DSDP: A Blind Docking Strategy Accelerated by GPUs

• URL: http://arxiv.org/abs/2303.09916v1
• Date: Thu, 16 Mar 2023 07:00:21 GMT
• Title: DSDP: A Blind Docking Strategy Accelerated by GPUs
• Authors: YuPeng Huang, Hong Zhang, Siyuan Jiang, Dajiong Yue, Xiaohan Lin, Jun Zhang, Yi Qin Gao
• Abstract summary: We take the advantage of both traditional and machine-learning based methods, and present a method Deep Site and Docking Pose (DSDP) to improve the performance of blind docking. DSDP reaches a 2 top-1 success rate (RMSD 2 AA) on an unbiased and challenging test dataset with 1.2 s wall-clock computational time per system. Its performances on DUD-E dataset and the time-split PDBBind dataset used in EquiBind, TankBind, and DiffDock are also effective.
• Score: 6.221048348194304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Virtual screening, including molecular docking, plays an essential role in drug discovery. Many traditional and machine-learning based methods are available to fulfil the docking task. The traditional docking methods are normally extensively time-consuming, and their performance in blind docking remains to be improved. Although the runtime of docking based on machine learning is significantly decreased, their accuracy is still limited. In this study, we take the advantage of both traditional and machine-learning based methods, and present a method Deep Site and Docking Pose (DSDP) to improve the performance of blind docking. For the traditional blind docking, the entire protein is covered by a cube, and the initial positions of ligands are randomly generated in the cube. In contract, DSDP can predict the binding site of proteins and provide an accurate searching space and initial positions for the further conformational sampling. The docking task of DSDP makes use of the score function and a similar but modified searching strategy of AutoDock Vina, accelerated by implementation in GPUs. We systematically compare its performance with the state-of-the-art methods, including Autodock Vina, GNINA, QuickVina, SMINA, and DiffDock. DSDP reaches a 29.8% top-1 success rate (RMSD < 2 {\AA}) on an unbiased and challenging test dataset with 1.2 s wall-clock computational time per system. Its performances on DUD-E dataset and the time-split PDBBind dataset used in EquiBind, TankBind, and DiffDock are also effective, presenting a 57.2% and 41.8% top-1 success rate with 0.8 s and 1.0 s per system, respectively.

Related papers

• DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking [15.205550571902366]
  Molecular docking is crucial in structure-based drug design for understanding protein-ligand interactions. Recent advancements in docking methods have demonstrated significant efficiency and accuracy advantages over traditional sampling methods. We propose a novel two-stage docking framework, DeltaDock, consisting of pocket prediction and site-specific docking.
  arXiv  Detail & Related papers  (2024-10-15T03:09:06Z)
• Quantum-Inspired Machine Learning for Molecular Docking [9.16729372551085]
  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%.
  arXiv  Detail & Related papers  (2024-01-22T09:16:41Z)
• Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning [50.809769498312434]
  We propose a novel dataset pruning method termed as Temporal Dual-Depth Scoring (TDDS) Our method achieves 54.51% accuracy with only 10% training data, surpassing random selection by 7.83% and other comparison methods by at least 12.69%.
  arXiv  Detail & Related papers  (2023-11-22T03:45:30Z)
• DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models [47.73386438748902]
  DiffDock-PP is a diffusion generative model that learns to translate and rotate unbound protein structures into their bound conformations. We achieve state-of-the-art performance on DIPS with a median C-RMSD of 4.85, outperforming all considered baselines.
  arXiv  Detail & Related papers  (2023-04-08T02:10:44Z)
• TempNet: Temporal Attention Towards the Detection of Animal Behaviour in Videos [63.85815474157357]
  We propose an efficient computer vision- and deep learning-based method for the detection of biological behaviours in videos. TempNet uses an encoder bridge and residual blocks to maintain model performance with a two-staged, spatial, then temporal, encoder. We demonstrate its application to the detection of sablefish (Anoplopoma fimbria) startle events.
  arXiv  Detail & Related papers  (2022-11-17T23:55:12Z)
• 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)
• ETAD: A Unified Framework for Efficient Temporal Action Detection [70.21104995731085]
  Untrimmed video understanding such as temporal action detection (TAD) often suffers from the pain of huge demand for computing resources. We build a unified framework for efficient end-to-end temporal action detection (ETAD) ETAD achieves state-of-the-art performance on both THUMOS-14 and ActivityNet-1.3.
  arXiv  Detail & Related papers  (2022-05-14T21:16:21Z)
• EAutoDet: Efficient Architecture Search for Object Detection [110.99532343155073]
  EAutoDet framework can discover practical backbone and FPN architectures for object detection in 1.4 GPU-days. We propose a kernel reusing technique by sharing the weights of candidate operations on one edge and consolidating them into one convolution. In particular, the discovered architectures surpass state-of-the-art object detection NAS methods and achieve 40.1 mAP with 120 FPS and 49.2 mAP with 41.3 FPS on COCO test-dev set.
  arXiv  Detail & Related papers  (2022-03-21T05:56:12Z)
• ParaVS: A Simple, Fast, Efficient and Flexible Graph Neural Network Framework for Structure-Based Virtual Screening [2.5137859989323537]
  We introduce a docking-based SBVS method and a deep learning non-docking-based method that is able to avoid the computational cost of the docking process. The inference speed of ParaVS-ND is about 3.6e5 molecule / core-hour, while a conventional docking-based method is around 20, which is about 16000 times faster.
  arXiv  Detail & Related papers  (2021-02-08T08:24:05Z)
• Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data [93.76907759950608]
  We propose a doubly kernel learning algorithm for vertically partitioned data. We show that FDSKL is significantly faster than state-of-the-art federated learning methods when dealing with kernels.
  arXiv  Detail & Related papers  (2020-08-14T05:46:56Z)

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.