Designing screens: how to make your hits a hit

Discovery of novel microsomal prostaglandin E2 synthase 1 (mPGES-1) inhibitors by a structurally inspired virtual screening study

Prostaglandin (PG) E2 is a pro-inflammatory lipid mediator derived from the metabolism of arachidonic acid (AA) by cyclooxygenases (COX) and PGE2 synthases. Nonsteroidal anti-inflammatory drugs (NSAIDs), commonly used in the treatment of inflammation, nonselectively inhibit COX activity and decrease PGE2 production. However, these drugs cause gastrointestinal bleeding and several cardiovascular complications. Therefore, inhibiting microsomal PGE2 Synthase-1 (mPGES-1) to block PGE2 production downstream of COX is expected to yield safer and more effective treatments for inflammation, cancer, and cardiovascular diseases. At present, there are no mPGES-1 inhibitors available on the market, but ongoing research continuously evaluates new compounds in both preclinical and clinical stages. Here, we conducted a high throughput virtual screening campaign to discover novel mPGES-1 inhibitor scaffolds. This campaign utilized physicochemical filtering alongside both structure-aware ligand-based approaches (shape screening templates and pharmacophore models, which were generated based on the 3D binding modes of the co-crystallized mPGES-1 inhibitors) and structure-based strategies (refinement with docking and molecular dynamics). Thirty-four compounds were selected and biologically tested for mPGES-1 inhibition in a cell-free assay using microsomes from interleukin-1β-stimulated A549 cells as the source of mPGES-1. The most potent compound inhibited the remaining enzyme activity with an IC50 value of 6.46 μM in a cell-free assay for PGE2 production. We also compared the binding patterns of the most active compounds identified in this study with those of co-crystallized inhibitors using molecular dynamics simulations. This comparison underscored the crucial role of ionic interactions, π-π interactions, hydrogen bonds, and water bridges involving specific amino acids. Our results highlight the importance of these interaction networks within the binding cavity in various binding scenarios. Ultimately, the insights gained from this study could assist in designing and developing new mPGES-1 inhibitors.

Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives

The applications of artificial intelligence (AI) in pharmaceutical sectors have advanced drug discovery and development methods. AI has been applied in virtual drug design, molecule synthesis, advanced research, various screening methods, and decision-making processes. In the fourth industrial revolution, when medical discoveries are happening swiftly, AI technology is essential to reduce the costs, effort, and time in the pharmaceutical industry. Further, it will aid "genome-based medicine" and "drug discovery." AI may prepare proactive databases according to diseases, disorders, and appropriate usage of drugs which will facilitate the required data for the process of drug development. The application of AI has improved clinical trials on patient selection in a population, stratification, and sample assessment such as biomarkers, effectiveness measures, dosage selection, and trial length. Various studies suggest AI could be perform better compared to conventional techniques in drug discovery. The present review focused on the positive impact of AI in drug discovery and development processes in the pharmaceutical industry and beneficial usage in health sectors as well.

Uncovering Novel Drugs that Restore Vision Using Orthogonal Pooling in Zebrafish

Photoreceptor and retinal pigment epithelium (RPE) dysfunction in inherited retinal degenerations (IRDs) and age-related macular degeneration (AMD) necessitate innovative therapies to preserve vision. Vision impairment incurs a substantial global economic burden, with the World Health Organization reporting an annual global productivity loss of approximately $411 billion. Current treatments are limited, underscoring the urgency for novel solutions. Leveraging new screening techniques, novel drugs restoring vision can be uncovered. Here, a workflow is described utilising orthogonal pooling to screen randomised library compounds for drug hits restoring vision and assessing the optokinetic response (OKR) in the atp6v0e1-/- zebrafish model of inherited blindness.

Discovery of 5-Nitro-N-(3-(trifluoromethyl)phenyl) Pyridin-2-amine as a Novel Pure Androgen Receptor Antagonist against Antiandrogen Resistance

The transformation of clinical androgen receptor (AR) antagonists into agonists driven by AR mutations poses a significant challenge in treating prostate cancer (PCa). Novel anti-AR therapeutics combating mutation-induced resistance are required. Herein, by combining structure-based virtual screening and biological evaluation, a high-affinity agonist E10 was first discovered. Then guided by the representative conformation of State 1 at the free energy landscape, the structural optimization of E10 was performed, and pure AR antagonists EL15 (IC50 = 0.94 μM) and EF2 (IC50 = 0.30 μM) were successfully identified. Both can antagonize wild-type and variant drug-resistant ARs. Therein, EF2 demonstrated potent inhibition of the AR pathway and effectively suppressed tumor growth in a C4-2B xenograft mouse model following oral administration. Further molecular dynamics simulation and mutagenesis studies revealed atomic insights into the mode of action of EF2 which may serve as a novel lead compound for developing therapeutics against AR-driven PCa.

Computationally accelerated identification of P-glycoprotein inhibitors

Overexpression of the polyspecific efflux transporter, P-glycoprotein (P-gp, MDR1, ABCB1 ), is a major mechanism by which cancer cells acquire multidrug resistance (MDR), the resistance to diverse chemotherapeutic drugs. Inhibiting drug transport by P-gp can resensitize cancer cells to chemotherapy, but there are no P-gp inhibitors available to patients. Clinically unsuccessful P-gp inhibitors tend to bind at the pump's transmembrane drug binding domains and are often P-gp transport substrates, resulting in lowered intracellular concentration of the drug and altered pharmacokinetics. In prior work, we used computationally accelerated drug discovery to identify novel P-gp inhibitors that target the pump's cytoplasmic nucleotide binding domains. Our first-draft study provided conclusive evidence that the nucleotide binding domains of P-gp are viable targets for drug discovery. Here we develop an enhanced, computationally accelerated drug discovery pipeline that expands upon our prior work by iteratively screening compounds against multiple conformations of P-gp with molecular docking. Targeted molecular dynamics simulations with our homology model of human P-gp were used to generate docking receptors in conformations mimicking a putative drug transport cycle. We offset the increased computational complexity using custom Tanimoto chemical datasets, which maximize the chemical diversity of ligands screened by docking. Using our expanded, virtual-assisted pipeline, we identified nine novel P-gp inhibitors that reverse MDR in two types of P-gp overexpressing human cancer cell lines, reflecting a 13.4% hit rate. Of these inhibitors, all were non-toxic to non-cancerous human cells, and six were not likely to be transport substrates of P-gp. Our novel P-gp inhibitors are chemically diverse and are good candidates for lead optimization. Our results demonstrate that the nucleotide binding domains of P-gp are an underappreciated target in the effort to reverse P-gp-mediated multidrug resistance in cancer.

