تقرير
Quantum-Inspired Machine Learning for Molecular Docking
| العنوان: | Quantum-Inspired Machine Learning for Molecular Docking |
|---|---|
| المؤلفون: | Shu, Runqiu, Liu, Bowen, Xiong, Zhaoping, Cui, Xiaopeng, Li, Yunting, Cui, Wei, Yung, Man-Hong, Qiao, Nan |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Physics - Chemical Physics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | Molecular docking is an important tool for structure-based drug design, accelerating the efficiency of drug development. Complex and dynamic binding processes between proteins and small molecules require searching and sampling over a wide spatial range. Traditional docking by searching for possible binding sites and conformations is computationally complex and results poorly under blind docking. Quantum-inspired algorithms combining quantum properties and annealing show great advantages in solving combinatorial optimization problems. Inspired by this, we achieve an improved in blind docking by using quantum-inspired combined with gradients learned by deep learning in the encoded molecular space. Numerical simulation shows that our method outperforms traditional docking algorithms and deep learning-based algorithms over 10\%. Compared to the current state-of-the-art deep learning-based docking algorithm DiffDock, the success rate of Top-1 (RMSD<2) achieves an improvement from 33\% to 35\% in our same setup. In particular, a 6\% improvement is realized in the high-precision region(RMSD<1) on molecules data unseen in DiffDock, which demonstrates the well-generalized of our method. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2401.12999 |
| رقم الأكسشن: | edsarx.2401.12999 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |