Journal Article10.1021/acs.jctc.3c00943
Encoding Molecular Docking for Quantum Computers.
Jinyin Zha,Jiaqi Su,Tiange Li,Chongyu Cao,Yin Ma,Hai Wei,Zhiguo Huang,Ling Qian,Kai Wen,Jian Zhang +9 more
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TL;DR: Grid Point Matching and Feature Atom Matching are introduced to accelerate pose sampling in molecular docking by encoding the problem into quadratic unconstrained binary optimization models so that it could be solved by quantum computers like the coherent Ising machine (CIM).
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Abstract: Molecular docking is important in drug discovery but is burdensome for classical computers. Here, we introduce Grid Point Matching (GPM) and Feature Atom Matching (FAM) to accelerate pose sampling in molecular docking by encoding the problem into quadratic unconstrained binary optimization (QUBO) models so that it could be solved by quantum computers like the coherent Ising machine (CIM). As a result, GPM shows a sampling power close to that of Glide SP, a method performing an extensive search. Moreover, it is estimated to be 1000 times faster on the CIM than on classical computers. Our methods could boost virtual drug screening of small molecules and peptides in future.
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- 19 Apr 2024
TL;DR: Molecular docking via weighted subgraph isomorphism on quantum annealers is a novel approach for drug discovery using quantum annealing techniques.
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References
Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.
Richard A. Friesner,Jay L. Banks,Robert B. Murphy,Thomas A. Halgren,Jasna Klicic,Daniel T. Mainz,Matthew P. Repasky,Eric H. Knoll,Mee Shelley,Jason K. Perry,David E. Shaw,Perry Francis,Peter S Shenkin +12 more
TL;DR: Glide approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand to find the best docked pose using a model energy function that combines empirical and force-field-based terms.
Development and validation of a genetic algorithm for flexible docking.
TL;DR: GOLD (Genetic Optimisation for Ligand Docking) is an automated ligand docking program that uses a genetic algorithm to explore the full range of ligand conformational flexibility with partial flexibility of the protein, and satisfies the fundamental requirement that the ligand must displace loosely bound water on binding.
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Computational protein–ligand docking and virtual drug screening with the AutoDock suite
TL;DR: This protocol covers the docking and virtual screening methods provided by the AutoDock suite of programs, including a basic docking of a drug molecule with an anticancer target, a virtual screen of this target with a small ligand library, docking with selective receptor flexibility, active site prediction and docking with explicit hydration.
ProDy: protein dynamics inferred from theory and experiments.
TL;DR: A Python package, ProDy, for structure-based analysis of protein dynamics allows for quantitative characterization of structural variations in heterogeneous datasets of structures experimentally resolved for a given biomolecular system, and for comparison of these variations with the theoretically predicted equilibrium dynamics.
Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power.
TL;DR: Overall, the ligand binding poses could be identified in most cases by the evaluated docking programs but the ranks of the binding affinities for the entire dataset could not be well predicted by most docking programs.
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