Quantitative analyses of aggregation, autofluorescence, and reactivity artifacts in a screen for inhibitors of a thiol protease

Structure-Aided Computational Design of Triazole-Based Targeted Covalent Inhibitors of Cruzipain

Cruzipain (CZP), the major cysteine protease present in T. cruzi, the ethiological agent of Chagas disease, has attracted particular attention as a therapeutic target for the development of targeted covalent inhibitors (TCI). The vast chemical space associated with the enormous molecular diversity feasible to explore by means of modern synthetic approaches allows the design of CZP inhibitors capable of exhibiting not only an efficient enzyme inhibition but also an adequate translation to anti-T. cruzi activity. In this work, a computer-aided design strategy was developed to combinatorially construct and screen large libraries of 1,4-disubstituted 1,2,3-triazole analogues, further identifying a selected set of candidates for advancement towards synthetic and biological activity evaluation stages. In this way, a virtual molecular library comprising more than 75 thousand diverse and synthetically feasible analogues was studied by means of molecular docking and molecular dynamic simulations in the search of potential TCI of CZP, guiding the synthetic efforts towards a subset of 48 candidates. These were synthesized by applying a Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) centered synthetic scheme, resulting in moderate to good yields and leading to the identification of 12 hits selectively inhibiting CZP activity with IC50 in the low micromolar range. Furthermore, four triazole derivatives showed good anti-T. cruzi inhibition when studied at 50 μM; and Ald-6 excelled for its high antitrypanocidal activity and low cytotoxicity, exhibiting complete in vitro biological activity translation from CZP to T. cruzi. Overall, not only Ald-6 merits further advancement to preclinical in vivo studies, but these findings also shed light on a valuable chemical space where molecular diversity might be explored in the search for efficient triazole-based antichagasic agents.

Cheminformatics analysis of indoleamine and tryptophan 2,3-dioxygenase inhibitors: A descriptor and fingerprint based machine learning approach to disclose selectivity measures

Indoleamine 2,3-dioxygenase (IDO) and tryptophan 2,3-dioxygenase (TDO) are attractive drug targets for cancer immunotherapy. After disappointing results of the epacadostat as a selective IDO inhibitor in phase III clinical trials, there is much interest in the development of the TDO selective inhibitors. In the current study, several data analysis methods and machine learning approaches including logistic regression, Random Forest, XGBoost and Support Vector Machines were used to model a data set of compounds retrieved from ChEMBL. Models based on the Morgan fingerprints revealed notable fragments for the selective inhibition of the IDO, TDO or both. Multiple fragment docking was performed to find the best set of bound fragments and their orientation in the space for efficient linking. Linking the fragments and optimization of the final molecules were accomplished by means of an artificial intelligence generative framework. Finally, selectivity of the optimized molecules was assessed and the top 4 lead molecules were filtered through PAINS, Brenk and NIH filters. Results indicated that phenyloxalamide, fluoroquinoline, and 3-bromo-4-fluroaniline confer selectivity towards the IDO inhibition. Correspondingly, 1-benzyl-1H-naphtho[2,3-d][1,2,3]triazole-4,9-dione was found to be an integral fragment for the selective inhibition of the TDO by constituting a coordination bond with the Fe atom of heme. In addition, furo[2,3-c]pyridine-2,3-diamine was found as a common fragment for inhibition of the both targets and can be used in the design of the dual target inhibitors of the IDO and TDO. The new fragments introduced here can be a useful building blocks for incorporation into the selective TDO or dual IDO/TDO inhibitors.

Small-Molecule Disruptors of the Interaction between Calcium- and Integrin-Binding Protein 1 and Integrin αIIbβ3 as Novel Antiplatelet Agents

Thrombosis, a key factor in most cardiovascular diseases, is a major contributor to human mortality. Existing antithrombotic agents carry a risk of bleeding. Consequently, there is a keen interest in discovering innovative antithrombotic agents that can prevent thrombosis without negatively impacting hemostasis. Platelets play crucial roles in both hemostasis and thrombosis. We have previously characterized calcium- and integrin-binding protein 1 (CIB1) as a key regulatory molecule that regulates platelet function. CIB1 interacts with several platelet proteins including integrin αIIbβ3, the major glycoprotein receptor for fibrinogen on platelets. Given that CIB1 regulates platelet function through its interaction with αIIbβ3, we developed a fluorescence polarization (FP) assay to screen for potential inhibitors. The assay was miniaturized to 1536-well and screened in quantitative high-throughput screening (qHTS) format against a diverse compound library of 14,782 compounds. After validation and selectivity testing using the FP assay, we identified 19 candidate inhibitors and validated them using an in-gel binding assay that monitors the interaction of CIB1 with αIIb cytoplasmic tail peptide, followed by testing of top hits by intrinsic tryptophan fluorescence (ITF) and microscale thermophoresis (MST) to ascertain their interaction with CIB1. Two of the validated hits shared similar chemical structures, suggesting a common mechanism of action. Docking studies further revealed promising interactions within the hydrophobic binding pocket of the target protein, particularly forming key hydrogen bonds with Ser180. The compounds exhibited a potent antiplatelet activity based on their inhibition of thrombin-induced human platelet aggregation, thus indicating that disruptors of the CIB1- αIIbβ3 interaction could carry a translational potential as antithrombotic agents.

Boosting the Accuracy and Chemical Space Coverage of the Detection of Small Colloidal Aggregating Molecules Using the BAD Molecule Filter

The ability to conduct effective high throughput screening (HTS) campaigns in drug discovery is often hampered by the detection of false positives in these assays due to small colloidally aggregating molecules (SCAMs). SCAMs can produce artifactual hits in HTS by nonspecific inhibition of the protein target. In this work, we present a new computational prediction tool for detecting SCAMs based on their 2D chemical structure. The tool, called the boosted aggregation detection (BAD) molecule filter, employs decision tree ensemble methods, namely, the CatBoost classifier and the light gradient-boosting machine, to significantly improve the detection of SCAMs. In developing the filter, we explore models trained on individual data sets, a consensus approach using these models, and, third, a merged data set approach, each tailored for specific drug discovery needs. The individual data set method emerged as most effective, achieving 93% sensitivity and 90% specificity, outperforming existing state-of-the-art models by 20 and 5%, respectively. The consensus models offer broader chemical space coverage, exceeding 90% for all testing sets. This feature is an important aspect particularly for early stage medicinal chemistry projects, and provides information on applicability domain. Meanwhile, the merged data set models demonstrated robust performance, with a notable sensitivity of 79% in the comprehensive 10-fold cross-validation test set. A SHAP analysis of model features indicates the importance of hydrophobicity and molecular complexity as primary factors influencing the aggregation propensity. The BAD molecule filter is readily accessible for the public usage on https://molmodlab-aau.com/Tools.html. This filter provides a new, more robust tool for aggregate prediction in the early stages of drug discovery to optimize hit rates and reduce associated testing and validation overheads.

