Comment on The Ecstasy and Agony of Assay Interference Compounds

Tackling assay interference associated with small molecules

Biochemical and cell-based assays are essential to discovering and optimizing efficacious and safe drugs, agrochemicals and cosmetics. However, false assay readouts stemming from colloidal aggregation, chemical reactivity, chelation, light signal attenuation and emission, membrane disruption, and other interference mechanisms remain a considerable challenge in screening synthetic compounds and natural products. To address assay interference, a range of powerful experimental approaches are available and in silico methods are now gaining traction. This Review begins with an overview of the scope and limitations of experimental approaches for tackling assay interference. It then focuses on theoretical methods, discusses strategies for their integration with experimental approaches, and provides recommendations for best practices. The Review closes with a summary of the critical facts and an outlook on potential future developments.

A practical guide to machine-learning scoring for structure-based virtual screening

Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.

Virtual Screening-Based Drug Development for the Treatment of Nervous System Diseases

The incidence rate of nervous system diseases has increased in recent years. Nerve injury or neurodegenerative diseases usually cause neuronal loss and neuronal circuit damage, which seriously affect motor nerve and autonomic nervous function. Therefore, safe and effective treatment is needed. As traditional drug research becomes slower and more expensive, it is vital to enlist the help of cutting- edge technology. Virtual screening (VS) is an attractive option for the identification and development of promising new compounds with high efficiency and low cost. With the assistance of computer- aided drug design (CADD), VS is becoming more and more popular in new drug development and research. In recent years, it has become a reality to transform non-neuronal cells into functional neurons through small molecular compounds, which provides a broader application prospect than transcription factor-mediated neuronal reprogramming. This review mainly summarizes related theory and technology of VS and the drug research and development using VS technology in nervous system diseases in recent years, and focuses more on the potential application of VS technology in neuronal reprogramming, thus facilitating new drug design for both prevention and treatment of nervous system diseases.

Dual Targeting of MDM4 and FTH1 by MMRi71 for Induced Protein Degradation and p53-Independent Apoptosis in Leukemia Cells

MDM2 and MDM4 are cancer drug targets validated in multiple models for p53-based cancer therapies. The RING domains of MDM2 and non-p53-binder MDM2 splice isoforms form RING domain heterodimer polyubiquitin E3 ligases with MDM4, which regulate p53 stability in vivo and promote tumorigenesis independent of p53. Despite the importance of the MDM2 RING domain in p53 regulation and cancer development, small molecule inhibitors targeting the E3 ligase activity of MDM2-MDM4 are poorly explored. Here, we describe the synthesis and characterization of quinolinol derivatives for the identification of analogs that are capable of targeting the MDM2-MDM4 heterodimer E3 ligase and inducing apoptosis in cells. The structure-activity-relationship (SAR) study identified structural moieties critical for the inhibitory effects toward MDM2-MDM4 E3 ligase, the targeted degradation of MDM4 and FTH1 in cells, and anti-proliferation activity. Lead optimization led to the development of compound MMRi71 with improved activity. In addition to accumulating p53 proteins in wt-p53 bearing cancer cells as expected of any MDM2 inhibitors, MMRi71 effectively kills p53-null leukemia cells, an activity that conventional MDM2-p53 disrupting inhibitors lack. This study provides a prototype structure for developing MDM4/FTH1 dual-targeting inhibitors as potential cancer therapeutics.

Promoting GAINs (Give Attention to Limitations in Assays) over PAINs Alerts: no PAINS, more GAINs

Many concepts and guidelines in medicinal chemistry have been introduced to aid in successful drug discovery and development. An example is the concept of Pan-Assay Interference Compounds (PAINS) and the elimination of such nuisance compounds from high-throughput screening (HTS) libraries. PAINs, along with other guidelines in medicinal chemistry, are like double-edged swords. If used appropriately, they may be beneficial for drug discovery and development. However, rigid and blind use of such concepts can hinder productivity. In this perspective, we introduce GAINS (give attention to limitations in assays) and highlight its relevance for successful drug discovery.

