Molecular docking using shape descriptors

By Brian K. Shoichet, Irwin D. Kuntz, D. Bodian. 1992

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Citations in the corpus

Economic-based Distributed Resource Management and Scheduling for Grid Computing (2002, methodology): “The program dock matches spheres (generated by sphgen) with ligand atoms and uses scoring grids (from grid) to evaluate ligand orientations [11].”
Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy (2009, methodology): “The ﬂexible anchor-and-grow algorithm 38,39 available in DOCK 6.1 was used in this study.”
Principles of docking: An overview of search algorithms and a guide to scoring functions (2002, background): “136 Combining all patches of the surface of one protein molecule with all patches of a second molecule takes on the order of 10 7 trials.”
Insights into Protein–Ligand Interactions: Mechanisms, Models, and Methods (2016, methodology): “In these algorithms, possible ligand conformations may be generated on-the-fly in the binding cavity of the protein [166] (e.g., DOCK [156] uses of this approach), or using the fragmentation-based method in which the multiple rigid fragments located in the binding cavity are rotated and linked [167]”
Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014, background): “…two serious problems: (1) we need to know the three-dimensional structure of a target to compute the binding of each drug candidate to the target [16–20], but the three-dimensional structures of many targets, especially GPCRs, are still unavailable [21, 22]; and (2) simulation is time-consuming,…”
Binding Affinity via Docking: Fact and Fiction (2018, methodology): “Indeed, many of the scoring functions use this approach to model dispersion [5,24,29], although GOLD uses softer 8–4 potential while Dock and Glide prefer 12–6 potential [1,3–5,8–10].”
Geometric Range Searching and Its Relatives (2007, background): “This is one of the most widely studied problems in computational geometry because it arises in so many di erent areas, including pattern recognition [98, 107], data compression [25, 243], information retrieval [124, 249], CAD [213], molecular biology [261], image analysis [186, 188], data mining [12”
Accelerating Drugs Discovery with Deep Reinforcement Learning: An Early Approach (2018, background): “Docking is very intensive computationally speaking since it is considered a NP-complete problem [46].”
Flexible ligand docking using conformational ensembles (1998, methodology): “A steric map was calculated with DISTMAP (Shoichet et al., 1992) using limits of 2.”