Colloidal Aggregation Confounds Cell-Based Covid-19 Antiviral Screens

Colloidal aggregation is one of the largest contributors to false positives in early drug discovery. Here, we consider aggregation's role in cell-based infectivity assays in Covid-19 drug repurposing. We investigated the potential aggregation of 41 drug candidates reported as SARs-CoV-2 entry inhibitors. Of these, 17 formed colloidal particles by dynamic light scattering and exhibited detergent-dependent enzyme inhibition. To evaluate the impact of aggregation on antiviral efficacy in cells, we presaturated the colloidal drug suspensions with BSA or spun them down by centrifugation and measured the effects on spike pseudovirus infectivity. Antiviral potencies diminished by at least 10-fold following both BSA and centrifugation treatments, supporting a colloid-based mechanism. Aggregates induced puncta of the labeled spike protein in fluorescence microscopy, consistent with sequestration of the protein on the colloidal particles. These observations suggest that colloidal aggregation is common among cell-based antiviral drug repurposing and offers rapid counter-screens to detect and eliminate these artifacts.

The Pan-Canadian Chemical Library: A Mechanism to Open Academic Chemistry to High-Throughput Virtual Screening

Computationally screening chemical libraries to discover molecules with desired properties is a common technique used in early-stage drug discovery. Recent progress in the field now enables the efficient exploration of billions of molecules within days or hours, but this exploration remains confined within the boundaries of the accessible chemistry space. While the number of commercially available compounds grows rapidly, it remains a limited subset of all druglike small molecules that could be synthesized. Here, we present a workflow where chemical reactions typically developed in academia and unconventional in drug discovery are exploited to dramatically expand the chemistry space accessible to virtual screening. We use this process to generate a first version of the Pan-Canadian Chemical Library, a collection of nearly 150 billion diverse compounds that does not overlap with other ultra-large libraries such as Enamine REAL or SAVI and could be a resource of choice for protein targets where other libraries have failed to deliver bioactive molecules.

AI is a viable alternative to high throughput screening: a 318-target study

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

The evolution of small molecule enzyme activators

There is a myriad of enzymes within the body responsible for maintaining homeostasis by providing the means to convert substrates to products as and when required. Physiological enzymes are tightly controlled by many signaling pathways and their products subsequently control other pathways. Traditionally, most drug discovery efforts focus on identifying enzyme inhibitors, due to upregulation being prevalent in many diseases and the existence of endogenous substrates that can be modified to afford inhibitor compounds. As enzyme downregulation and reduction of endogenous activators are observed in multiple diseases, the identification of small molecules with the ability to activate enzymes has recently entered the medicinal chemistry toolbox to afford chemical probes and potential therapeutics as an alternative means to intervene in diseases. In this review we highlight the progress made in the identification and advancement of non-kinase enzyme activators and their potential in treating various disease states.

Discovery and structural optimization of a new series of N-acyl-2-aminobenzothiazole as inhibitors of Zika virus

Zika virus infection is associated to severe diseases such as congenital microcephaly and Zika fever causing serious harm to humans and special concern to health systems in low-income countries. Currently, there are no approved drugs against the virus, and the development of anti-Zika virus drugs is thus urgent. The present investigation describes the discovery and hit expansion of a N-acyl-2-aminobenzothiazole series of compounds against Zika virus replication. A structure-activity relationship study was obtained with the synthesis and evaluation of anti-Zika virus activity and cytotoxicity on Vero cells of nineteen derivatives. The three optimized compounds were 2.2-fold more potent than the initial hit and 20.9, 7.7 and 6.4-fold more selective. Subsequent phenotypic and biochemical assays were performed to evidence whether non-structural proteins, such as the complex NS2B-NS3pro, are related to the mechanism of action of the most active compounds.

Colloidal aggregation confounds cell-based Covid-19 antiviral screens

Colloidal aggregation is one of the largest contributors to false-positives in early drug discovery and chemical biology. Much work has focused on its impact on pure-protein screens; here we consider aggregations role in cell-based infectivity assays in Covid-19 drug repurposing. We began by investigating the potential aggregation of 41 drug candidates reported as SARs-CoV-2 entry inhibitors. Of these, 17 formed colloidal-particles by dynamic light scattering and exhibited detergent-dependent enzyme inhibition. To evaluate antiviral efficacy of the drugs in cells we used spike pseudotyped lentivirus and pre-saturation of the colloids with BSA. The antiviral potency of the aggregators was diminished by at least 10-fold and often entirely eliminated in the presence of BSA, suggesting antiviral activity can be attributed to the non-specific nature of the colloids. In confocal microscopy, the aggregates induced fluorescent puncta of labeled spike protein, consistent with sequestration of the protein on the colloidal particles. Addition of either non-ionic detergent or of BSA disrupted these puncta. These observations suggest that colloidal aggregation is common among cell-based anti-viral drug repurposing, and perhaps cell-based assays more broadly, and offers rapid counter-screens to detect and eliminate these artifacts, allowing the community invest resources in compounds with true potential as a Covid-19 therapeutic.

Novel inhibitors that target bacterial virulence identified via HTS against intra-macrophage survival of Shigella flexneri