Identification of N,N-arylalkyl-picolinamide derivatives targeting the RNA-binding protein HuR, by combining biophysical fragment-screening and molecular hybridization

Hu proteins are members of the RNA-binding protein (RBP) family and play a pivotal role in the regulation of post-transcriptional processes. Through interaction with selected mRNAs, RBPs regulate their function and stability; as a consequence, RBP dysregulation can cause abnormal translation of key proteins involved in several pathologies. In the past few years, this observation has sparked interest to develop new treatments against these pathologies by using small molecules able to modulate RBP activity. Among the four Hu proteins, we have directed our efforts towards the isoform HuR, which is mainly involved in cancer, inflammation and retinopathy. Aimed at developing compounds able to modulate the stability of HuR-mRNA complexes, in the present work, we applied a biophysical fragment screening by assessing a library of halogen-enriched heterocyclic fragments (HEFLibs) via Surface Plasmon Resonance (SPR) and Saturation Transfer Difference (STD) NMR to select promising fragments able to interact with HuR. One selected fragment and a few commercially available congeners were exploited to design and synthesize focused analogues of compound N-(3-chlorobenzyl)-N-(3,5-dihydroxyphenethyl)-4-hydroxybenzamide (1), our previously reported hit. STDNMR spectroscopy, molecular modeling, and SPR offered further insight into the HuR-small molecule interaction and showed that fragment-based approaches represent a promising and yet underexplored strategy to tackle such unusual targets. Lastly, fluorescence polarization (FP) studies revealed the capability of the new compounds to interfere with the formation of the HuR-mRNA complex. This is, to our knowledge, the first fragment-based campaign performed on the Hu protein class, and one of the few examples in the larger RBP field and constitutes an important step in the quest for the rational modulation of RBPs and related RNA functions by small molecules.

Understanding the Polypharmacological Profiles of Triple Reuptake Inhibitors by Molecular Simulation

The triple reuptake inhibitors (TRIs) class is a class of effective inhibitors of human monoamine transporters (hMATs), which includes dopamine, norepinephrine, and serotonin transporters (hDATs, hNETs, and hSERTs). Due to the high degree of structural homology of the binding sites of those transporters, it is a great challenge to design potent TRIs with fine-tuned binding profiles. The molecular determinants responsible for the binding selectivity of TRIs to hDATs, hNETs, and hSERTs remain elusive. In this study, the solved X-ray crystallographic structure of hSERT in complex with escitalopram was used as a basis for modeling nine complexes of three representative TRIs (SEP225289, NS2359, and EB1020) bound to their corresponding targets. Molecular dynamics (MD) and effective post-trajectory analysis were performed to estimate the drug binding free energies and characterize the selective profiles of each TRI to hMATs. The common binding mode of studied TRIs to hMATs was revealed by hierarchical clustering analysis of the per-residue energy. Furthermore, the combined protein-ligand interaction fingerprint and residue energy contribution analysis indicated that several conserved and nonconserved "Warm Spots" such as S149, V328, and M427 in hDAT, F317, F323, and V325 in hNET and F335, F341, and V343 in hSERT were responsible for the TRI-binding selectivity. These findings provided important information for rational design of a single drug with better polypharmacological profiles through modulating multiple targets.

Anti-SARS-CoV-2 activities in vitro of Shuanghuanglian preparations and bioactive ingredients

