Structure-based maximal affinity model predicts small-molecule druggability
By Alan C. Cheng, Ryan G. Coleman, Kathleen T Smyth, Qing Cao, P. Soulard, Daniel R. Caffrey, Anna C. Salzberg, Enoch S. Huang. Nature Biotechnology, 2007
643 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Molecular Biology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/e4fb30468f206164ece2cc6f8b7fccd89b81bc5e
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In Computational Theory and Mathematics
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Citations in the corpus
- Selectivity by Small-Molecule Inhibitors of Protein Interactions Can Be Driven by Protein Surface Fluctuations (2015, positive): “As pointed out by Cheng and colleagues (68), pocket formation is necessary but may not be sufficient for a protein surface to be druggable: the curvature and hydrophobicity of the pocket are also important.”
- The influence of drug-like concepts on decision-making in medicinal chemistry (2007, background): “Factors driving the increases in physical properties in the most recently discovered oral drugs include a greater number of apparently less druggable new target”
- Collaborative matrix factorization with multiple similarities for predicting drug-target interactions (2013, background): “Docking simulation is biologically well-accepted but has two serious problems: 1) simulation always needs three-dimensional (3D) structures of targets which are often unavailable [4] and 2) simulation is heavily time consuming.”
- Reaching for high-hanging fruit in drug discovery at protein–protein interfaces (2007, result): “compared with those involved in protein–small-molecule interactions (∼300–1,000 A 2”
- Supervised prediction of drug–target interactions using bipartite local models (2009, methodology): “A powerful method is docking simulation (Cheng et al., 2007; Rarey et al., 1996), but this requires 3D structure information for target proteins.”
- Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014, background): “…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, in…”
- A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information (2017, methodology): “Traditional computational methods mainly depend on two strategies, including the molecular docking-based approaches [11, 17, 42] and the ligand-based approaches [29, 30].”
- Fpocket: An open source platform for ligand pocket detection (2009, neutral): “[39].”
- Prediction of drug–target interaction networks from the integration of chemical and genomic spaces (2008, background): “…new biological findings and also the problem of redundancy Recently, a classification of target proteins based on the structure their ligands (Keiser et al. , 2007) and in related work an of the drug–target network revealed characteristic features taken into consideration in the network analysis.”