In silico screening and in vivo experimental validation of 15-PGDH inhibitors from traditional Chinese medicine promoting liver regeneration

The enzyme 15-hydroxyprostaglandin dehydrogenase (15-PGDH), which acts as a negative regulator of prostaglandin E2 (PGE2) levels and activity, represents a promising pharmacological target for promoting liver regeneration. In this study, we collected data on 15-PGDH homologous family proteins, their inhibitors, and traditional Chinese medicine (TCM) compounds. Leveraging machine learning and molecular docking techniques, we constructed a prediction model for virtual screening of 15-PGDH inhibitors from TCM compound library and successfully screened genistein as a potential 15-PGDH inhibitor. Through further validation, it was discovered that genistein considerably enhances liver regeneration by inhibiting 15-PGDH, resulting in a significant increase in the PGE2 level. Genistein's effectiveness suggests its potential as a novel therapeutic agent for liver diseases, highlighting this study's contribution to expanding the clinical applications of TCM.

The impact of Library Size and Scale of Testing on Virtual Screening

Virtual libraries for ligand discovery have recently increased 10,000-fold, and this is thought to have improved hit rates and potencies from library docking. This idea has not, however, been experimentally tested in direct comparisons of larger-vs-smaller libraries. Meanwhile, though libraries have exploded, the scale of experimental testing has little changed, with often only dozens of high-ranked molecules investigated, making interpretation of hit rates and affinities uncertain. Accordingly, we docked a 1.7 billion molecule virtual library against the model enzyme AmpC β-lactamase, testing 1,521 new molecules and comparing the results to the same screen with a library of 99 million molecules, where only 44 molecules were tested. Encouragingly, the larger screen outperformed the smaller one: hit rates improved by two-fold, more new scaffolds were discovered, and potency improved. Overall, 50-fold more inhibitors were found, supporting the idea that there are many more compounds to be discovered than are being tested. With so many compounds evaluated, we could ask how the results vary with number tested, sampling smaller sets at random from the 1521. Hit rates and affinities were highly variable when we only sampled dozens of molecules, and it was only when we included several hundred molecules that results converged. As docking scores improved, so too did the likelihood of a molecule binding; hit rates improved steadily with docking score, as did affinities. This also appeared true on reanalysis of large-scale results against the σ2 and dopamine D4 receptors. It may be that as the scale of both the virtual libraries and their testing grows, not only are better ligands found but so too does our ability to rank them.

Creating a more strategic small molecule biophysical hit characterization workflow

To confirm target engagement of hits from our high-throughput screening efforts, we ran biophysical assays on several hundreds of hits from 15 different high-throughput screening campaigns. Analyzing the biophysical assay results from these screening campaigns led us to conclude that we could be more strategic in our biophysical analysis of hits by first confirming activity in a thermal shift assay (TSA) and then confirming activity in either a surface plasmon resonance (SPR) assay or a temperature-related intensity change (TRIC) assay. To understand how this new workflow shapes the quality of the final hits, we compared TSA/SPR or TSA/TRIC confirmed and unconfirmed hits to one another using four measures of compound quality: quantitative estimate of drug-likeness (QED), Pan-Assay Interference Compounds (PAINS), promiscuity, and aqueous solubility. In general, we found that the biophysically confirmed hits performed better in the compound quality metrics than the unconfirmed hits, demonstrating that our workflow not only confirmed target engagement of the hits but also enriched for higher quality hits.

Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery

Efficient prioritization of bioactive compounds from high throughput screening campaigns is a fundamental challenge for accelerating drug development efforts. In this study, we present the first data-driven approach to simultaneously detect assay interferents and prioritize true bioactive compounds. By analyzing the learning dynamics during training of a gradient boosting model on noisy high throughput screening data using a novel formulation of sample influence, we are able to distinguish between compounds exhibiting the desired biological response and those producing assay artifacts. Therefore, our method enables false positive and true positive detection without relying on prior screens or assay interference mechanisms, making it applicable to any high throughput screening campaign. We demonstrate that our approach consistently excludes assay interferents with different mechanisms and prioritizes biologically relevant compounds more efficiently than all tested baselines, including a retrospective case study simulating its use in a real drug discovery campaign. Finally, our tool is extremely computationally efficient, requiring less than 30 s per assay on low-resource hardware. As such, our findings show that our method is an ideal addition to existing false positive detection tools and can be used to guide further pharmacological optimization after high throughput screening campaigns.

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.

De Novo Drug Design Using Transformer-Based Machine Translation and Reinforcement Learning of an Adaptive Monte Carlo Tree Search

The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder-Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards their targets. We successfully integrated the Encoder-Decoder Transformer architecture, which generates molecular structures (drugs) from scratch with the RL-MCTS, serving as a reinforcement learning framework. The RL-MCTS combines the exploitation and exploration capabilities of a Monte Carlo Tree Search with the machine translation of a transformer-based Encoder-Decoder model. This dynamic approach allows the model to iteratively refine its drug candidate generation process, ensuring that the generated molecules adhere to essential physicochemical and biological constraints and effectively bind to their targets. The results from drugAI showcase the effectiveness of the proposed approach across various benchmark datasets, demonstrating a significant improvement in both the validity and drug-likeness of the generated compounds, compared to two existing benchmark methods. Moreover, drugAI ensures that the generated molecules exhibit strong binding affinities to their respective targets. In summary, this research highlights the real-world applications of drugAI in drug discovery pipelines, potentially accelerating the identification of promising drug candidates for a wide range of diseases.

Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor

HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold is a machine learning approach that can predict the 3D structure of proteins with high accuracy even in absence of similar structures. However, the fact that AlphaFold models are predicted in the absence of small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an HDAC11 AlphaFold model by adding the catalytic zinc ion and minimization in the presence of reported HDAC11 inhibitors. In the current study, we implement a comparative structure-based virtual screening approach utilizing the previously optimized HDAC11 AlphaFold model to identify novel and selective HDAC11 inhibitors. The stepwise virtual screening approach was successful in identifying a hit that was subsequently tested using an in vitro enzymatic assay. The hit compound showed an IC50 value of 3.5 µM for HDAC11 and could selectively inhibit HDAC11 over other HDAC subtypes at 10 µM concentration. In addition, we carried out molecular dynamics simulations to further confirm the binding hypothesis obtained by the docking study. These results reinforce the previously presented AlphaFold optimization approach and confirm the applicability of AlphaFold models in the search for novel inhibitors for drug discovery.

Shape-Based Virtual Screening of a Billion-Compound Library Identifies Mycobacterial Lipoamide Dehydrogenase Inhibitors

Lpd (lipoamide dehydrogenase) in Mycobacterium tuberculosis (Mtb) is required for virulence and is a genetically validated tuberculosis (TB) target. Numerous screens have been performed over the last decade, yet only two inhibitor series have been identified. Recent advances in large-scale virtual screening methods combined with make-on-demand compound libraries have shown the potential for finding novel hits. In this study, the Enamine REAL library consisting of ∼1.12 billion compounds was efficiently screened using the GPU Shape screen method against Mtb Lpd to find additional chemical matter that would expand on the known sulfonamide inhibitor series. We identified six new inhibitors with IC50 in the range of 5-100 μM. While these compounds remained chemically close to the already known sulfonamide series inhibitors, some diversity was found in the cores of the hits. The two most potent hits were further validated by one-step potency optimization to submicromolar levels. The co-crystal structure of optimized analogue TDI-13537 provided new insights into the potency determinants of the series.

Automation, live-cell imaging, and endpoint cell viability for prostate cancer drug screens

Androgen deprivation therapy (ADT) is the standard of care for high risk and advanced prostate cancer; however, disease progression from androgen-dependent prostate cancer (ADPC) to lethal and incurable castration-resistant prostate cancer (CRPC) and (in a substantial minority of cases) neuroendocrine prostate cancer (NEPC) is common. Identifying effective targeted therapies is challenging because of acquired resistance to established treatments and the vast heterogeneity of advanced prostate cancer (PC). To streamline the identification of potentially active prostate cancer therapeutics, we have developed an adaptable semi-automated protocol which optimizes cell growth and leverages automation to enhance robustness, reproducibility, and throughput while integrating live-cell imaging and endpoint viability assays to assess drug efficacy in vitro. In this study, culture conditions for 72-hr drug screens in 96-well plates were established for a large, representative panel of human prostate cell lines including: BPH-1 and RWPE-1 (non-tumorigenic), LNCaP and VCaP (ADPC), C4-2B and 22Rv1 (CRPC), DU 145 and PC3 (androgen receptor-null CRPC), and NCI-H660 (NEPC). The cell growth and 72-hr confluence for each cell line was optimized for real-time imaging and endpoint viability assays prior to screening for novel or repurposed drugs as proof of protocol validity. We demonstrated effectiveness and reliability of this pipeline through validation of the established finding that the first-in-class BET and CBP/p300 dual inhibitor EP-31670 is an effective compound in reducing ADPC and CRPC cell growth. In addition, we found that insulin-like growth factor-1 receptor (IGF-1R) inhibitor linsitinib is a potential pharmacological agent against highly lethal and drug-resistant NEPC NCI-H660 cells. This protocol can be employed across other cancer types and represents an adaptable strategy to optimize assay-specific cell growth conditions and simultaneously assess drug efficacy across multiple cell lines.

Dual inhibition of cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) by resveratrol derivatives in cancer therapy: in silico approach

In a number of human cancers, both cycloxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) are up-regulated and co-expressed, promoting cancer cell proliferation and angiogenesis. Resveratrol (3,4',5-trihydroxy-trans-stilbene) is a natural polyphenolic phytoalexin found in a variety of plants that influences various signal-transduction pathways which control apoptosis, cell growth and cell division, metastasis, angiogenesis and inflammation, and has an impact on cancer stages ranging from initiation to progression. In this work, molecular docking and molecular dynamics simulation method are employed to design resveratrol derivatives for COX-2 and 5-LOX enzymes. By attaching several functional groups on four different places of the resveratrol scaffold, the R group enumeration approach was employed to build four libraries of resveratrol derivatives. Thus, R group enumeration is done to focus on the enhancement of potency of compounds and other chemical characteristics like solubility. Drug-like filters such as REOS 1, 2, 3 and PAINS were applied to the libraries, generating a total of 5557 compounds. Drug-like filters such as REOS and PAINS-1, 2 and 3 were applied to the libraries, generating a total of 5557 compounds. All of these compounds were docked with both enzymes using the Glide SP and XP docking methods. Enrichment calculations were performed using 40 compounds from XP docking along with resveratrol, and 1000 decoy compounds from the DUD-E database to validate the docking protocol. The stability of the complexes was further studied using molecular dynamics simulation, radius of gyration, MM/GBSA, H bond monitoring and electrostatic potential surface (EPS). ADMET properties of compounds were studied using SwissADME and pkCSM server.Communicated by Ramaswamy H. Sarma.

