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This perspective is an attempt to document the problems that medicinal chemists are facing in drug discovery. It is also trying to identify relevant/possible, research areas in which academics can have an impact and should thus be the subject of grant calls. Accordingly, it describes how hit discovery happens, how compounds to be screened are selected from available chemicals and the possible reasons for the recurrent paucity of useful/exploitable results reported. This is followed by the successful hit to lead stories leading to recent and original antibacterials which are, or about to be, used in human medicine. Then, illustrated considerations and suggestions are made on the possible inputs of academic medicinal chemists. This starts with the observation that discovering a “good” hit in the course of a screening campaign still rely on a lot of luck – which is within the reach of academics –, that the hit to lead process requires a lot of chemistry and that if public–private partnerships can be important throughout these stages, they are absolute requirements for clinical trials. Concerning suggestions to improve the current hit success rate, one academic input in organic chemistry would be to identify new and pertinent chemical space, design synthetic accesses to reach these and prepare the corresponding chemical libraries. Concerning hit to lead programs on a given target, if no new hits are available, previously reported leads along with new structural data can be pertinent starting points to design, prepare and assay original analogues. In conclusion, this text is an actual plea illustrating that, in many countries, academic research in medicinal chemistry should be more funded, especially in the therapeutic area neglected by the industry. At the least, such funds would provide the intensive to secure series of hopefully relevant chemical entities which appears to often lack when considering the results of academic as well as industrial screening campaigns.

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. [ABSTRACT FROM AUTHOR]

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.

Background Fluorescent detection methods are indispensable tools for chemical biology. However, the frequent appearance of potential fluorescent compound has greatly interfered with the recognition of compounds with genuine activity. Such fluorescence interference is especially difficult to identify as it is reproducible and possesses concentration-dependent characteristic. Therefore, the development of a credible screening tool to detect fluorescent compounds from chemical libraries is urgently needed in early stages of drug discovery. Results In this study, we developed a webserver ChemFLuo for fluorescent compound detection, based on two large and high-quality training datasets containing 4906 blue and 8632 green fluorescent compounds. These molecules were used to construct a group of prediction models based on the combination of three machine learning algorithms and seven types of molecular representations. The best blue fluorescence prediction model achieved with balanced accuracy (BA) = 0.858 and area under the receiver operating characteristic curve (AUC) = 0.931 for the validation set, and BA = 0.823 and AUC = 0.903 for the test set. The best green fluorescence prediction model achieved the prediction accuracy with BA = 0.810 and AUC = 0.887 for the validation set, and BA = 0.771 and AUC = 0.852 for the test set. Besides prediction model, 22 blue and 16 green representative fluorescent substructures were summarized for the screening of potential fluorescent compounds. The comparison with other fluorescence detection tools and theapplication to external validation sets and large molecule libraries have demonstrated the reliability of prediction model for fluorescent compound detection. Conclusion ChemFLuo is a public webserver to filter out compounds with undesirable fluorescent properties, which will benefit the design of high-quality chemical libraries for drug discovery. It is freely available at http://admet.scbdd.com/chemfluo/index/. [ABSTRACT FROM AUTHOR]

Background High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery. Results In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram. Conclusion Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy. [ABSTRACT FROM AUTHOR]

ABSTRACT: 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.

The Yap-Hippo pathway has a significant role in regulating cell proliferation and growth, thus controlling organ size and regeneration. The Hippo pathway regulates two highly conserved, transcription coactivators, YAP and TAZ. The upstream regulators of the Yap-Hippo pathway have not been fully characterized. The aim of this study was to use a siRNA screen, in a liver biliary cell line, to identify regulators of the Yap-Hippo pathway that allow activation of the YAP transcription coactivator at high cell density. Activation of the YAP transcription coactivator was monitored using a high-content, image-based assay that measured the intracellular localization of native YAP protein. Active siRNAs were identified and further validated by quantification of CYR61 mRNA levels (a known YAP target gene). The effect of compounds targeting the putative gene targets identified as hits was also used for further validation. A number of validated hits reveal basic aspects of Yap-Hippo biology, such as components of the nuclear pore, by which YAP cytoplasmic–nuclear shuttling occurs, or how proteasomal degradation regulates intracellular YAP concentrations, which then alter YAP localization and transcription. Such results highlight how targeting conserved cellular functions can lead to validated activity in phenotypic assays. [ABSTRACT FROM AUTHOR]

Abstract: The goal of this study is the understanding of biologically promiscuous compounds (frequent hitters) in HTS outcomes through their chemical behavior estimated via reactivity descriptors. Chemical reactivity is often an undesirable property due to the lack in biological selectivity of compounds comprised in HTS libraries. In this study the reactivity indexes have been computed within the DFT formalism, at different levels of theory, for two classes of representative compounds compiled from PubChem database, one comprising frequent hitters and the second one comprising rare hitters (biologically more selective compounds). We found that frequent hitters exert increased reactivity, mainly due to their electrophilic character, compared to the more selective class of compounds. [Copyright &y& Elsevier]

