Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine.

By Ajay N. Jain. 2003

1,258 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Organic Chemistry, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Molecular Docking and Structure-Based Drug Design Strategies (2015, methodology): “Surflex and FlexX are broadly used molecular docking programs using empirical scoring functions [31,39].”
Computational drug discovery (2012, methodology): “A validation test docking known inhibitors into the binding pockets of thymidine kinase (TK) and HIV-1 reverse RT indicated that GAsDock is more accurate than other docking programs, such as GOLD [33] , FlexX [33] , DOCK [33] , Surflex [30] , and Glide [29] .”
Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review (2012, neutral): “Empirical LUDI (62), X-Score (63), PLP (64), ChemScore (65), FlexX/F-Score (12), GlideScore (13), Surflex (15), QXP/Flo+ (66)”
Challenges and advances in computational docking: 2009 in review (2011, background): “outperform other ones are Glide (23–26) and Surflex (37,38).”
Computational Methods in Drug Discovery (2014, methodology): “Empirical functions have been used in several commercially available docking suits like LUDI (Bohm, 1992), FLEXX (Rarey et al., 1996), and SURFLEX (Jain, 2003).”
Protein–ligand docking: Current status and future challenges (2006, background): “There have been many studies comparing such programs in terms of the accuracy in reproducing the X-ray pose of selected ligands, the capacity to predict binding free energies from the best-scored pose, and the ability to discriminate known binders from randomly chosen molecules in virtual screening ”
Comparative evaluation of eight docking tools for docking and virtual screening accuracy (2004, background): “SURFLEX robustness and reliability, notably for docking flexible ligands in large binding sites, may be explained by an efficient and original surfacebased similarity match between ligand atoms and receptor hotspots.(20,44) The binding pocket is first characterized by a Fig.”