Accelerating inhibitor discovery for deubiquitinating enzymes

Deubiquitinating enzymes (DUBs) are an emerging drug target class of ~100 proteases that cleave ubiquitin from protein substrates to regulate many cellular processes. A lack of selective chemical probes impedes pharmacologic interrogation of this important gene family. DUBs engage their cognate ligands through a myriad of interactions. We embrace this structural complexity to tailor a chemical diversification strategy for a DUB-focused covalent library. Pairing our library with activity-based protein profiling as a high-density primary screen, we identify selective hits against 23 endogenous DUBs spanning four subfamilies. Optimization of an azetidine hit yields a probe for the understudied DUB VCPIP1 with nanomolar potency and in-family selectivity. Our success in identifying good chemical starting points as well as structure-activity relationships across the gene family from a modest but purpose-build library challenges current paradigms that emphasize ultrahigh throughput in vitro or virtual screens against an ever-increasing scope of chemical space.

Discovery of new PKN2 inhibitory chemotypes via QSAR-guided selection of docking-based pharmacophores

Serine/threonine-protein kinase N2 (PKN2) plays an important role in cell cycle progression, cell migration, cell adhesion and transcription activation signaling processes. In cancer, however, it plays important roles in tumor cell migration, invasion and apoptosis. PKN2 inhibitors have been shown to be promising in treating cancer. This prompted us to model this interesting target using our QSAR-guided selection of docking-based pharmacophores approach where numerous pharmacophores are extracted from docked ligand poses and allowed to compete within the context of QSAR. The optimal pharmacophore was sterically-refined, validated by receiver operating characteristic (ROC) curve analysis and used as virtual search query to screen the National Cancer Institute (NCI) database for new promising anti-PKN2 leads of novel chemotypes. Three low micromolar hits were identified with IC50 values ranging between 9.9 and 18.6 µM. Pharmacological assays showed promising cytotoxic properties for active hits in MTT and wound healing assays against MCF-7 and PANC-1 cancer cells.

High throughput screen of small molecules as potential countermeasures to galactic cosmic radiation induced cellular dysfunction

Space travel increases galactic cosmic ray exposure to flight crews and this is significantly elevated once travel moves beyond low Earth orbit. This includes combinations of high energy protons and heavy ions such as ⁵⁶Fe or ¹⁶O. There are distinct differences in the biological response to low-energy transfer (x-rays) or high-energy transfer (High-LET). However, given the relatively low fluence rate of exposure during flight operations, it might be possible to manage these deleterious effects using small molecules currently available. Virtually all reports to date examining small molecule management of radiation exposure are based on low-LET challenges. To that end an FDA approved drug library (725 drugs) was used to perform a high throughput screen of cultured cells following exposure to galactic cosmic radiation. The H9c2 myoblasts, ES-D3 pluripotent cells, and Hy926 endothelial cell lines were exposed to a single exposure (75 cGy) using the 5-ion GCRsim protocol developed at the NASA Space Radiation Laboratory (NSRL). Following GCR exposure cells were maintained for up to two weeks. For each drug (@10µM), a hierarchical cumulative score was developed incorporating measures of mitochondrial and cellular function, oxidant stress and cell senescence. The top 160 scores were retested following a similar protocol using 1µM of each drug. Within the 160 drugs, 33 are considered to have an anti-inflammatory capacity, while others also indirectly suppressed pro-inflammatory pathways or had noted antioxidant capacity. Lead candidates came from different drug classes that included angiotensin converting enzyme inhibitors or AT1 antagonists, COX2 inhibitors, as well as drugs mediated by histamine receptors. Surprisingly, different classes of anti-diabetic medications were observed to be useful including sulfonylureas and metformin. Using a hierarchical decision structure, we have identified several lead candidates. That no one drug or even drug class was completely successful across all parameters tested suggests the complexity of managing the consequences of galactic cosmic radiation exposure.

Pharmacophore Modeling of Targets Infested with Activity Cliffs via Molecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study

Activity cliffs (ACs) are defined as pairs of structurally similar compounds with large difference in their potencies against certain biotarget. We recently proposed that potent AC members induce significant entropically-driven conformational modifications of the target that unveil additional binding interactions, while their weakly-potent counterparts are enthalpically-driven binders with little influence on the protein target. We herein propose to extract pharmacophores for ACs-infested target(s) from molecular dynamics (MD) frames of purely "enthalpic" potent binder(s) complexed within the particular target. Genetic function algorithm/machine learning (GFA/ML) can then be employed to search for the best possible combination of MD pharmacophore(s) capable of explaining bioactivity variations within a list of inhibitors. We compared the performance of this approach with established ligand-based and structure-based methods. Kinase inserts domain receptor (KDR) was used as a case study. KDR plays a crucial role in angiogenic signalling and its inhibitors have been approved in cancer treatment. Interestingly, GFA/ML selected, MD-based, pharmacophores were of comparable performances to ligand-based and structure-based pharmacophores. The resulting pharmacophores and QSAR models were used to capture hits from the national cancer institute list of compounds. The most active hit showed anti-KDR IC50 of 2.76 μM.

Predictive validity in drug discovery: what it is, why it matters and how to improve it

Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which 'predictive validity' is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.

Copper-Mediated Three-Component Reaction for the Synthesis of N-Acylsulfonamide on DNA

The DNA-encoded library (DEL) technology is a new method for discovering hit compounds for target proteins in the pharmaceutical industry. The N-acylsulfonamide functional group has been reported to exhibit various pharmacological activities, and based on this, the demand for a method that allows its introduction into the DEL platform has increased. In this report, a procedure for synthesizing N-acylsulfonamide functional groups applicable to DEL construction was developed in the presence of a copper reagent and water as a nucleophile from simple alkynes or sulfonyl azides, which are widely commercially available. Furthermore, we prove that a new alternative procedure can be used to construct a DNA-encoded library.