Shigella flexneri is a facultative intracellular pathogen that causes shigellosis, a human diarrheal disease characterized by the destruction of the colonic epithelium. Novel antimicrobial compounds to treat infections are urgently needed due to the proliferation of bacterial antibiotic resistance and lack of new effective antimicrobials in the market. Our approach to find compounds that block the Shigella virulence pathway has three potential advantages: (i) resistance development should be minimized due to the lack of growth selection pressure, (ii) no resistance due to environmental antibiotic exposure should be developed since the virulence pathways are not activated outside of host infection, and (iii) the normal intestinal microbiota, which do not have the targeted virulence pathways, should be unharmed. We chose to utilize two phenotypic assays, inhibition of Shigella survival in macrophages and Shigella growth inhibition (minimum inhibitory concentration), to interrogate the 1.7 M compound screening collection subset of the GlaxoSmithKline drug discovery chemical library. A number of secondary assays on the hit compounds resulting from the primary screens were conducted, which, in combination with chemical, structural, and physical property analyses, narrowed the final hit list to 44 promising compounds for further drug discovery efforts. The rapid development of antibiotic resistance is a critical problem that has the potential of returning the world to a "pre-antibiotic" type of environment, where millions of people will die from previously treatable infections. One relatively newer approach to minimize the selection pressures for the development of resistance is to target virulence pathways. This is anticipated to eliminate any resistance selection pressure in environmental exposure to virulence-targeted antibiotics and will have the added benefit of not affecting the non-virulent microbiome. This paper describes the development and application of a simple, reproducible, and sensitive assay to interrogate an extensive chemical library in high-throughput screening format for activity against the survival of Shigella flexneri 2457T-nl in THP-1 macrophages. The ability to screen very large numbers of compounds in a reasonable time frame (~1.7 M compounds in ~8 months) distinguishes this assay as a powerful tool in further exploring new compounds with intracellular effect on S. flexneri or other pathogens with similar pathways of pathogenesis. The assay utilizes a luciferase reporter which is extremely rapid, simple, relatively inexpensive, and sensitive and possesses a broad linear range. The assay also utilized THP-1 cells that resemble primary monocytes and macrophages in morphology and differentiation properties. THP-1 cells have advantages over human primary monocytes or macrophages because they are highly plastic and their homogeneous genetic background minimizes the degree of variability in the cell phenotype (1). The intracellular and virulence-targeted selectivity of our methodology, determined via secondary screening, is an enormous advantage. Our main interest focuses on hits that are targeting virulence, and the most promising compounds with adequate physicochemical and drug metabolism and pharmacokinetic (DMPK) properties will be progressed to a suitable in vivo shigellosis model to evaluate the therapeutic potential of this approach. Additionally, compounds that act via a host-directed mechanism could be a promising source for further research given that it would allow a whole new, specific, and controlled approach to the treatment of diseases caused by some pathogenic bacteria.

Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds

Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with assay detection technology. In response, we generated the largest publicly available library of chemical liabilities and developed "Liability Predictor," a free web tool to predict HTS artifacts. More specifically, we generated, curated, and integrated HTS data sets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity and developed and validated quantitative structure-interference relationship (QSIR) models to predict these nuisance behaviors. The resulting models showed 58-78% external balanced accuracy for 256 external compounds per assay. QSIR models developed and validated herein identify nuisance compounds among experimental hits more reliably than do popular PAINS filters. Both the models and the curated data sets were implemented in "Liability Predictor," publicly available at https://liability.mml.unc.edu/. "Liability Predictor" may be used as part of chemical library design or for triaging HTS hits.

Evaluating Known Zika Virus NS2B-NS3 Protease Inhibitor Scaffolds via In Silico Screening and Biochemical Assays

The NS2B-NS3 protease (NS2B-NS3pro) is regarded as an interesting molecular target for drug design, discovery, and development because of its essential role in the Zika virus (ZIKV) cycle. Although no NS2B-NS3pro inhibitors have reached clinical trials, the employment of drug-like scaffolds can facilitate the screening process for new compounds. In this study, we performed a combination of ligand-based and structure-based in silico methods targeting two known non-peptide small-molecule scaffolds with micromolar inhibitory activity against ZIKV NS2B-NS3pro by a virtual screening (VS) of promising compounds. Based on these two scaffolds, we selected 13 compounds from an initial library of 509 compounds from ZINC15's similarity search. These compounds exhibited structural modifications that are distinct from previously known compounds yet keep pertinent features for binding. Despite promising outcomes from molecular docking and initial enzymatic assays against NS2B-NS3pro, confirmatory assays with a counter-screening enzyme revealed an artifactual inhibition of the assessed compounds. However, we report two compounds, 9 and 11, that exhibited antiviral properties at a concentration of 50 μM in cellular-based assays. Overall, this study provides valuable insights into the ongoing research on anti-ZIKV compounds to facilitate and improve the development of new inhibitors.

Next Generation of Fluorometric Protease Assays: 7-Nitrobenz-2-oxa-1,3-diazol-4-yl-amides (NBD-Amides) as Class-Spanning Protease Substrates

Fluorometric assays are one of the most frequently used methods in medicinal chemistry. Over the last 50 years, the reporter molecules for the detection of protease activity have evolved from first-generation colorimetric p-nitroanilides, through FRET substrates, and 7-amino-4-methyl coumarin (AMC)-based substrates. The aim of further substrate development is to increase sensitivity and reduce vulnerability to assay interferences. Herein, we describe a new generation of substrates for protease assays based on 7-nitrobenz-2-oxa-1,3-diazol-4-yl-amides (NBD-amides). In this study, we synthesized and tested substrates for 10 different proteases from the serine-, cysteine-, and metalloprotease classes. Enzyme- and substrate-specific parameters as well as the inhibitory activity of literature-known inhibitors confirmed their suitability for application in fluorometric assays. Hence, we were able to present NBD-based alternatives for common protease substrates. In conclusion, these NBD substrates are not only less susceptible to common assay interference, but they are also able to replace FRET-based substrates with the requirement of a prime site amino acid residue.

Structure-based discovery of novel cruzain inhibitors with distinct trypanocidal activity profiles

Over 110 years after the first formal description of Chagas disease, the trypanocidal drugs thus far available have limited efficacy and several side effects. This encourages the search for novel treatments that inhibit T. cruzi targets. One of the most studied anti-T. cruzi targets is the cysteine protease cruzain; it is associated with metacyclogenesis, replication, and invasion of the host cells. We used computational techniques to identify novel molecular scaffolds that act as cruzain inhibitors. First, with a docking-based virtual screening, we identified compound 8, a competitive cruzain inhibitor with a K_i of 4.6 μM. Then, aided by molecular dynamics simulations, cheminformatics, and docking, we identified the analog compound 22 with a K_i of 27 μM. Surprisingly, despite sharing the same isoquinoline scaffold, compound 8 presented higher trypanocidal activity against the epimastigote forms, while compound 22, against the trypomastigotes and amastigotes. Taken together, compounds 8 and 22 represent a promising scaffold for further development of trypanocidal compounds as drug candidates for treating Chagas disease.