This perspective is an attempt to document the problems that medicinal chemists are facing in drug discovery. It is also trying to identify relevant/possible, research areas in which academics can have an impact and should thus be the subject of grant calls. Accordingly, it describes how hit discovery happens, how compounds to be screened are selected from available chemicals and the possible reasons for the recurrent paucity of useful/exploitable results reported. This is followed by the successful hit to lead stories leading to recent and original antibacterials which are, or about to be, used in human medicine. Then, illustrated considerations and suggestions are made on the possible inputs of academic medicinal chemists. This starts with the observation that discovering a "good" hit in the course of a screening campaign still rely on a lot of luck - which is within the reach of academics -, that the hit to lead process requires a lot of chemistry and that if public-private partnerships can be important throughout these stages, they are absolute requirements for clinical trials. Concerning suggestions to improve the current hit success rate, one academic input in organic chemistry would be to identify new and pertinent chemical space, design synthetic accesses to reach these and prepare the corresponding chemical libraries. Concerning hit to lead programs on a given target, if no new hits are available, previously reported leads along with new structural data can be pertinent starting points to design, prepare and assay original analogues. In conclusion, this text is an actual plea illustrating that, in many countries, academic research in medicinal chemistry should be more funded, especially in the therapeutic area neglected by the industry. At the least, such funds would provide the intensive to secure series of hopefully relevant chemical entities which appears to often lack when considering the results of academic as well as industrial screening campaigns., (Copyright © 2022, Janin.)

Frequent hitters are compounds that are detected as a “hit” in multiple high-throughput screening (HTS) assays. Such behavior is specific (e.g., target family related) or unspecific (e.g., reactive compounds) or can result from a combination of such behaviors. Detecting such hits while predicting the underlying reason behind their promiscuous behavior is desirable because it provides valuable information not only about the compounds themselves but also about the assay methodology and target classes at hand. This information can also greatly reduce cost and time during HTS hit profiling. The present study exemplifies how to mine large HTS data repositories, such as the one at Boehringer Ingelheim, to identify frequent hitters, gain further insights into the causes of promiscuous behavior, and generate models for predicting promiscuous compounds. Applications of this approach are demonstrated using two recent large-scale HTS assays. The authors believe this analysis and its concrete applications are valuable tools for streamlining and accelerating decision-making processes during the course of hit discovery. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

A significant challenge in high-throughput screening (HTS) campaigns is the identification of assay technology interference compounds. A Compound Interfering with an Assay Technology (CIAT) gives false readouts in many assays. CIATs are often considered viable hits and investigated in follow-up studies, thus impeding research and wasting resources. In this study, we developed a machine-learning (ML) model to predict CIATs for three assay technologies. The model was trained on known CIATs and non-CIATs (NCIATs) identified in artefact assays and described by their 2D structural descriptors. Usual methods identifying CIATs are based on statistical analysis of historical primary screening data and do not consider experimental assays identifying CIATs. Our results show successful prediction of CIATs for existing and novel compounds and provide a complementary and wider set of predicted CIATs compared to BSF, a published structure-independent model, and to the PAINS substructural filters. Our analysis is an example of how well-curated datasets can provide powerful predictive models despite their relatively small size., (© 2019 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.)

High-throughput screening (HTS) hits include compounds with undesirable properties. Many filters have been described to identify such hits. Notably, pan-assay interference compounds (PAINS) has been adopted by the community as the standard term to refer to such filters, and very useful guidelines have been adopted by the American Chemical Society (ACS) and subsequently triggered a healthy scientific debate about the pitfalls of draconian use of filters. Using an inhibitory frequency index, we have analyzed in detail the promiscuity profile of the whole GlaxoSmithKline (GSK) HTS collection comprising more than 2 million unique compounds that have been tested in hundreds of screening assays. We provide a comprehensive analysis of many previously published filters and newly described classes of nuisance structures that may serve as a useful source of empirical information to guide the design or growth of HTS collections and hit triaging strategies.

False-positive assay readouts caused by badly behaving compounds-frequent hitters, pan-assay interference compounds (PAINS), aggregators, and others-continue to pose a major challenge to experimental screening. There are only a few in silico methods that allow the prediction of such problematic compounds. We report the development of Hit Dexter, two extremely randomized trees classifiers for the prediction of compounds likely to trigger positive assay readouts either by true promiscuity or by assay interference. The models were trained on a well-prepared dataset extracted from the PubChem Bioassay database, consisting of approximately 311 000 compounds tested for activity on at least 50 proteins. Hit Dexter reached MCC and AUC values of up to 0.67 and 0.96 on an independent test set, respectively. The models are expected to be of high value, in particular to medicinal chemists and biochemists who can use Hit Dexter to identify compounds for which extra caution should be exercised with positive assay readouts. Hit Dexter is available as a free web service at http://hitdexter.zbh. uni-hamburg.de., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)