Abstract
High-throughput screening (HTS) campaigns are routinely performed in pharmaceutical companies to explore activity profiles of chemical libraries for the identification of promising candidates for further investigation. With the aim of improving hit rates in these campaigns, data-driven approaches have been used to design relevant compound screening collections, enable effective hit triage and perform activity modeling for compound prioritization. Remarkable progress has been made in the activity modeling area since the recent introduction of large-scale bioactivity-based compound similarity metrics. This is evidenced by increased hit rates in iterative screening strategies and novel insights into compound mode of action obtained through activity modeling. Here, we provide an overview of the developments in data-driven approaches, elaborate on novel activity modeling techniques and screening paradigms explored and outline their significance in HTS.
Highlights
Knowledge from the areas of pharmacology and medicinal chemistry is combined to design potentially active compounds for testing [1,2,3]
We summarize the recent developments in data-driven applications to improve effectiveness in High-throughput screening (HTS) and discuss the strengths and limitations of these methods
HTS has greatly gained momentum over the past decades, much profit can be realized by using intelligent measures to improve efficiency at the library design, hit triage and activity modeling stages
Summary
Knowledge from the areas of pharmacology and medicinal chemistry is combined to design potentially active compounds for testing [1,2,3]. The DFT density spectrum is compared with the null density spectrum using the Kolmogorov–Smirnov test to determine the existence of systematic errors Together, all these methods can be used to measure the error in the hit distribution surface, to measure errors for samples with different sizes and to analyze signal frequency. While ideally this should take place at the library design stage, analysis of historical HTS data requires that this filtering be applied at the triage stage as well, as often historical assays contain undesirable compounds because of improper filtering at the time of design This is followed by the selection of diverse sets of actives for follow-up testing based on potency and scaffold structure–activity relationships (SAR) [8, 69, 70]. Rescuing false negatives is important; a number of data mining approaches have been explored to this end
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