Human infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19) and there is no cure currently. The 3CL protease (3CLpro) is a highly conserved protease which is indispensable for CoVs replication, and is a promising target for development of broad-spectrum antiviral drugs. In this study we investigated the anti-SARS-CoV-2 potential of Shuanghuanglian preparation, a Chinese traditional patent medicine with a long history for treating respiratory tract infection in China. We showed that either the oral liquid of Shuanghuanglian, the lyophilized powder of Shuanghuanglian for injection or their bioactive components dose-dependently inhibited SARS-CoV-2 3CLpro as well as the replication of SARS-CoV-2 in Vero E6 cells. Baicalin and baicalein, two ingredients of Shuanghuanglian, were characterized as the first noncovalent, nonpeptidomimetic inhibitors of SARS-CoV-2 3CLpro and exhibited potent antiviral activities in a cell-based system. Remarkably, the binding mode of baicalein with SARS-CoV-2 3CLpro determined by X-ray protein crystallography was distinctly different from those of known 3CLpro inhibitors. Baicalein was productively ensconced in the core of the substrate-binding pocket by interacting with two catalytic residues, the crucial S1/S2 subsites and the oxyanion loop, acting as a “shield” in front of the catalytic dyad to effectively prevent substrate access to the catalytic dyad within the active site. Overall, this study provides an example for exploring the in vitro potency of Chinese traditional patent medicines and effectively identifying bioactive ingredients toward a specific target, and gains evidence supporting the in vivo studies of Shuanghuanglian oral liquid as well as two natural products for COVID-19 treatment.

Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement

Developing realistic data sets for evaluating virtual screening methods is a task that has been tackled by the cheminformatics community for many years. Numerous artificially constructed data collections were developed, such as DUD, DUD-E, or DEKOIS. However, they all suffer from multiple drawbacks, one of which is the absence of experimental results confirming the impotence of presumably inactive molecules, leading to possible false negatives in the ligand sets. In light of this problem, the PubChem BioAssay database, an open-access repository providing the bioactivity information of compounds that were already tested on a biological target, is now a recommended source for data set construction. Nevertheless, there exist several issues with the use of such data that need to be properly addressed. In this article, an overview of benchmarking data collections built upon experimental PubChem BioAssay input is provided, along with a thorough discussion of noteworthy issues that one must consider during the design of new ligand sets from this database. The points raised in this review are expected to guide future developments in this regard, in hopes of offering better evaluation tools for novel in silico screening procedures.

Discovery and Structure-Activity Relationship Study of (Z)-5-Methylenethiazolidin-4-one Derivatives as Potent and Selective Pan-phosphatidylinositol 5-Phosphate 4-Kinase Inhibitors

Due to their role in many important signaling pathways, phosphatidylinositol 5-phosphate 4-kinases (PI5P4Ks) are attractive targets for the development of experimental therapeutics for cancer, metabolic, and immunological disorders. Recent efforts to develop small molecule inhibitors for these lipid kinases resulted in compounds with low- to sub-micromolar potencies. Here, we report the identification of CVM-05-002 using a high-throughput screen of PI5P4Kα against our in-house kinase inhibitor library. CVM-05-002 is a potent and selective inhibitor of PI5P4Ks, and a 1.7 Å X-ray structure reveals its binding interactions in the ATP-binding pocket. Further investigation of the structure-activity relationship led to the development of compound 13, replacing the rhodanine-like moiety present in CVM-05-002 with an indole, a potent pan-PI5P4K inhibitor with excellent kinome-wide selectivity. Finally, we employed isothermal cellular thermal shift assays (CETSAs) to demonstrate the effective cellular target engagement of PI5P4Kα and -β by the inhibitors in HEK 293T cells.

Identification of a 2,4-diaminopyrimidine scaffold targeting Trypanosoma brucei pteridine reductase 1 from the LIBRA compound library screening campaign

The LIBRA compound library is a collection of 522 non-commercial molecules contributed by various Italian academic laboratories. These compounds have been designed and synthesized during different medicinal chemistry programs and are hosted by the Italian Institute of Technology. We report the screening of the LIBRA compound library against Trypanosoma brucei and Leishmania major pteridine reductase 1, TbPTR1 and LmPTR1. Nine compounds were active against parasitic PTR1 and were selected for cell-based parasite screening, as single agents and in combination with methotrexate (MTX). The most interesting TbPTR1 inhibitor identified was 4-(benzyloxy)pyrimidine-2,6-diamine (LIB_66). Subsequently, six new LIB_66 derivatives were synthesized to explore its Structure-Activity-Relationship (SAR) and absorption, distribution, metabolism, excretion and toxicity (ADMET) properties. The results indicate that PTR1 has a preference to bind inhibitors, which resemble its biopterin/folic acid substrates, such as the 2,4-diaminopyrimidine derivatives.

