Quantum-Inspired Machine Learning for Molecular Docking

• URL: http://arxiv.org/abs/2401.12999v2
• Date: Thu, 22 Feb 2024 02:56:06 GMT
• Title: Quantum-Inspired Machine Learning for Molecular Docking
• Authors: Runqiu Shu, Bowen Liu, Zhaoping Xiong, Xiaopeng Cui, Yunting Li, Wei Cui, Man-Hong Yung and Nan Qiao
• Abstract summary: Molecular docking is an important tool for structure-based drug design, accelerating the efficiency of drug development. Traditional docking by searching for possible binding sites and conformations is computationally complex and results poorly under blind docking. We introduce quantum-inspired algorithms combining quantum properties and spatial optimization problems. Our method outperforms traditional docking algorithms and deep learning-based algorithms over 10%.
• Score: 9.16729372551085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Quantum molecular docking with quantum-inspired algorithm [4.959284967789063]
  We propose a novel quantum molecular docking (QMD) approach based on QA-inspired algorithm. We construct two binary encoding methods to efficiently discretize the degrees of freedom with exponentially reduced number of bits. We show that QMD has shown advantages over the search-based Auto Vina and the deep-learning DIFFDOCK in both re-docking and self-docking scenarios.
  arXiv  Detail & Related papers  (2024-04-12T06:24:45Z)
• Multi-scale Iterative Refinement towards Robust and Versatile Molecular Docking [17.28573902701018]
  Molecular docking is a key computational tool utilized to predict the binding conformations of small molecules to protein targets. We introduce DeltaDock, a robust and versatile framework designed for efficient molecular docking.
  arXiv  Detail & Related papers  (2023-11-30T14:09:20Z)
• Molecular docking via quantum approximate optimization algorithm [1.2406403248205282]
  Molecular docking plays a pivotal role in drug discovery and precision medicine, enabling us to understand protein functions and advance novel therapeutics. Here, we introduce a potential alternative solution to this problem, the digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA), which utilizes counterdiabatic driving and QAOA on a quantum computer. Our findings underscore quantum computing's potential in drug discovery and offer valuable insights for optimizing protein-ligand docking processes.
  arXiv  Detail & Related papers  (2023-08-08T07:25:34Z)
• Optimization of a Hydrodynamic Computational Reservoir through Evolution [58.720142291102135]
  We interface with a model of a hydrodynamic system, under development by a startup, as a computational reservoir. We optimized the readout times and how inputs are mapped to the wave amplitude or frequency using an evolutionary search algorithm. Applying evolutionary methods to this reservoir system substantially improved separability on an XNOR task, in comparison to implementations with hand-selected parameters.
  arXiv  Detail & Related papers  (2023-04-20T19:15:02Z)
• D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory [79.50644650795012]
  We propose a deep learning approach to solve Kohn-Sham Density Functional Theory (KS-DFT) We prove that such an approach has the same expressivity as the SCF method, yet reduces the computational complexity. In addition, we show that our approach enables us to explore more complex neural-based wave functions.
  arXiv  Detail & Related papers  (2023-03-01T10:38:10Z)
• Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks [0.9785311158871759]
  We introduce Deep Surrogate Docking (DSD), a framework that applies deep learning-based surrogate modeling to accelerate the docking process substantially. We show that the DSD workflow combined with the FiLMv2 architecture provides a 9.496x speedup in molecule screening with a 3% recall error rate.
  arXiv  Detail & Related papers  (2022-11-04T19:36:02Z)
• DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking [28.225704750892795]
  Predicting the binding structure of a small molecule ligand to a protein is critical to drug design. Recent deep learning methods that treat docking as a regression problem have decreased runtime compared to traditional search-based methods. We frame molecular docking as a generative modeling problem and develop DiffDock, a diffusion generative model over the non-Euclidean manifold of ligand poses.
  arXiv  Detail & Related papers  (2022-10-04T17:38:14Z)
• A single $T$-gate makes distribution learning hard [56.045224655472865]
  This work provides an extensive characterization of the learnability of the output distributions of local quantum circuits. We show that for a wide variety of the most practically relevant learning algorithms -- including hybrid-quantum classical algorithms -- even the generative modelling problem associated with depth $d=omega(log(n))$ Clifford circuits is hard.
  arXiv  Detail & Related papers  (2022-07-07T08:04:15Z)
• Improving RNA Secondary Structure Design using Deep Reinforcement Learning [69.63971634605797]
  We propose a new benchmark of applying reinforcement learning to RNA sequence design, in which the objective function is defined to be the free energy in the sequence's secondary structure. We show results of the ablation analysis that we do for these algorithms, as well as graphs indicating the algorithm's performance across batches.
  arXiv  Detail & Related papers  (2021-11-05T02:54:06Z)
• Guiding Deep Molecular Optimization with Genetic Exploration [79.50698140997726]
  We propose genetic expert-guided learning (GEGL), a framework for training a deep neural network (DNN) to generate highly-rewarding molecules. Extensive experiments show that GEGL significantly improves over state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-04T05:01:26Z)
• APQ: Joint Search for Network Architecture, Pruning and Quantization Policy [49.3037538647714]
  We present APQ for efficient deep learning inference on resource-constrained hardware. Unlike previous methods that separately search the neural architecture, pruning policy, and quantization policy, we optimize them in a joint manner. With the same accuracy, APQ reduces the latency/energy by 2x/1.3x over MobileNetV2+HAQ.
  arXiv  Detail & Related papers  (2020-06-15T16:09:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.