Dissecting the structural heterogeneity of proteins by native mass spectrometry

A single gene yields many forms of proteins via combinations of posttranscriptional/posttranslational modifications. Proteins also fold into higher-order structures and interact with other molecules. The combined molecular diversity leads to the heterogeneity of proteins that manifests as distinct phenotypes. Structural biology has generated vast amounts of data, effectively enabling accurate structural prediction by computational methods. However, structures are often obtained heterologously under homogeneous states in vitro. The lack of native heterogeneity under cellular context creates challenges in precisely connecting the structural data to phenotypes. Mass spectrometry (MS) based proteomics methods can profile proteome composition of complex biological samples. Most MS methods follow the "bottom-up" approach, which denatures and digests proteins into short peptide fragments for ease of detection. Coupled with chemical biology approaches, higher-order structures can be probed via incorporation of covalent labels on native proteins that are maintained at the peptide level. Alternatively, native MS follows the "top-down" approach and directly analyzes intact proteins under nondenaturing conditions. Various tandem MS activation methods can dissect the intact proteins for in-depth structural elucidation. Herein, we review recent native MS applications for characterizing heterogeneous samples, including proteins binding to mixtures of ligands, homo/hetero-complexes with varying stoichiometry, intrinsically disordered proteins with dynamic conformations, glycoprotein complexes with mixed modification states, and active membrane protein complexes in near-native membrane environments. We summarize the benefits, challenges, and ongoing developments in native MS, with the hope to demonstrate an emerging technology that complements other tools by filling the knowledge gaps in understanding the molecular heterogeneity of proteins.

Screening the Pathogen Box to Discover and Characterize New Cruzain and TbrCatL Inhibitors

Chagas disease and Human African Trypanosomiasis, caused by Trypanosoma cruzi and T. brucei, respectively, pose relevant health challenges throughout the world, placing 65 to 70 million people at risk each. Given the limited efficacy and severe side effects associated with current chemotherapy, new drugs are urgently needed for both diseases. Here, we report the screening of the Pathogen Box collection against cruzain and TbrCatL, validated targets for Chagas disease and Human African Trypanosomiasis, respectively. Enzymatic assays were applied to screen 400 compounds, validate hits, determine IC₅₀ values and, when possible, mechanisms of inhibition. In this case, 12 initial hits were obtained and ten were prioritized for follow-up. IC₅₀ values were obtained for six of them (hit rate = 1.5%) and ranged from 0.46 ± 0.03 to 27 ± 3 µM. MMV687246 was found to be a mixed inhibitor of cruzain (K_i = 57 ± 6 µM) while MMV688179 was found to be a competitive inhibitor of cruzain with a nanomolar potency (K_i = 165 ± 63 nM). A putative binding mode for MMV688179 was obtained by docking. The six hits discovered against cruzain and TbrCatL are of great interest for further optimization by the medicinal chemistry community.

Enzyme Inhibitors from Gorgonians and Soft Corals

For decades, gorgonians and soft corals have been considered promising sources of bioactive compounds, attracting the interest of scientists from different fields. As the most abundant bioactive compounds within these organisms, terpenoids, steroids, and alkaloids have received the highest coverage in the scientific literature. However, enzyme inhibitors, a functional class of bioactive compounds with high potential for industry and biomedicine, have received much less notoriety. Thus, we revised scientific literature (1974-2022) on the field of marine natural products searching for enzyme inhibitors isolated from these taxonomic groups. In this review, we present representative enzyme inhibitors from an enzymological perspective, highlighting, when available, data on specific targets, structures, potencies, mechanisms of inhibition, and physiological roles for these molecules. As most of the characterization studies for the new inhibitors remain incomplete, we also included a methodological section presenting a general strategy to face this goal by accomplishing STRENDA (Standards for Reporting Enzymology Data) project guidelines.

Structure-Based Identification of Naphthoquinones and Derivatives as Novel Inhibitors of Main Protease Mpro and Papain-like Protease PLpro of SARS-CoV-2

The worldwide COVID-19 pandemic caused by the coronavirus SARS-CoV-2 urgently demands novel direct antiviral treatments. The main protease (Mpro) and papain-like protease (PLpro) are attractive drug targets among coronaviruses due to their essential role in processing the polyproteins translated from the viral RNA. In this study, we virtually screened 688 naphthoquinoidal compounds and derivatives against Mpro of SARS-CoV-2. Twenty-four derivatives were selected and evaluated in biochemical assays against Mpro using a novel fluorogenic substrate. In parallel, these compounds were also assayed with SARS-CoV-2 PLpro. Four compounds inhibited Mpro with half-maximal inhibitory concentration (IC50) values between 0.41 μM and 9.0 μM. In addition, three compounds inhibited PLpro with IC50 ranging from 1.9 μM to 3.3 μM. To verify the specificity of Mpro and PLpro inhibitors, our experiments included an assessment of common causes of false positives such as aggregation, high compound fluorescence, and inhibition by enzyme oxidation. Altogether, we confirmed novel classes of specific Mpro and PLpro inhibitors. Molecular dynamics simulations suggest stable binding modes for Mpro inhibitors with frequent interactions with residues in the S1 and S2 pockets of the active site. For two PLpro inhibitors, interactions occur in the S3 and S4 pockets. In summary, our structure-based computational and biochemical approach identified novel naphthoquinonal scaffolds that can be further explored as SARS-CoV-2 antivirals.

An open-source molecular builder and free energy preparation workflow

Automated free energy calculations for the prediction of binding free energies of congeneric series of ligands to a protein target are growing in popularity, but building reliable initial binding poses for the ligands is challenging. Here, we introduce the open-source FEgrow workflow for building user-defined congeneric series of ligands in protein binding pockets for input to free energy calculations. For a given ligand core and receptor structure, FEgrow enumerates and optimises the bioactive conformations of the grown functional group(s), making use of hybrid machine learning/molecular mechanics potential energy functions where possible. Low energy structures are optionally scored using the gnina convolutional neural network scoring function, and output for more rigorous protein-ligand binding free energy predictions. We illustrate use of the workflow by building and scoring binding poses for ten congeneric series of ligands bound to targets from a standard, high quality dataset of protein-ligand complexes. Furthermore, we build a set of 13 inhibitors of the SARS-CoV-2 main protease from the literature, and use free energy calculations to retrospectively compute their relative binding free energies. FEgrow is freely available at https://github.com/cole-group/FEgrow, along with a tutorial.

Understanding of black salve toxicity by multi-compound cytotoxicity assays

Background

Black salve is a controversial complementary and alternative medicine (CAM) associated with skin toxicity and skin cancer treatment failures. Black salve formulations vary between manufacturers and contain a number of botanical and synthetic constituents. The skin cancer cytotoxicity of a number of these constituents has not been assessed to date. The alkaloids from the rhizomes of Sanguinaria canadensis, a key black salve ingredient, have had their single compound cytotoxicity assessed; however, whether they possess synergistic cytotoxicity with other compounds has not been studied and is of direct clinical relevance. This research aimed to improve our understanding of the skin cancer cytotoxicity of black salve constituents.