Synthesis, characterization, and biological activity of a triphenylphosphonium-containing imidazolium salt against select bladder cancer cell lines

Imidazolium salts have shown great promise as anticancer materials. A new imidazolium salt (TPP1), with a triphenylphosphonium substituent, has been synthesized and evaluated for in vitro and in vivo cytotoxicity against bladder cancer. TPP1 was determined to have a GI50 ranging from 200 to 250 μM over a period of 1 h and the ability to effectively inhibit bladder cancer. TPP1 induces apoptosis, and it appears to act as a direct mitochondrial toxin. TPP1 was applied intravesically to a bladder cancer mouse model based on the carcinogen N-butyl-N-(4-hydroxybutyl)nitrosamine (BBN). Cancer selectivity of TPP1 was demonstrated, as BBN-induced tumors exhibited apoptosis but normal adjacent urothelium did not. These results suggest that TPP1 may be a promising intravesical agent for the treatment of bladder cancer.

Identification of Target Associations for Polypharmacology from Analysis of Crystallographic Ligands of the Protein Data Bank

The design of a chemical entity that potently and selectively binds to a biological target of therapeutic relevance has dominated the scene of drug discovery so far. However, recent findings suggest that multitarget ligands may be endowed with superior efficacy and be less prone to drug resistance. The Protein Data Bank (PDB) provides experimentally validated structural information about targets and bound ligands. Therefore, it represents a valuable source of information to help identifying active sites, understanding pharmacophore requirements, designing novel ligands, and inferring structure-activity relationships. In this study, we performed a large-scale analysis of the PDB by integrating different ligand-based and structure-based approaches, with the aim of identifying promising target associations for polypharmacology based on reported crystal structure information. First, the 2D and 3D similarity profiles of the crystallographic ligands were evaluated using different ligand-based methods. Then, activity data of pairs of similar ligands binding to different targets were inspected by comparing structural information with bioactivity annotations reported in the ChEMBL, BindingDB, BindingMOAD, and PDBbind databases. Afterward, extensive docking screenings of ligands in the identified cross-targets were made in order to validate and refine the ligand-based results. Finally, the therapeutic relevance of the identified target combinations for polypharmacology was evaluated from comparison with information on therapeutic targets reported in the Therapeutic Target Database (TTD). The results led to the identification of several target associations with high therapeutic potential for polypharmacology.

Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies

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.

Ligand potency - an essential estimator for drug design: between intuition, misinterpretation and serendipity

This is a review of developments in the field of potency, which is an essential estimator for drug design. We discuss the basic concepts of research and discovery, focusing on how misinterpretation in this area, - but also intuition and serendipity, which are common in drug design - helped us to pave a bumpy road towards better drugs. How far we still are from this goal can be seen in the Eroom law, which states that the efficiency of pharma research and development is decreasing. At the same time, pharma bestsellers are getting older.

Quantitative high-throughput screening assays for the discovery and development of SIRPα-CD47 interaction inhibitors