Maximizing the integration of virtual and experimental screening in hit discovery

INTRODUCTION

Experimental and virtual screening contributes to the discovery of more than 50% of clinical candidates. Considering the similar concept and goals, early-phase drug discovery would benefit from the effective integration of these approaches.

AREAS COVERED

After reviewing the recent trends in both experimental and virtual screening, the authors discuss different integration strategies from parallel, focused, sequential, and iterative screening. Strategic considerations are demonstrated in a number of real-life case studies.

EXPERT OPINION

Experimental and virtual screening are complementary approaches that should be integrated in lead discovery settings. Virtual screening can access extremely large synthetically feasible chemical space that can be effectively searched on GPU clusters or cloud architectures. Experimental screening provides reliable datasets by quantitative HTS applications, and DNA-encoded libraries (DEL) have enlarged the chemical space covered by these technologies. These developments, together with the use of artificial intelligence methods, represent new options for their efficient integration. The case studies discussed here demonstrate the benefits of complementary strategies, such as focused and iterative screening.

Comparative Analyses of Medicinal Chemistry and Cheminformatics Filters with Accessible Implementation in Konstanz Information Miner (KNIME)

High-throughput virtual screening (HTVS) is, in conjunction with rapid advances in computer hardware, becoming a staple in drug design research campaigns and cheminformatics. In this context, virtual compound library design becomes crucial as it generally constitutes the first step where quality filtered databases are essential for the efficient downstream research. Therefore, multiple filters for compound library design were devised and reported in the scientific literature. We collected the most common filters in medicinal chemistry (PAINS, REOS, Aggregators, van de Waterbeemd, Oprea, Fichert, Ghose, Mozzicconacci, Muegge, Egan, Murcko, Veber, Ro3, Ro4, and Ro5) to facilitate their open access use and compared them. Then, we implemented these filters in the open platform Konstanz Information Miner (KNIME) as a freely accessible and simple workflow compatible with small or large compound databases for the benefit of the readers and for the help in the early drug design steps.

Towards discovery of inhibitors of the undecaprenyl-pyrophosphate phosphatase BacA by virtual high-throughput screening

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BacA is an important conserved bacterial protein and a promising drug target.

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We identified the first small molecule inhibitors of BacA via ensemble docking.

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Novel inhibitors possess 5-sulfamoyl-2-thenoic acid scaffold.

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Hit compounds display IC₅₀s from 42 to 374 μM and antibacterial activity on E. coli.

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Reported compounds are a valuable starting point for new antibacterial drug design.

Increasing resistance to common antibiotics is becoming a major challenge that requires the development of new antibacterial agents. Peptidoglycan is an essential heteropolymer of the bacterial envelope that ensures the integrity and shape of all bacteria and is also an important target for antibiotics. The biosynthesis of peptidoglycan depends on a lipid carrier, undecaprenyl phosphate. As a byproduct of peptidoglycan polymerization, the lipid carrier is released as undecaprenyl pyrophosphate, which must be recycled to allow new polymerization cycles. To this end, it undergoes a dephosphorylation process catalyzed by the membrane phosphatase BacA, which is specific and highly conserved in bacteria. In the present study, we identified small molecules displaying inhibitory potency towards BacA. We began by preparing a commercial compound library, followed by high-throughput virtual screening by ensemble docking using the 3D structure of BacA and molecular dynamics snapshots to account for the flexibility of the protein. Of 83 compounds computationally selected and tested in a biochemical assay, one sulfamoylthiophene molecule showed significant inhibition of the undecaprenyl pyrophosphate dephosphorylation activity catalyzed by BacA. Subsequently, an additional 33 scaffold analogs were selected and acquired, of which 6 compounds exhibited BacA inhibition. The IC₅₀ values of these compounds ranged from 42 to 366 μM. In addition, significant antibacterial activity against Escherichia coli was observed in TolC/PAP2-depleted strains. We believe that the overall strategy followed in this study and the identified class of inhibitors provide a solid foundation for the further development of potent BacA-targeted inhibitors and the discovery of novel antibacterial compounds.

Lockdown, a selective small-molecule inhibitor of the integrin phosphatase PPM1F, blocks cancer cell invasion

Phosphatase PPM1F is a regulator of cell adhesion by fine-tuning integrin activity and actin cytoskeleton structures. Elevated expression of this enzyme in human tumors is associated with high invasiveness, enhanced metastasis, and poor prognosis. Thus, PPM1F is a target for pharmacological intervention, yet inhibitors of this enzyme are lacking. Here, we use high-throughput screening to identify Lockdown, a reversible and non-competitive PPM1F inhibitor. Lockdown is selective for PPM1F, because this compound does not inhibit other protein phosphatases in vitro and does not induce additional phenotypes in PPM1F knockout cells. Importantly, Lockdown-treated glioblastoma cells fully re-capitulate the phenotype of PPM1F-deficient cells as assessed by increased phosphorylation of PPM1F substrates and corruption of integrin-dependent cellular processes. Ester modification yields Lockdown^Pro with increased membrane permeability and prodrug-like properties. Lockdown^Pro suppresses tissue invasion by PPM1F-overexpressing human cancer cells, validating PPM1F as a therapeutic target and providing an access point to control tumor cell dissemination.

High-throughput glycolytic inhibitor discovery targeting glioblastoma by graphite dots-assisted LDI mass spectrometry

Malignant tumors will become vulnerable if their uncontrolled biosynthesis and energy consumption engaged in metabolic reprogramming can be cut off. Here, we report finding a glycolytic inhibitor targeting glioblastoma with graphite dots-assisted laser desorption/ionization mass spectrometry as an integrated drug screening and pharmacokinetic platform (GLMSD). We have performed high-throughput virtual screening to narrow an initial library of 240,000 compounds down to the docking of 40 compounds and identified five previously unknown chemical scaffolds as promising hexokinase-2 inhibitors. The best inhibitor (Compd 27) can regulate the reprogrammed metabolic pathway in U87 glioma cells (median inhibitory concentration ~ 11.3 μM) for tumor suppression. Highly effective therapy against glioblastoma has been demonstrated in both subcutaneous and orthotopic brain tumors by synergizing Compd 27 and temozolomide. Our glycolytic inhibitor discovery can inspire personalized medicine targeting reprogrammed metabolisms of malignant tumors. GLMSD enables large, high-quality data for next-generation artificial intelligence-aided drug development.