Methods

The cytotoxicity of individual and combination black salve constituents were assessed against the A375 melanoma and A431 squamous cell carcinoma cell lines. Cytotoxicity was determined using the Resazurin assay with fluorescence measured using a Tecan Infinite 200 Pro Microplate reader, compound cytotoxicity being compared to that of the topical cancer therapeutic agent, 5- fluouracil. Docetaxal was used as a positive control. Dunnetts p value was used to determine whether significant synergistic cytotoxicity was present.

Results

Sanguinarine was the most cytotoxic compound tested with a 24-hour IC50 of 2.1 μM against the A375 Melanoma cell line and 3.14 μM against the A431 SCC cell line. All black salve constituents showed greater cytotoxicity against the two skin cancer cell lines tested than the skin cancer therapeutic 5-Fluouracil with 24 hours of compound exposure. Chelerythrine and minor Quaternary Benzophenanthridine Alkaloids (QBAs) present in black salve, at concentrations not having a cytotoxic effect by themselves, boosted the cytotoxic effects of sanguinarine. This could be a synergistic rather than additive cytotoxic effect although the synergistic effect was cell line and concentration dependent.

Conclusions

Black salve contains several cytotoxic compounds, a number of which have been found to possess synergistic cytotoxicity for the first time against skin cancer cell lines. In addition, these compounds together increase the overall cytotoxic effect. Assessing multi-compound cytotoxicity in herbal medicine can provide additional information about both their therapeutic and toxicity potential. As black salve is currently being used by patients, further cytotoxicity work should be undertaken to assess whether synergistic cytotoxicity exists when tested in normal skin cells.

Mass Spectrometric Assays Reveal Discrepancies in Inhibition Profiles for the SARS-CoV-2 Papain-Like Protease

The two SARS-CoV-2 proteases, i. e. the main protease (Mpro ) and the papain-like protease (PLpro ), which hydrolyze the viral polypeptide chain giving functional non-structural proteins, are essential for viral replication and are medicinal chemistry targets. We report a high-throughput mass spectrometry (MS)-based assay which directly monitors PLpro catalysis in vitro. The assay was applied to investigate the effect of reported small-molecule PLpro inhibitors and selected Mpro inhibitors on PLpro catalysis. The results reveal that some, but not all, PLpro inhibitor potencies differ substantially from those obtained using fluorescence-based assays. Some substrate-competing Mpro inhibitors, notably PF-07321332 (nirmatrelvir) which is in clinical development, do not inhibit PLpro . Less selective Mpro inhibitors, e. g. auranofin, inhibit PLpro , highlighting the potential for dual PLpro /Mpro inhibition. MS-based PLpro assays, which are orthogonal to widely employed fluorescence-based assays, are of utility in validating inhibitor potencies, especially for inhibitors operating by non-covalent mechanisms.

Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets

The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ∼40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ∼3.7 billion candidate molecules.

Isometric Stratified Ensembles: A Partial and Incremental Adaptive Applicability Domain and Consensus-Based Classification Strategy for Highly Imbalanced Data Sets with Application to Colloidal Aggregation

Partial and incremental stratification analysis of a quantitative structure-interference relationship (QSIR) is a novel strategy intended to categorize classification provided by machine learning techniques. It is based on a 2D mapping of classification statistics onto two categorical axes: the degree of consensus and level of applicability domain. An internal cross-validation set allows to determine the statistical performance of the ensemble at every 2D map stratum and hence to define isometric local performance regions with the aim of better hit ranking and selection. During training, isometric stratified ensembles (ISE) applies a recursive decorrelated variable selection and considers the cardinal ratio of classes to balance training sets and thus avoid bias due to possible class imbalance. To exemplify the interest of this strategy, three different highly imbalanced PubChem pairs of AmpC β-lactamase and cruzain inhibition assay campaigns of colloidal aggregators and complementary aggregators data set available at the AGGREGATOR ADVISOR predictor web page were employed. Statistics obtained using this new strategy show outperforming results compared to former published tools, with and without a classical applicability domain. ISE performance on classifying colloidal aggregators shows from a global AUC of 0.82, when the whole test data set is considered, up to a maximum AUC of 0.88, when its highest confidence isometric stratum is retained.

Structure-based identification of naphthoquinones and derivatives as novel inhibitors of main protease Mpro and papain-like protease PLpro of SARS-CoV-2

The worldwide COVID-19 pandemic caused by the coronavirus SARS-CoV-2 urgently demands novel direct antiviral treatments. The main protease (Mpro) and papain-like protease (PLpro) are attractive drug targets among coronaviruses due to their essential role in processing the polyproteins translated from the viral RNA. In the present work, we virtually screened 688 naphthoquinoidal compounds and derivatives against Mpro of SARS-CoV-2. Twenty-four derivatives were selected and evaluated in biochemical assays against Mpro using a novel fluorogenic substrate. In parallel, these compounds were also assayed with SARS-CoV-2 PLpro. Four compounds inhibited Mpro with half-maximal inhibitory concentration (IC ₅₀ ) values between 0.41 µM and 66 µM. In addition, eight compounds inhibited PLpro with IC ₅₀ ranging from 1.7 µM to 46 µM. Molecular dynamics simulations suggest stable binding modes for Mpro inhibitors with frequent interactions with residues in the S1 and S2 pockets of the active site. For two PLpro inhibitors, interactions occur in the S3 and S4 pockets. In summary, our structure-based computational and biochemical approach identified novel naphthoquinonal scaffolds that can be further explored as SARS-CoV-2 antivirals.

