Towards unified compound screening strategies: A critical evaluation of error sources in experimental and virtual high-throughput screening

This contribution focuses on an assessment of errors in experimental and virtual screening. Sources of errors in high-throughput screening can be classified as logistic, measurement-related, or strategic. Biological assays formatted for high throughput are generally susceptible to small but systematic errors arising from a variety of sources, and the correction of such errors often requires the application of advanced data analysis methods. For virtual screening, chemical space design and molecular similarity analysis play crucial roles and similarity-based methods also have principal limitations, as discussed herein. In addition, the relative performance of computational screening methods, regardless of their specific features, generally displays strong compound class dependence. However, given their opportunities and limitations, experimental and computational screening can be carried out in a highly complementary manner and integrated screening strategies are thought to have significant potential in pharmaceutical research.