduced to enhance the accuracy and efficiency of predicting DNA reaction parameters. By integrating quantum chemical calculations with self-designed descriptor matrices, the model offers a comprehensive description of energy variations and considers a broad range of relevant factors. To overcome the challenge of limited labeled data, an active learning method is employed. The results demonstrate that this model outperforms existing methods in predicting DNA hybridization free energies and strand displacement rate constants, thus advancing the understanding of DNA molecular interactions, and aiding in the precise design and optimization of DNA-based systems.
本研究介绍了一种基于量子化学的深度学习模型,以提高预测 DNA 反应参数的准确性和效率。通过将量子化学计算与自主设计的描述矩阵相结合,该模型提供了对能量变化的全面描述,并考虑了广泛的相关因素。为了克服标注数据有限的挑战,该模型采用了主动学习方法。结果表明,该模型在预测 DNA 杂交自由能和链位移速率常数方面优于现有方法,从而推进了对 DNA 分子相互作用的理解,并有助于基于 DNA 的系统的精确设计和优化。
期刊介绍:
Advanced Science is a prestigious open access journal that focuses on interdisciplinary research in materials science, physics, chemistry, medical and life sciences, and engineering. The journal aims to promote cutting-edge research by employing a rigorous and impartial review process. It is committed to presenting research articles with the highest quality production standards, ensuring maximum accessibility of top scientific findings. With its vibrant and innovative publication platform, Advanced Science seeks to revolutionize the dissemination and organization of scientific knowledge.