Tetracycline Derivatives Inhibit Plasmodial Cysteine Protease Falcipain-2 through Binding to a Distal Allosteric Site

Allosteric inhibitors regulate enzyme activity from remote and usually specific pockets. As they promise an avenue for less toxic and safer drugs, the identification and characterization of allosteric inhibitors has gained great academic and biomedical interest in recent years. Research on falcipain-2 (FP-2), the major papain-like cysteine hemoglobinase of Plasmodium falciparum, might benefit from this strategy to overcome the low selectivity against human cathepsins shown by active site-directed inhibitors. Encouraged by our previous finding that methacycline inhibits FP-2 noncompetitively, here we assessed other five tetracycline derivatives against this target and characterized their inhibition mechanism. As previously shown for methacycline, tetracycline derivatives inhibited FP-2 in a noncompetitive fashion, with K_i values ranging from 121 to 190 μM. A possible binding to the S' side of the FP-2 active site, similar to that described by X-ray crystallography (PDB: 6SSZ) for the noncompetitive inhibitor E-chalcone 48 (EC48), was experimentally discarded by kinetic analysis using a large peptidyl substrate spanning the whole active site. By combining lengthy molecular dynamics (MD) simulations that allowed methacycline to diffuse from solution to different FP-2 surface regions and free energy calculations, we predicted the most likely binding mode of the ligand. Of note, the proposed binding pose explains the low differences in K_i values observed for the tested tetracycline derivatives and the calculated binding free energies match the experimental values. Overall, this study has implications for the design of novel allosteric inhibitors against FP-2 and sets the basis for further optimization of the tetracycline scaffold to produce more potent and selective inhibitors.

Updated Prediction of Aggregators and Assay-Interfering Substructures in Food Compounds

Positive outcomes in biochemical and biological assays of food compounds may appear due to the well-described capacity of some compounds to form colloidal aggregates that adsorb proteins, resulting in their denaturation and loss of function. This phenomenon can lead to wrongly ascribing mechanisms of biological action for these compounds (false positives) as the effect is nonspecific and promiscuous. Similar false positives can show up due to chemical (photo)reactivity, redox cycling, metal chelation, interferences with the assay technology, membrane disruption, etc., which are more frequently observed when the tested molecule has some definite interfering substructures. Although discarding false positives can be achieved experimentally, it would be very useful to have in advance a prognostic value for possible aggregation and/or interference based only in the chemical structure of the compound tested in order to be aware of possible issues, help in prioritization of compounds to test, design of appropriate assays, etc. Previously, we applied cheminformatic tools derived from the drug discovery field to identify putative aggregators and interfering substructures in a database of food compounds, the FooDB, comprising 26,457 molecules at that time. Here, we provide an updated account of that analysis based on a current, much-expanded version of the FooDB, comprising a total of 70,855 compounds. In addition, we also apply a novel machine learning model (SCAM Detective) to predict aggregators with 46-53% increased accuracies over previous models. In this way, we expect to provide the researchers in the mode of action of food compounds with a much improved, robust, and widened set of putative aggregators and interfering substructures of food compounds.

Colloidal Aggregators in Biochemical SARS-CoV-2 Repurposing Screens

To fight COVID-19, much effort has been directed toward in vitro drug repurposing. Here, we investigate the impact of colloidal aggregation, a common screening artifact, in these repurposing campaigns. We tested 56 drugs reported as active in biochemical assays for aggregation by dynamic light scattering and by detergent-based enzyme counter screening; 19 formed colloids at concentrations similar to their literature IC50's, and another 14 were problematic. From a common repurposing library, we further selected another 15 drugs that had physical properties resembling known aggregators, finding that six aggregated at micromolar concentrations. This study suggests not only that many of the drugs repurposed for SARS-CoV-2 in biochemical assays are artifacts but that, more generally, at screening-relevant concentrations, even drugs can act artifactually via colloidal aggregation. Rapid detection of these artifacts will allow the community to focus on those molecules that genuinely have potential for treating COVID-19.

Fragment Hit Discovery and Binding Site Characterization by Indirect Affinity Capillary Electrophoresis: Application to Factor XIIa

Fragment-based lead discovery is a usual strategy in drug discovery to identify innovative lead compounds. The success of this approach strongly relies on the capacity to detect weak binders and characterize their binding site. NMR and X-ray crystallography are the conventional technologies used to tackle this challenge. However, their large protein consumption and the cost of equipment reduce their accessibility. Here, an affinity capillary electrophoresis methodology was developed that enables the detection of mM binders, the determination of dissociation constants, and the characterization of the fragment binding site. On the basis of multiple equilibrium theory, dissociation constants in the μM-mM range were determined, and a new methodology is proposed to establish graphically if two fragments bind the same protein pocket. The applicability of this methodology was demonstrated experimentally on coagulation factor XIIa by evaluating pairs of fragments with expected behavior. This study reinforces the significance of using affinity capillary electrophoresis to gather valuable information for medicinal chemistry projects.

A practical guide to large-scale docking

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

Structure-Based Optimization of Quinazolines as Cruzain and TbrCATL Inhibitors

The cysteine proteases, cruzain and TbrCATL (rhodesain), are therapeutic targets for Chagas disease and Human African Trypanosomiasis, respectively. Among the known inhibitors for these proteases, we have described N⁴-benzyl-N²-phenylquinazoline-2,4-diamine (compound 7 in the original publication, 1a in this study), as a competitive cruzain inhibitor (K_i = 1.4 μM). Here, we describe the synthesis and biological evaluation of 22 analogs of 1a, containing modifications in the quinazoline core, and in the substituents in positions 2 and 4 of this ring. The analogs demonstrate low micromolar inhibition of the target proteases and cidal activity against Trypanosoma cruzi with up to two log selectivity indices in counterscreens with myoblasts. Fourteen compounds were active against Trypanosoma brucei at low to mid micromolar concentrations. During the optimization of 1a, structure-based design and prediction of physicochemical properties were employed to maintain potency against the enzymes while removing colloidal aggregator characteristics observed for some molecules in this series.

Colloidal aggregators in biochemical SARS-CoV-2 repurposing screens

To fight the SARS-CoV-2 pandemic, much effort has been directed toward drug repurposing, testing investigational and approved drugs against several viral or human proteins in vitro . Here we investigate the impact of colloidal aggregation, a common artifact in early drug discovery, in these repurposing screens. We selected 56 drugs reported to be active in biochemical assays and tested them for aggregation by both dynamic light scattering and by enzyme counter screening with and without detergent; seventeen of these drugs formed colloids at concentrations similar to their literature reported IC 50 s. To investigate the occurrence of colloidal aggregators more generally in repurposing libraries, we further selected 15 drugs that had physical properties resembling known aggregators from a common repurposing library, and found that 6 of these aggregated at micromolar concentrations. An attraction of repurposing is that drugs active on one target are considered de-risked on another. This study suggests not only that many of the drugs repurposed for SARS-CoV-2 in biochemical assays are artifacts, but that, more generally, when screened at relevant concentrations, drugs can act artifactually via colloidal aggregation. Understanding the role of aggregation, and detecting its effects rapidly, will allow the community to focus on those drugs and leads that genuinely have potential for treating COVID-19.