Discovery of New Estrogen-Related Receptor α Agonists via a Combination Strategy Based on Shape Screening and Ensemble Docking

Estrogen-related receptor α (ERRα), a member of nuclear receptors (NRs), plays a role in the regulation of cellular energy metabolism and is reported to be a novel potential target for type 2 diabetes therapy. To date, only a few agonists of ERRα have been identified to improve insulin sensitivity and decrease blood glucose levels. Herein, the discovery of novel potent agonists of ERRα determined using a combined virtual screening approach is described. Molecular dynamics (MD) simulations were used to obtain structural ensembles that can consider receptor flexibility. Then, an efficient virtual screening strategy with a combination of similarity search and ensemble docking was performed against the Enamine, SPECS, and Drugbank databases to identify potent ERRα agonists. Finally, a total of 66 compounds were purchased for experimental testing. Biological investigation of promising candidates identified seven compounds that have activity against ERRα with EC50 values ranging from 1.11 to 21.70 μM, with novel scaffolds different from known ERRα agonists until now. Additionally, the molecule GX66 showed micromolar inverse activity against ERRα with an IC50 of 0.82 μM. The predicted binding modes showed that these compounds were anchored in ERRα-LBP via interactions with several residues of ERRα. Overall, this study not only identified the novel potent ERRα agonists or an inverse agonist that would be the promising starting point for further exploration but also demonstrated a successful molecular dynamics-guided approach applicable in virtual screening for ERRα agonists.

Discovery of novel TrkA allosteric inhibitors: Structure-based virtual screening, biological evaluation and preliminary SAR studies

Tropomyosin receptor kinases A (TrkA) is a potential therapeutic target for the treatment of numerous tumor types and chronic pain. However, most of the reported TrkA inhibitors are ATP competitive pan-Trks inhibitors that lack subtype selectivity. A selective TrkA inhibitor may provide valuable therapeutic benefits. Here, we described the discovery of novel TrkA allosteric inhibitors by structure-based virtual screening. A promising hit (D5261, TrkA cell IC₅₀ = 3.32 μM) was selected for further studies. The binding free energy between TrkA and D5261 was calculated. In addition, the preliminary structure-activity relationship (SAR) studies with D5261 were investigated. The results suggest that D5261 can be used as a starting point for development of TrkA allosteric inhibitors.

Modeling viral infection with tissue engineering: COVID-19 and the next outbreaks

Pandemics caused by respiratory viruses have impacted millions of lives and caused massive destruction to global infrastructure. With their emergence, it has become a priority to develop platforms to rapidly dissect host/pathogen interactions, develop diagnostics, and evaluate therapeutics. Traditional viral culture methods do not faithfully recapitulate key aspects of infection. Tissue engineering as a discipline has developed techniques to produce three-dimensional human tissues which can serve as platforms to study respiratory viruses in vitro. In this chapter, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has been used as a representative respiratory virus motivating the use of tissue engineering to generate in vitro culture models. SARS-CoV-2 pathophysiology, traditional cell culture, tissue engineering-based cell culture, and future directions for the field are highlighted.

Discovery of novel antagonists targeting the DNA binding domain of androgen receptor by integrated docking-based virtual screening and bioassays

Androgen receptor (AR), a ligand-activated transcription factor, is a master regulator in the development and progress of prostate cancer (PCa). A major challenge for the clinically used AR antagonists is the rapid emergence of resistance induced by the mutations at AR ligand binding domain (LBD), and therefore the discovery of novel anti-AR therapeutics that can combat mutation-induced resistance is quite demanding. Therein, blocking the interaction between AR and DNA represents an innovative strategy. However, the hits confirmed targeting on it so far are all structurally based on a sole chemical scaffold. In this study, an integrated docking-based virtual screening (VS) strategy based on the crystal structure of the DNA binding domain (DBD) of AR was conducted to search for novel AR antagonists with new scaffolds and 2-(2-butyl-1,3-dioxoisoindoline-5-carboxamido)-4,5-dimethoxybenzoicacid (Cpd39) was identified as a potential hit, which was competent to block the binding of AR DBD to DNA and showed decent potency against AR transcriptional activity. Furthermore, Cpd39 was safe and capable of effectively inhibiting the proliferation of PCa cell lines (i.e., LNCaP, PC3, DU145, and 22RV1) and reducing the expression of the genes regulated by not only the full-length AR but also the splice variant AR-V7. The novel AR DBD-ARE blocker Cpd39 could serve as a starting point for the development of new therapeutics for castration-resistant PCa.

Identification of a novel compound that simultaneously impairs the ubiquitin-proteasome system and autophagy

The ubiquitin-proteasome system (UPS) and macroautophagy/autophagy are the main proteolytic systems in eukaryotic cells for preserving protein homeostasis, i.e., proteostasis. By facilitating the timely destruction of aberrant proteins, these complementary pathways keep the intracellular environment free of inherently toxic protein aggregates. Chemical interference with the UPS or autophagy has emerged as a viable strategy for therapeutically targeting malignant cells which, owing to their hyperactive state, heavily rely on the sanitizing activity of these proteolytic systems. Here, we report on the discovery of CBK79, a novel compound that impairs both protein degradation by the UPS and autophagy. While CBK79 was identified in a high-content screen for drug-like molecules that inhibit the UPS, subsequent analysis revealed that this compound also compromises autophagic degradation of long-lived proteins. We show that CBK79 induces non-canonical lipidation of MAP1LC3B/LC3B (microtubule-associated protein 1 light chain 3 beta) that requires ATG16L1 but is independent of the ULK1 (unc-51 like autophagy activating kinase 1) and class III phosphatidylinositol 3-kinase (PtdIns3K) complexes. Thermal preconditioning of cells prevented CBK79-induced UPS impairment but failed to restore autophagy, indicating that activation of stress responses does not allow cells to bypass the inhibitory effect of CBK79 on autophagy. The identification of a small molecule that simultaneously impairs the two main proteolytic systems for protein quality control provides a starting point for the development of a novel class of proteostasis-targeting drugs.

