Vina-GPU 2.0: Further Accelerating AutoDock Vina and Its Derivatives with Graphics Processing Units

Ding, Ji; Shidi, Tang; Mei, Zheming; Wang, Lingyue; Huang, Qinqin; Hu, Haifeng; Ling, Ming; Wu, Jiansheng

doi:10.1021/acs.jcim.2c01504

Cited by 44 publications

(28 citation statements)

References 27 publications

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“…For stage 1, we first generate 36 pocket conformations using the empty pocket packing model given the pocket backbone conformation, then calculate the RMSD matrix and cluster them into 6 groups using the K-Medoids algorithm, and then use the medoids of each cluster as the representative conformation, thus simulating the conformation selection theory. After obtaining 6 pocket conformations similar to the “free” state, we dock one ligand conformation for each of them using Vina -GPU [67] (for work efficiency, the docking process uniformly uses GPU-accelerated Vina ). For stage 2, we use the ligand induced pocket packing model with the 6 ligand conformations obtained in the stage 1 as the initial condition.…”

Section: Methodsmentioning

confidence: 99%

PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking

Zhang,

Jiang,

Cao

et al. 2024

Preprint

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Structure-based drug design (SBDD) relies on accurate knowledge of protein structure and ligand-binding conformations. However, most of the static conformations obtained by advanced methods such as structural biology and de novo protein folding algorithms often don't meet the needs for drug design. We introduce PackDock, a flexible docking method that combines "conformation selection" and "induced fit" mechanisms in a two-stage docking pipeline. The core module of this method is PackPocket, which uses a diffusion model to explore the side-chain conformation space in ligand binding pockets, both with or without a ligand. We evaluate our method using several tests that reflect real-world application scenarios. (1) Side-chain packing and Re-docking experiments validate the ability of PackDock to predict accurate side-chain conformations and ligand conformations. (2) Cross-docking experiments with apo and non-homologous ligand-induced holo structures align with real docking scenarios, demonstrating PackDock's practical value. (3) Docking experiments with hypothetical models show that PackPocket can potentially conduct SBDD starting from protein sequence information only. Additionally, we found that PackDock can identify key amino acid conformation changes, which may provide insights for lead compound optimization. We demonstrate PackDock can accurately predict the complex conformations in various application scenarios, by combining the conformation selection theory and the induced fit theory, and by using the ability of PackPocket to accurately predict the side chain conformations in the pocket region. We believe this method can improve the usability of existing structures, providing a new perspective for the SBDD community.

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Section: Methodsmentioning

confidence: 99%

PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking

Zhang,

Jiang,

Cao

et al. 2024

Preprint

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“…The computational time is also crucial to deciding which docking program to choose for virtual screening purposes. Recently, Autodock Vina has been modified to fit the GPU architecture, which significantly accelerates the computations and adds more advantages in comparison to other molecular docking software [48].…”

