Benchmarking the mechanisms of frequent hitters: limitation of PAINS alerts.

In 2010, the pan-assay interference compounds (PAINS) rule was proposed to identify false-positive compounds, especially frequent hitters (FHs), in biological screening campaigns, and has rapidly become an essential component in drug design. However, the specific mechanisms remain unknown, and the result validation and follow-up processing schemes are still unclear. In this review, a large benchmark collection of >600,000 compounds sourced from databases and the literature, including six common false-positive mechanisms, was used to evaluate the detection ability of PAINS. In addition, 400 million purchasable molecules from the ZINC database were also applied to PAINS screening. The results indicate that the PAINS rule is not suitable for the screening of all types of false-positive results and needs more improvement.

Application of Negative Design To Design a More Desirable Virtual Screening Library.

Negative design is a group of virtual screening methods that aims at weeding out compounds with undesired properties during the early stages of drug development. These methods are mainly designed to predict three important types of pharmacological properties: drug-likeness, frequent hitters, and toxicity. In order to achieve high screening efficiency, most negative design methods are physicochemical property-based and/or substructure-based rules or filters. Such methods have advantages of simplicity and good interpretability, but they also suffer from some defects such as inflexibility, discontinuity, and hard decision-making. In this review, the advances in negative design for the evaluations of drug-likeness, frequent hitters, and toxicity are outlined. In addition, the related Web servers and software packages developed recently for negative design are summarized. Finally, future research directions in this field are discussed.

Frequent hitters: nuisance artifacts in high-throughput screening.

One of the major challenges in early drug discovery is the recognition of frequent hitters (FHs), that is, compounds that nonspecifically bind to a range of macromolecular targets or false positives caused by various types of assay interferences. In this review, we survey the mechanisms underlying different types of FHs, including aggregators, spectroscopic interference compounds (i.e., luciferase inhibitors and fluorescent compounds), chemical reactive compounds, and promiscuous compounds. We also review commonly used experimental detection techniques and computational prediction models for FH identification. In addition, the rational applications of these computational filters are discussed. It is believed that, with the rational use of FH filters, the efficiency of drug discovery will be significantly improved.