How To Optimize Shape-Based Virtual Screening: Choosing the Right Query and Including Chemical Information

Abstract

Shape-based molecular similarity approaches have been established as important and popular virtual screening techniques. Recent applications have shown successful screening campaigns using different parameters and query selection. It is common sense that pure volume overlap scoring (or "shape-based screening") under-represents chemical or pharmacophoric information of a molecule. Using the "Directory of Useful Decoys" (DUD) as a benchmark set, we systematically evaluate how (i) the choice of query conformations, (ii) the selection of the active compound to be used as a query structure, and (iii) the inclusion of chemical information (i.e., the pharmacophoric properties of the query molecule) affect screening performance. Varying these parameters bears remarkable potential for improvements and delivers the best screening performance reported using these tools so far. From these insights, guidelines on how to reach optimum performance during virtual screening are developed.

Comprehensive Comparison of Ligand-Based Virtual Screening Tools Against the DUD Data set Reveals Limitations of Current 3D Methods

“…It has been reported previously that VS results depend strongly on the target family, 12 the query structure and conformation, 23 and also on the nature of the ligand and decoy sets. 22 Although analogue bias can influence the apparent utility of 2D fingerprint-based methods, we believe this is less of a concern for 3D shape-matching approaches which have to deal with the additional problems of selecting the best conformation to use as the query and finding the best 3D superposition between the query and each of the database compounds.…”

In Silico‐Guided Strategies for the Discovery and Rationalization of Bioactive Natural Products

“…However, electron densities and B-factors in the structure file deposited by the crystallographer help to interpret the information. In addition, there are many software programs and online tools, which help to better understand X-ray structures (Davis, Teague, and Kleywegt, 2003;Kirchmair et al, 2009).…”

Prospective virtual screening with Ultrafast Shape Recognition: the identification of novel inhibitors of arylamine N -acetyltransferases

“…Unfortunately, as discussed by Shoichet (2004), docking has the drawback that accurate binding affinity calculation for many thousands of diverse molecules remains beyond our reach to date and hence it is only feasible to use less accurate binding energy estimation in large-scale virtual screening, which harms the effectiveness of these techniques. 1 In practice, it has been shown (Hawkins et al 2007;Kirchmair et al 2009) that shape similarity performs at least as well as a range of docking methods. Moreover, a previous prospective virtual screening study concluded (Rush et al 2005) that an accurate description of shape alone is unexpectedly powerful.…”