CD47 is an immune checkpoint molecule that downregulates key aspects of both the innate and adaptive anti-tumor immune response via its counter receptor SIRPα, and it is expressed at high levels in a wide variety of tumor types. This has led to the development of biologics that inhibit SIRPα engagement including humanized CD47 antibodies and a soluble SIRPα decoy receptor that are currently undergoing clinical trials. Unfortunately, toxicological issues, including anemia related to on-target mechanisms, are barriers to their clinical advancement. Another potential issue with large biologics that bind CD47 is perturbation of CD47 signaling through its high-affinity interaction with the matricellular protein thrombospondin-1 (TSP1). One approach to avoid these shortcomings is to identify and develop small molecule molecular probes and pretherapeutic agents that would (1) selectively target SIRPα or TSP1 interactions with CD47, (2) provide a route to optimize pharmacokinetics, reduce on-target toxicity and maximize tissue penetration, and (3) allow more flexible routes of administration. As the first step toward this goal, we report the development of an automated quantitative high-throughput screening (qHTS) assay platform capable of screening large diverse drug-like chemical libraries to discover novel small molecules that inhibit CD47-SIRPα interaction. Using time-resolved Förster resonance energy transfer (TR-FRET) and bead-based luminescent oxygen channeling assay formats (AlphaScreen), we developed biochemical assays, optimized their performance, and individually tested them in small-molecule library screening. Based on performance and low false positive rate, the LANCE TR-FRET assay was employed in a ~90,000 compound library qHTS, while the AlphaScreen oxygen channeling assay served as a cross-validation orthogonal assay for follow-up characterization. With this multi-assay strategy, we successfully eliminated compounds that interfered with the assays and identified five compounds that inhibit the CD47-SIRPα interaction; these compounds will be further characterized and later disclosed. Importantly, our results validate the large library qHTS for antagonists of CD47-SIRPα interaction and suggest broad applicability of this approach to screen chemical libraries for other protein-protein interaction modulators.

Computational advances in combating colloidal aggregation in drug discovery

Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.

Novel Benzene-Based Carbamates for AChE/BChE Inhibition: Synthesis and Ligand/Structure-Oriented SAR Study

A series of new benzene-based derivatives was designed, synthesized and comprehensively characterized. All of the tested compounds were evaluated for their in vitro ability to potentially inhibit the acetyl- and butyrylcholinesterase enzymes. The selectivity index of individual molecules to cholinesterases was also determined. Generally, the inhibitory potency was stronger against butyryl- compared to acetylcholinesterase; however, some of the compounds showed a promising inhibition of both enzymes. In fact, two compounds (23, benzyl ethyl(1-oxo-1-phenylpropan-2-yl)carbamate and 28, benzyl (1-(3-chlorophenyl)-1-oxopropan-2-yl) (methyl)carbamate) had a very high selectivity index, while the second one (28) reached the lowest inhibitory concentration IC₅₀ value, which corresponds quite well with galanthamine. Moreover, comparative receptor-independent and receptor-dependent structure–activity studies were conducted to explain the observed variations in inhibiting the potential of the investigated carbamate series. The principal objective of the ligand-based study was to comparatively analyze the molecular surface to gain insight into the electronic and/or steric factors that govern the ability to inhibit enzyme activities. The spatial distribution of potentially important steric and electrostatic factors was determined using the probability-guided pharmacophore mapping procedure, which is based on the iterative variable elimination method. Additionally, planar and spatial maps of the host–target interactions were created for all of the active compounds and compared with the drug molecules using the docking methodology.

Identification of Novel Potential Inhibitors of Pteridine Reductase 1 in Trypanosoma brucei via Computational Structure-Based Approaches and in Vitro Inhibition Assays

Pteridine reductase 1 (PTR1) is a trypanosomatid multifunctional enzyme that provides a mechanism for escape of dihydrofolate reductase (DHFR) inhibition. This is because PTR1 can reduce pterins and folates. Trypanosomes require folates and pterins for survival and are unable to synthesize them de novo. Currently there are no anti-folate based Human African Trypanosomiasis (HAT) chemotherapeutics in use. Thus, successful dual inhibition of Trypanosoma brucei dihydrofolate reductase (TbDHFR) and Trypanosoma brucei pteridine reductase 1 (TbPTR1) has implications in the exploitation of anti-folates. We carried out molecular docking of a ligand library of 5742 compounds against TbPTR1 and identified 18 compounds showing promising binding modes. The protein-ligand complexes were subjected to molecular dynamics to characterize their molecular interactions and energetics, followed by in vitro testing. In this study, we identified five compounds which showed low micromolar Trypanosome growth inhibition in in vitro experiments that might be acting by inhibition of TbPTR1. Compounds RUBi004, RUBi007, RUBi014, and RUBi018 displayed moderate to strong antagonism (mutual reduction in potency) when used in combination with the known TbDHFR inhibitor, WR99210. This gave an indication that the compounds might inhibit both TbPTR1 and TbDHFR. RUBi016 showed an additive effect in the isobologram assay. Overall, our results provide a basis for scaffold optimization for further studies in the development of HAT anti-folates.

Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set

Biochemical assay interference is becoming increasingly recognized as a significant waste of resource in drug discovery, both in industry and academia. A seminal publication from Baell and Holloway raised the awareness of this issue, and they published a set of alerts to identify what they described as PAINS (pan-assay interference compounds). These alerts have been taken up by drug discovery groups, even though the original paper had a somewhat limited data set. Here, we have taken Lilly’s far larger internal data set to assess the PAINS alerts on four criteria: promiscuity (over six assay formats including AlphaScreen), compound stability, cytotoxicity, and presence of a high Hill slope as a surrogate for non-1:1 protein–ligand binding. It was found that only three of the alerts show pan-assay promiscuity, and the alerts appear to encode primarily AlphaScreen promiscuous molecules. Although not enriching for pan-assay promiscuity, many of the alerts do encode molecules that are unstable, show cytotoxicity, and increase the prevalence of high Hill slopes.

Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds

Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.

Nuisance Compounds, PAINS Filters, and Dark Chemical Matter in the GSK HTS Collection

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.

Fragment-growing guided design of Keap1-Nrf2 protein-protein interaction inhibitors for targeting myocarditis

Small-molecule inhibitors that block the Keap1-Nrf2 protein-protein interactions are being intensely pursued as a new therapeutic strategy for oxidative stress-related diseases, such as cancer, diabetes, Alzheimer's disease, arteriosclerosis, inflammation and myocarditis. However, there are not enough studies on antioxidant treatments using small molecules in myocarditis. We herein provided a series of novel hydronaphthoquinones as the Keap1-Nrf2 interaction inhibitors targeting LPS-induced myocarditis both in vitro and in vivo. These compounds were designed through an in-silico fragment growing approach based on our previous reported compound, S47 (1). The new compounds were predicted to form additional hydrogen bonds with the S363 residue, leading to higher inhibitory activity. Among these new derivatives, compounds S01 and S05 emerged as inhibitors with significant biochemical potency, as determined by fluorescent anisotropy assay and confirmed by surface plasmon resonance (SPR) and differential scanning fluorimetry (DSF) assays. These inhibitors can dose-dependently protect the H9c2 cardiac cells against LPS-induced injury (100% at 2 μM and 4 μM) and effectively prolong survival or save the life of LPS-injured mice. Mechanistic studies showed that these inhibitors could release Nrf2 in H9c2 cells and LPS-inflammatory mouse models and translocate into the nucleus in a dose-response manner, which significantly increased the downstream genes (HO-1, NQO-1) and the pro-inflammatory cytokines (TNF-α, IL-1β, IL-6), while ROS production dramatically decreased. Their protective effects and the mechanism of action were further confirmed by siNrf2 transfected experiment. Collectively, the novel hydronaphthoquinones can be used as promising lead compounds for the study of Keap1-Nrf2 protein-protein interactions and further anti-myocarditis drug development.

Hit Dexter: A Machine-Learning Model for the Prediction of Frequent Hitters

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.

Seven Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017-Utility and Limitations

Pan-Assay Interference Compounds (PAINS) are very familiar to medicinal chemists who have spent time fruitlessly trying to optimize these nonprogressible compounds. Electronic filters formulated to recognize PAINS can process hundreds and thousands of compounds in seconds and are in widespread current use to identify PAINS in order to exclude them from further analysis. However, this practice is fraught with danger because such black box treatment is simplistic. Here, we outline for the first time all necessary considerations for the appropriate use of PAINS filters.