Hit Identification and Optimization in Virtual Screening: Practical Recommendations Based Upon a Critical Literature Analysis
By Tian Zhu, Shuyi Cao, P. Su, Raman R. Patel, D. Shah, Heta B Chokshi, R. Szukala, Michael E. Johnson, K. Hevener. 2013
273 citations recorded by the source corpus.
A critical analysis of virtual screening results published between 2007 and 2011 was performed. The activity of reported hit compounds from over 400 studies was compared to their hit identification criteria. Hit rates and ligand efficiencies were calculated to assist in these analyses and the results were compared with factors such as the size of the virtual library and the number of compounds tested. A series of promiscuity, drug-like, and ADMET filters were applied to the reported hits to assess the quality of compounds reported and a careful analysis of a subset of the studies which presented hit optimization was performed. This data allowed us to make several practical recommendations with respect to selection of compounds for experimental testing, defining hit identification criteria, and general virtual screening hit criteria to allow for realistic hit optimization. A key recommendation is the use of size-targeted ligand efficiency values as hit identification criteria.
Subfields: Computational Theory and Mathematics, Organic Chemistry, Pharmacology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Tomohide Masuda
- David Ryan Koes
- Mohammad Mahdi Jaghoori
- Sílvia Delgado Olabarriaga
- Chanjin Jung
- Boris Bleijlevens
- Jingyu Lee
- Bilal Shaker
In Organic Chemistry
In Pharmacology
Citations in the corpus
- Ligand biological activity predicted by cleaning positive and negative chemical correlations (2019, background): “14% (40); thus, our model performance is around almost 40-fold better than the background hit rate.”
- In silico methods and tools for drug discovery (2021, background): “Generally, the hit rate of experimental HTS ranges between 0.01% and 0.14% [84].”
- 1001 Ways to run AutoDock Vina for virtual screening (2016, background): “Virtual screening (VS) is nowadays a standard step before wet-lab experiments in drug discovery [1, 2].”
- Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models (2020, background): “Despite its successful applications [2, 3], virtual screening has inherent limitations.”