Gains from no real PAINS: Where 'Fair Trial Strategy' stands in the development of multi-target ligands

Compounds that selectively modulate multiple targets can provide clinical benefits and are an alternative to traditional highly selective agents for unique targets. High-throughput screening (HTS) for multitarget-directed ligands (MTDLs) using approved drugs, and fragment-based drug design has become a regular strategy to achieve an ideal multitarget combination. However, the unexpected presence of pan-assay interference compounds (PAINS) suspects in the development of MTDLs frequently results in nonspecific interactions or other undesirable effects leading to artefacts or false-positive data of biological assays. Publicly available filters can help to identify PAINS suspects; however, these filters cannot comprehensively conclude whether these suspects are "bad" or innocent. Additionally, these in silico approaches may inappropriately label a ligand as PAINS. More than 80% of the initial hits can be identified as PAINS by the filters if appropriate biochemical tests are not used resulting in false positive data that are unacceptable for medicinal chemists in manuscript peer review and future studies. Therefore, extensive offline experiments should be used after online filtering to discriminate "bad" PAINS and avoid incorrect evaluation of good scaffolds. We suggest that the use of "Fair Trial Strategy" to identify interesting molecules in PAINS suspects to provide certain structure‒function insight in MTDL development.

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.

The Growing Importance of Chirality in 3D Chemical Space Exploration and Modern Drug Discovery Approaches for Hit-ID: Topical Innovations

Modern-day drug discovery is now blessed with a wide range of high-throughput hit identification (hit-ID) strategies that have been successfully validated in recent years, with particular success coming from high-throughput screening, fragment-based lead discovery, and DNA-encoded library screening. As screening efficiency and throughput increases, this enables the viable exploration of increasingly complex three-dimensional (3D) chemical structure space, with a realistic chance of identifying highly specific hit ligands with increased target specificity and reduced attrition rates in preclinical and clinical development. This minireview will explore the impact of an improved design of multifunctionalized, sp3-rich, stereodefined scaffolds on the (virtual) exploration of 3D chemical space and the specific requirements for different hit-ID technologies.

An Overview of Cell-Based Assay Platforms for the Solute Carrier Family of Transporters

The solute carrier (SLC) superfamily represents the biggest family of transporters with important roles in health and disease. Despite being attractive and druggable targets, the majority of SLCs remains understudied. One major hurdle in research on SLCs is the lack of tools, such as cell-based assays to investigate their biological role and for drug discovery. Another challenge is the disperse and anecdotal information on assay strategies that are suitable for SLCs. This review provides a comprehensive overview of state-of-the-art cellular assay technologies for SLC research and discusses relevant SLC characteristics enabling the choice of an optimal assay technology. The Innovative Medicines Initiative consortium RESOLUTE intends to accelerate research on SLCs by providing the scientific community with high-quality reagents, assay technologies and data sets, and to ultimately unlock SLCs for drug discovery.

A capillary-based microfluidic device enables primary high-throughput room-temperature crystallographic screening

A novel capillary-based microfluidic strategy to accelerate the process of small-molecule-compound screening by room-temperature X-ray crystallography using protein crystals is reported. The ultra-thin microfluidic devices are composed of a UV-curable polymer, patterned by cleanroom photolithography, and have nine capillary channels per chip. The chip was designed for ease of sample manipulation, sample stability and minimal X-ray background. 3D-printed frames and cassettes conforming to SBS standards are used to house the capillary chips, providing additional mechanical stability and compatibility with automated liquid- and sample-handling robotics. These devices enable an innovative in situ crystal-soaking screening workflow, akin to high-throughput compound screening, such that quantitative electron density maps sufficient to determine weak binding events are efficiently obtained. This work paves the way for adopting a room-temperature microfluidics-based sample delivery method at synchrotron sources to facilitate high-throughput protein-crystallography-based screening of compounds at high concentration with the aim of discovering novel binding events in an automated manner.

Transmol: repurposing a language model for molecular generation

Recent advances in convolutional neural networks have inspired the application of deep learning to other disciplines. Even though image processing and natural language processing have turned out to be the most successful, there are many other domains that have also benefited; among them, life sciences in general and chemistry and drug design in particular. In concordance with this observation, from 2018 the scientific community has seen a surge of methodologies related to the generation of diverse molecular libraries using machine learning. However to date, attention mechanisms have not been employed for the problem of de novo molecular generation. Here we employ a variant of transformers, an architecture recently developed for natural language processing, for this purpose. Our results indicate that the adapted Transmol model is indeed applicable for the task of generating molecular libraries and leads to statistically significant increases in some of the core metrics of the MOSES benchmark. The presented model can be tuned to either input-guided or diversity-driven generation modes by applying a standard one-seed and a novel two-seed approach, respectively. Accordingly, the one-seed approach is best suited for the targeted generation of focused libraries composed of close analogues of the seed structure, while the two-seeds approach allows us to dive deeper into under-explored regions of the chemical space by attempting to generate the molecules that resemble both seeds. To gain more insights about the scope of the one-seed approach, we devised a new validation workflow that involves the recreation of known ligands for an important biological target vitamin D receptor. To further benefit the chemical community, the Transmol algorithm has been incorporated into our cheML.io web database of ML-generated molecules as a second generation on-demand methodology.