ABSTRACT FIGURE

Preparing (Metalla)carboranes for Nanomedicine

"There's plenty of room at the bottom" (Richard Feynman, 1959): an invitation for (metalla)carboranes to enter the (new) field of nanomedicine. For two decades, the number of publications on boron cluster compounds designed for potential applications in medicine has been constantly increasing. Hundreds of compounds have been screened in vitro or in vivo for a variety of biological activities (chemotherapeutics, radiotherapeutics, antiviral, etc.), and some have shown rather promising potential for further development. However, until now, no boron cluster compounds have made it to the clinic, and even clinical trials have been very sparse. This review introduces a new perspective in the field of medicinal boron chemistry, namely that boron-based drugs should be regarded as nanomedicine platforms, due to their peculiar self-assembly behaviour in aqueous solutions, and treated as such. Examples for boron-based 12- and 11-vertex clusters and appropriate comparative studies from medicinal (in)organic chemistry and nanomedicine, highlighting similarities, differences and gaps in physicochemical and biological characterisation methods, are provided to encourage medicinal boron chemists to fill in the gaps between chemistry laboratory and real applications in living systems by employing bioanalytical and biophysical methods for characterising and controlling the aggregation behaviour of the clusters in solution.

Capillary electrophoresis as a fragment screening tool to cross-validate hits from chromogenic assay: Application to FXIIa

In this study, a partial-filling affinity capillary electrophoresis (pf-ACE) method was developed for the cross-validation of fragment hits revealed by chromogenic factor XIIa (FXIIa) assay. Chromogenic assay produces false positives, mainly due to spectrophotometric interferences and sample purity issues. pf-ACE was selected as counter-screening technology because of its separative character and the fact that the target does not have to be attached or tagged. The effects of protein plug length, applied voltage and composition of the running buffer were examined and optimized. Detection limit in terms of dissociation constant was estimated at 400 μM. The affinity evaluation was performed close to physiological conditions (pH 7.4, ionic strength 0.13 mol L^-1) in a poly (ethylene oxide)-coated capillary of 75 μm internal diameter x 33 cm length with an applied voltage of 3 kV. This method uncovered chromogenic assay's false positives due to zinc contamination. Moreover, pf-ACE supported the evaluation of compounds absorbing at 405 nm.

Detection of Small-Molecule Aggregation with High-Throughput Microplate Biophysical Methods

Small-molecule drug discovery can be hindered by the formation of aggregates that act as non-selective inhibitors of drug targets. Such aggregates appear as false positives in high-throughput screening campaigns and can bedevil structure-activity relationships during compound optimization. Protocols are described for resonant waveguide grating (RWG) and dynamic light scattering (DLS) as microplate-based high-throughput approaches to identify compound aggregation. Resonant waveguide grating and dynamic light scattering give equivalent results for the compound test set, as assessed with Bland-Altman analysis. © 2019 The Authors. Basic Protocol 1: Resonant waveguide grating (RWG) in 384-well or 1536-well plate format to detect compound aggregation Basic Protocol 2: Dynamic light scattering (DLS) in 384-well plate format to detect compound aggregation.

A Crowding Barrier to Protein Inhibition in Colloidal Aggregates

Small molecule colloidal aggregates adsorb and partially denature proteins, inhibiting them artifactually. Oddly, this inhibition is typically time-dependent. Two mechanisms might explain this: low concentrations of the colloid and enzyme might mean low encounter rates, or colloid-based protein denaturation might impose a kinetic barrier. These two mechanisms should have different concentration dependencies. Perplexingly, when enzyme concentration was increased, incubation times actually lengthened, inconsistent with both models and with classical chemical kinetics of solution species. We therefore considered molecular crowding, where colloids with lower protein surface density demand a shorter incubation time than more crowded colloids. To test this, we grew and shrank colloid surface area. As the surface area shrank, the incubation time lengthened, while as it increased, the converse was true. These observations support a crowding effect on protein binding to colloidal aggregates. Implications for drug delivery and for detecting aggregation-based inhibition will be discussed.

Predictive Global Models of Cruzain Inhibitors with Large Chemical Coverage

Chagas disease affects 8-11 million people worldwide, most of them living in Latin America. Moreover, migratory phenomena have spread the infection beyond endemic areas. Efforts for the development of new pharmacological therapies are paramount as the pharmacological profile of the two marketed drugs currently available, nifurtimox and benznidazole, needs to be improved. Cruzain, a parasitic cysteine protease, is one of the most attractive biological targets due to its roles in parasite survival and immune evasion. In this work, we compiled and curated a database of diverse cruzain inhibitors previously reported in the literature. From this data set, quantitative structure-activity relationship (QSAR) models for the prediction of their pIC50 values were generated using k-nearest neighbors and random forest algorithms. Local and global models were calculated and compared. The statistical parameters for internal and external validation indicate a significant predictability, with q loo 2 values around 0.66 and 0.61 and external R 2 coefficients of 0.725 and 0.766. The applicability domain is quantitatively defined, according to QSAR good practices, using the leverage and similarity methods. The models described in this work are readily available in a Python script for the discovery of novel cruzain inhibitors.

Screening and Identification of Metacaspase Inhibitors: Evaluation of Inhibition Mechanism and Trypanocidal Activity

A common strategy to identify new antiparasitic agents is the targeting of proteases, due to their essential contributions to parasite growth and development. Metacaspases (MCAs) are cysteine proteases present in fungi, protozoa, and plants. These enzymes, which are associated with crucial cellular events in trypanosomes, are absent in the human host, thus arising as attractive drug targets. To find new MCA inhibitors with trypanocidal activity, we adapted a continuous fluorescence enzymatic assay to a medium-throughput format and carried out screening of different compound collections, followed by the construction of dose-response curves for the most promising hits. We used MCA5 from Trypanosoma brucei (TbMCA5) as a model for the identification of inhibitors from the GlaxoSmithKline HAT and CHAGAS chemical boxes. We also assessed a third collection of nine compounds from the Maybridge database that had been identified by virtual screening as potential inhibitors of the cysteine peptidase falcipain-2 (clan CA) from Plasmodium falciparum Compound HTS01959 (from the Maybridge collection) was the most potent inhibitor, with a 50% inhibitory concentration (IC₅₀) of 14.39 µM; it also inhibited other MCAs from T. brucei and Trypanosoma cruzi (TbMCA2, 4.14 µM; TbMCA3, 5.04 µM; TcMCA5, 151 µM). HTS01959 behaved as a reversible, slow-binding, and noncompetitive inhibitor of TbMCA2, with a mechanism of action that included redox components. Importantly, HTS01959 displayed trypanocidal activity against bloodstream forms of T. brucei and trypomastigote forms of T. cruzi, without cytotoxic effects on Vero cells. Thus, HTS01959 is a promising starting point to develop more specific and potent chemical structures to target MCAs.