Section: Introductionmentioning

confidence: 99%

Keras/TensorFlow in Drug Design for Immunity Disorders

Dragan,

Joshi,

Atzei

et al. 2023

Preprint

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Homeostasis of the host immune system is regulated by white blood cells with a variety of cell surface receptors for cytokines. Chemotactic cytokines (chemokines) activate their receptors to evoke the chemotaxis of immune cells in homeostatic migrations or inflammatory conditions towards inflamed tissue or pathogens. Dysregulation of the immune system leading to disorders such as allergies, autoimmune diseases, or cancer requires efficient, fast-acting drugs to minimize the long-term effects of chronic inflammation. Here, we performed structure-based virtual screening (SBVS) assisted by the Keras/TensorFlow neural network (NN) to find novel com-pound scaffolds acting on three chemokine receptors: CCR2, CCR3 and one CXC receptor CXCR3. Keras/TensorFlow NN was used here not as a typically used binary classifier, but as an efficient multi-class classifier that can discard not only inactive compounds but also low or medium-activity compounds. Several compounds proposed by SBVS and NN were tested in 100 ns all-atom molecular dynamics simulations to confirm their binding affinity. To improve the basic binding affinity of the compounds, new chemical modifications were proposed. The modified compounds were compared with known antagonists of these three chemokine receptors. Known CXCR3 were among the top predicted compounds and thus benefits of using Keras/TensorFlow in drug discovery have been shown in addition to structure-based approaches. Furthermore, we showed that Keras/TensorFlow NN can accurately predict the receptor subtype selectivity of compounds, for which SBVS often fails. We cross-tested chemokine receptor datasets retrieved from ChEMBL and curated datasets for cannabinoid receptors available at: http://db-gpcr-chem.uw.edu.pl. The NN model trained on the cannabinoid receptor datasets re-trieved from ChEMBL was the most accurate in the receptor subtype selectivity prediction. Among NN models trained on the chemokine receptor datasets, the CXCR3 model showed the highest accuracy in differentiating the receptor subtype for a given compound dataset.

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“…In addition, molecular docking tools provide an efficient and cost-effective means for the initial stages of drug design, allowing the identification of potential compounds and the evaluation of their binding affinities [2], [3], [4]. Among the available tools, the AutoDock Vina suite and its derivatives are widely regarded as the preferred choice for molecular docking, owing to their remarkable speed and accuracy [5].…”

Section: Introductionmentioning

confidence: 99%

“…Previous investigations have revealed that early iterations of AutoDock Vina and its derivatives are incapable of meeting the speed requirements imposed by contemporary drug discovery practices, necessitating the development of accelerated versions [5], [6]. Graphics Processing Units (GPUs) have emerged as an ideal solution for common users due to their accessibility, cost-effectiveness, and ease of implementation.…”

Section: Introductionmentioning

confidence: 99%

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Vina-GPU 2.1: towards further optimizing docking speed and precision of AutoDock Vina and its derivatives

Tang,

Ding,

Zhu

et al. 2023

Preprint

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AutoDock Vina and its derivatives have established themselves as a prevailing pipeline for virtual screening in contemporary drug discovery. Our Vina-GPU method leverages the parallel computing power of GPUs to accelerate AutoDock Vina, and Vina-GPU 2.0 further enhances the speed of AutoDock Vina and its derivatives. Given the prevalence of large virtual screens in modern drug discovery, the improvement of speed and accuracy in virtual screening has become a longstanding challenge. In this study, we propose Vina-GPU 2.1, aimed at enhancing the docking speed and precision of AutoDock Vina and its derivatives through the integration of novel algorithms to facil-itate improved docking and virtual screening outcomes. Building upon the foundations laid by Vina-GPU 2.0, we introduce a novel algorithm, namely Reduced Iteration and Low Complexity BFGS (RILC-BFGS), designed to expedite the most time-consuming operation. Additionally, we implement grid cache optimization to further enhance the docking speed. Furthermore, we employ optimal strategies to individually optimize the structures of ligands, receptors, and binding pockets, thereby enhancing the docking precision. To assess the performance of Vina-GPU 2.1, we conduct extensive virtual screening experiments on three prominent targets, utilizing two fundamental compound libraries and seven docking tools. Our results demonstrate that Vina-GPU 2.1 achieves an average 4.97-fold acceleration in docking speed and an average 342% improvement in EF1% compared to Vina-GPU 2.0. The source code and tools for Vina-GPU 2.1 are freely available athttps://github.com/DeltaGroupNJUPT/Vina-GPU-2.1, accompanied by comprehensive instructions and illustrative examples.

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Vina-GPU 2.0: Further Accelerating AutoDock Vina and Its Derivatives with Graphics Processing Units

Cited by 44 publications

References 27 publications

PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking

PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking

Keras/TensorFlow in Drug Design for Immunity Disorders

Vina-GPU 2.1: towards further optimizing docking speed and precision of AutoDock Vina and its derivatives

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