ArticleJournal of computer-aided molecular design2019
D3R Grand Challenge 4: prospective pose prediction of BACE1 ligands with AutoDock-GPU.
Article in Journal of computer-aided molecular design, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 37 citations in OpenAlex.
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- The role of artificial intelligence in drug screening, drug design, and clinical trials.Frontiers in pharmacology · 2024Review
- Reactive Docking: A Computational Method for High-Throughput Virtual Screenings of Reactive Species.Journal of chemical information and modeling · 2023Article
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- iORandLigandDB: A Website for Three-Dimensional Structure Prediction of Insect Odorant Receptors and Docking with Odorants.Insects · 2023Article
- Accelerating AutoDock Vina with GPUs.Molecules (Basel, Switzerland) · 2022Article
- Benchmarking the Performance of Irregular Computations in AutoDock-GPU Molecular Docking.Parallel computing · 2022Article
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- AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings.Journal of chemical information and modeling · 2021Article
- Accelerating AutoDock4 with GPUs and Gradient-Based Local Search.Journal of chemical theory and computation · 2021Article
- The AutoDock suite at 30.Protein science : a publication of the Protein Society · 2021Article
- Supercomputing Pipelines Search for Therapeutics Against COVID-19.Computing in science & engineering · 2021Article
- ComparisonAvicenna journal of phytomedicineArticle
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 3 countries.
Funding
Abstract
In this paper we describe our approaches to predict the binding mode of twenty BACE1 ligands as part of Grand Challenge 4 (GC4), organized by the Drug Design Data Resource. Calculations for all submissions (except for one, which used AutoDock4.2) were performed using AutoDock-GPU, the new GPU-accelerated version of AutoDock4 implemented in OpenCL, which features a gradient-based local search. The pose prediction challenge was organized in two stages. In Stage 1a, the protein conformations associated with each of the ligands were undisclosed, so we docked each ligand to a set of eleven receptor conformations, chosen to maximize the diversity of binding pocket topography. Protein conformations were made available in Stage 1b, making it a re-docking task. For all calculations, macrocyclic conformations were sampled on the fly during docking, taking the target structure into account. To leverage information from existing structures containing BACE1 bound to ligands available in the PDB, we tested biased docking and pose filter protocols to facilitate poses resembling those experimentally determined. Both pose filters and biased docking resulted in more accurate docked poses, enabling us to predict for both Stages 1a and 1b ligand poses within 2 Å RMSD from the crystallographic pose. Nevertheless, many of the ligands could be correctly docked without using existing structural information, demonstrating the usefulness of physics-based scoring functions, such as the one used in AutoDock4, for structure based drug design.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.