High-throughput screening to discover inhibitors of the CarD·RNA polymerase protein-protein interaction in Mycobacterium tuberculosis

Multidrug-resistant Mycobacterium tuberculosis (MDR-TB) accounts for 3.7% of new cases of TB annually worldwide and is a major threat to global public health. Due to the prevalence of the MDR-TB and extensively drug resistant tuberculosis (XDR-TB) cases, there is an urgent need for new drugs with novel mechanisms of action. CarD, a global transcription regulator in MTB, binds RNAP and activates transcription by stabilizing the transcription initiation open-promoter complex (RPo). CarD is required for MTB viability and it has highly conserved homologues in many eubacteria. A fluorescence polarization (FP) assay which monitors the association of MTB RNAP, native rRNA promoter DNA and CarD has been developed. Overall, our objective is to identify and characterize small molecule inhibitors which block the CarD/RNAP interaction and to understand the mechanisms by which CarD interacts with the molecules. We expect that the development of a new and improved anti-TB compound with a novel mechanism of action will relieve the burden of resistance. This CarD FP assay is amenable to HTS and is an enabling tool for future novel therapeutic discovery.

ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds

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/.

Exploring new targets and chemical space with affinity selection-mass spectrometry

Affinity selection-mass spectrometry (AS-MS) is a high-throughput screening (HTS) technique for drug discovery that enables rapid screening of large collections of compounds to identify ligands for a specific biomolecular target. AS-MS is a binding assay that is insensitive to the functional effects a ligand might have, which is important because it lets us identify novel ligands irrespective of their binding site. This approach is gaining popularity, notably due to its role in the emergence of useful agents for targeted protein degradation. This Perspective highlights the use of AS-MS techniques to explore broad chemical space and identify small-molecule ligands for biological targets that have proven challenging to address with other screening paradigms. We present chemical structures of reported AS-MS hits to illustrate the potential of this screening approach to deliver high-quality hits for further optimization. AS-MS has, thus, evolved from being an infrequent alternative to traditional HTS or DNA-encoded library strategies to now firmly establishing itself as a HTS approach for drug discovery.

De-risking Drug Discovery of Intracellular Targeting Peptides: Screening Strategies to Eliminate False-Positive Hits

Nonspecific promiscuous compounds can mislead researchers and waste significant resources. This phenomenon, though well-documented for small molecules, has not been widely explored for the peptide modality. Here we demonstrate that two purported peptide-based KRas inhibitors, SAH-SOS1 _A and cyclorasin 9A5, exemplify false-positive molecules-in terms of both their binding affinities and cellular activities. Through multiple gold-standard biophysical techniques, we unambiguously show that both peptides lack specific binding to KRas and instead induce protein unfolding. Although these peptides inhibited cellular proliferation, the activities appeared to be off-target on the basis of a counterscreen with KRas-independent cell lines. We further demonstrate that their cellular activities are derived from membrane disruption. Accordingly, we propose that to de-risk false-positive molecules, orthogonal binding assays and cellular counterscreens are indispensable.

SCAM Detective: Accurate Predictor of Small, Colloidally Aggregating Molecules

Small, colloidally aggregating molecules (SCAMs) are the most common source of false positives in high-throughput screening (HTS) campaigns. Although SCAMs can be experimentally detected and suppressed by the addition of detergent in the assay buffer, detergent sensitivity is not routinely monitored in HTS. Computational methods are thus needed to flag potential SCAMs during HTS triage. In this study, we have developed and rigorously validated quantitative structure-interference relationship (QSIR) models of detergent-sensitive aggregation in several HTS campaigns under various assay conditions and screening concentrations. In particular, we have modeled detergent-sensitive aggregation in an AmpC β-lactamase assay, the preferred HTS counter-screen for aggregation, as well as in another assay that measures cruzain inhibition. Our models increase the accuracy of aggregation prediction by ∼53% in the β-lactamase assay and by ∼46% in the cruzain assay compared to previously published methods. We also discuss the importance of both assay conditions and screening concentrations in the development of QSIR models for various interference mechanisms besides aggregation. The models developed in this study are publicly available for fast prediction within the SCAM detective web application (https://scamdetective.mml.unc.edu/).

SyntaLinker: automatic fragment linking with deep conditional transformer neural networks

Linking fragments to generate a focused compound library for a specific drug target is one of the challenges in fragment-based drug design (FBDD). Hereby, we propose a new program named SyntaLinker, which is based on a syntactic pattern recognition approach using deep conditional transformer neural networks. This state-of-the-art transformer can link molecular fragments automatically by learning from the knowledge of structures in medicinal chemistry databases (e.g. ChEMBL database). Conventionally, linking molecular fragments was viewed as connecting substructures that were predefined by empirical rules. In SyntaLinker, however, the rules of linking fragments can be learned implicitly from known chemical structures by recognizing syntactic patterns embedded in SMILES notations. With deep conditional transformer neural networks, SyntaLinker can generate molecular structures based on a given pair of fragments and additional restrictions. Case studies have demonstrated the advantages and usefulness of SyntaLinker in FBDD.

AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization

We here present AutoGrow4, an open-source program for semi-automated computer-aided drug discovery. AutoGrow4 uses a genetic algorithm to evolve predicted ligands on demand and so is not limited to a virtual library of pre-enumerated compounds. It is a useful tool for generating entirely novel drug-like molecules and for optimizing preexisting ligands. By leveraging recent computational and cheminformatics advancements, AutoGrow4 is faster, more stable, and more modular than previous versions. It implements new docking-program compatibility, chemical filters, multithreading options, and selection methods to support a wide range of user needs. To illustrate both de novo design and lead optimization, we here apply AutoGrow4 to the catalytic domain of poly(ADP-ribose) polymerase 1 (PARP-1), a well characterized DNA-damage-recognition protein. AutoGrow4 produces drug-like compounds with better predicted binding affinities than FDA-approved PARP-1 inhibitors (positive controls). The predicted binding modes of the AutoGrow4 compounds mimic those of the known inhibitors, even when AutoGrow4 is seeded with random small molecules. AutoGrow4 is available under the terms of the Apache License, Version 2.0. A copy can be downloaded free of charge from http://durrantlab.com/autogrow4.