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Compounds interfering with high-throughput screening (HTS) assay technologies (also known as “badly behaving compounds”, “bad actors”, “nuisance compounds” or “PAINS”) pose a major challenge to early-stage drug discovery. Many of these problematic compounds are “frequent hitters”, and we have recently published a set of machine learning models (“Hit Dexter 2.0”) for flagging such compounds. Here we present a new generation of machine learning models which are derived from a large, manually curated and annotated data set. For the first time, these models cover, in addition to target-based assays, also cell-based assays. Our experiments show that cell-based assays behave indeed differently from target-based assays, with respect to hit rates and frequent hitters, and that dedicated models are required to produce meaningful predictions. In addition to these extensions and refinements, we explored a variety of additional setups for modeling, including the combination of four machine learning classifiers (i.e. k-nearest neighbors (KNN), extra trees, random forest and multilayer perceptron) with four sets of descriptors (Morgan2 fingerprints, Morgan3 fingerprints, MACCS keys and 2D physicochemical property descriptors). Testing on holdout data as well as data sets of “dark chemical matter” (i.e. compounds that have been extensively tested in biological assays but have never shown activity) and known bad actors show that the multilayer perceptron classifiers in combination with Morgan2 fingerprints outperform other setups in most cases. The best multilayer perceptron classifiers obtained Matthews correlation coefficients of up to 0.648 on holdout data. These models are available via a free web service.

AlphaScreen is one of the most widely used assay technologies in drug discovery due to its versatility, dynamic range and sensitivity. However, a presence of false positives and frequent hitters contributes to difficulties with an interpretation of measured HTS data. Although filters do exist to identify frequent hitters for AlphaScreen, they are frequently based on privileged scaffolds. The development of such filters is time consuming and requires deep domain knowledge. Recently, machine learning and artificial intelligence methods are emerging as important tools to advance drug discovery and chemoinformatics, including their application to identification of frequent hitters in screening assays. However, the relative performance and complementarity of the Machine Learning and scaffold-based techniques has not yet been comprehensively compared. In this study, we analysed filters based on the privileged scaffolds with filters built using machine learning. Our results demonstrate that machine-learning methods provide more accurate filters for identification of frequent hitters in AlphaScreen assays than scaffold-based methods and can be easily redeveloped once new data are measured. We present highly accurate models to identify frequent hitters in AlphaScreen assays.

For a significant number of years, scientists of many persuasions have assayed natural product materials ranging from crude extracts to pure compounds, in a multitude of assays causally related to some biological processes. However, in a very significant number of submitted papers and published articles, what may be considered as canned biological assays were used, and if a positive effect was observed, then the authors would claim that the material assayed was a potential drug lead. This also occurred with pure synthetic compounds and compounds derived from natural products by simple chemical modifications. However, what has now become quite obvious—with all such classes of materials—is that there are many promiscuous players with multiple bioactivities. These can range from relatively crude extracts, pure compounds from natural products, synthetic processes that produce natural product derivatives, and even compounds that are truly synthetic in origin. There is also a potential problem with the data from crude to purified extracts being used to claim some form of beneficial activities for such materials, to sell that particular mixture to the lay public, by very careful descriptions of its possible uses due to legal hurdles. With the advent of artificial intelligence and very large compound databases, some of which may well contain impure materials, scientists from a variety of backgrounds have begun to utilize such listings to obtain compounds for their low to high throughput biological screens, without realizing that there are very significant numbers of active compounds (eg, pan assay interference compounds and invalid metabolic panaceas), that will hit in many different screens for a variety of reasons, thus leading to significant wasted efforts and published scientific articles that have incorrect results. This commentary gives some of the history of such materials but is designed to be used as a warning to both researchers and in particular, journal editors, and reviewers, that reports of biological results that are claimed to be the result of the compounds used, need to be very carefully screened for results due to such promiscuous compounds, irrespective of their nominal source(s). All literature searches were made by the author and the background knowledge has come from more than 55 years of research in industry and governmental laboratories in both the United Kingdom and the United States, for enzyme inhibitors/activators as well as antimicrobial and antitumor lead compounds mainly from natural product sources. The conclusion that I came up with as a result is this: Caveat emptor. (Curr Ther Res Clin Exp. 2021; 82:XXX–XXX) © 2021 Elsevier HS Journals, Inc.