Tumor pyruvate kinase M2 modulators: a comprehensive account of activators and inhibitors as anticancer agents

Pyruvate kinase M2 (PKM2) catalyzes the conversion of phosphoenolpyruvate (PEP) to pyruvate. It plays a central role in the metabolic reprogramming of cancer cells and is expressed in most human tumors. It is essential in indiscriminate proliferation, survival, and tackling apoptosis in cancer cells. This positions PKM2 as a hot target in cancer therapy. Despite its well-known structure and several reported modulators targeting PKM2 as activators or inhibitors, a comprehensive review focusing on such modulators is lacking. Herein we summarize modulators of PKM2, the assays used to detect their potential, the preferable tense (T) and relaxed (R) states in which the enzyme resides, lacunae in existing modulators, and several strategies that may lead to effective anticancer drug development targeting PKM2.

Addressing Compound Reactivity and Aggregation Assay Interferences: Case Studies of Biochemical High-Throughput Screening Campaigns Benefiting from the National Institutes of Health Assay Guidance Manual Guidelines

Compound-dependent assay interferences represent a continued burden in drug and chemical probe discovery. The open-source National Institutes of Health/National Center for Advancing Translational Sciences (NIH/NCATS) Assay Guidance Manual (AGM) established an "Assay Artifacts and Interferences" section to address different sources of artifacts and interferences in biological assays. In addition to the frequent introduction of new chapters in this important topic area, older chapters are periodically updated by experts from academia, industry, and government to include new technologies and practices. Section chapters describe many best practices for mitigating and identifying compound-dependent assay interferences. Using two previously reported biochemical high-throughput screening campaigns for small-molecule inhibitors of the epigenetic targets Rtt109 and NSD2, the authors review best practices and direct readers to high-yield resources in the AGM and elsewhere for the mitigation and identification of compound-dependent reactivity and aggregation assay interferences.

FRET-Based Probe for High-Throughput DNA Intercalator Drug Discovery and In Vivo Imaging

Molecules that bind DNA by intercalating its bases remain among the most potent cancer therapies and antimicrobials due to their interference with DNA-processing proteins. To accelerate the discovery of novel intercalating drugs, we designed a fluorescence resonance energy transfer (FRET)-based probe that reports on DNA intercalation, allowing rapid and sensitive screening of chemical libraries in a high-throughput format. We demonstrate that the method correctly identifies known DNA intercalators in approved drug libraries and discover previously unreported intercalating compounds. When introduced in cells, the oligonucleotide-based probe rapidly distributes in the nucleus, allowing direct imaging of the dynamics of drug entry and its interaction with DNA in its native environment. This enabled us to directly correlate the potency of intercalators in killing cultured cancer cells with the ability of the drug to penetrate the cell membrane. The combined capability of the single probe to identify intercalators in vitro and follow their function in vivo can play a valuable role in accelerating the discovery of novel DNA-intercalating drugs or repurposing approved ones.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Drug screening to identify compounds to act as co-therapies for the treatment of Burkholderia species

Burkholderia pseudomallei is a soil-dwelling organism present throughout the tropics. It is the causative agent of melioidosis, a disease that is believed to kill 89,000 people per year. It is naturally resistant to many antibiotics, requiring at least two weeks of intravenous treatment with ceftazidime, imipenem or meropenem followed by 6 months of orally delivered co-trimoxazole. This places a large treatment burden on the predominantly middle-income nations where the majority of disease occurs. We have established a high-throughput assay for compounds that could be used as a co-therapy to potentiate the effect of ceftazidime, using the related non-pathogenic bacterium Burkholderia thailandensis as a surrogate. Optimization of the assay gave a Z’ factor of 0.68. We screened a library of 61,250 compounds and identified 29 compounds with a pIC₅₀ (-log₁₀(IC₅₀)) greater than five. Detailed investigation allowed us to down select to six “best in class” compounds, which included the licensed drug chloroxine. Co-treatment of B. thailandensis with ceftazidime and chloroxine reduced culturable cell numbers by two orders of magnitude over 48 hours, compared to treatment with ceftazidime alone. Hit expansion around chloroxine was performed using commercially available compounds. Minor modifications to the structure abolished activity, suggesting that chloroxine likely acts against a specific target. Finally, an initial study demonstrates the utility of chloroxine to act as a co-therapy to potentiate the effect of ceftazidime against B. pseudomallei. This approach successfully identified potential co-therapies for a recalcitrant Gram-negative bacterial species. Our assay could be used more widely to aid in chemotherapy to treat infections caused by these bacteria.

Nuisance compounds in cellular assays

Compounds that exhibit assay interference or undesirable mechanisms of bioactivity ("nuisance compounds") are routinely encountered in cellular assays, including phenotypic and high-content screening assays. Much is known regarding compound-dependent assay interferences in cell-free assays. However, despite the essential role of cellular assays in chemical biology and drug discovery, there is considerably less known about nuisance compounds in more complex cell-based assays. In our view, a major obstacle to realizing the full potential of chemical biology will not just be difficult-to-drug targets or even the sheer number of targets, but rather nuisance compounds, due to their ability to waste significant resources and erode scientific trust. In this review, we summarize our collective academic, government, and industry experiences regarding cellular nuisance compounds. We describe assay design strategies to mitigate the impact of nuisance compounds and suggest best practices to efficiently address these compounds in complex biological settings.

In silico analyses of toxicity of the major constituents of essential oils from two Ipomoea L. species

Convolvulaceae Juss. is a family of vines and shrubs composed of species of ecological and economic importance. Ipomoea asarifolia (Desr.) Roem. & Schult. and I. setifera Poir. are ruderal and evergreen weeds that invade pastures and cause intoxication in cattle during the dry season. In the present study, the essential oils (EOs) of the leaves from I. setifera (dry season) and I. asarifolia (dry and wet seasons) were obtained by steam distillation for 3h. The chemical composition of the EOs was determined using gas chromatography coupled to gas spectrometry (CG/MS) and gas chromatography with flame ionization detector (CG-FID). To correlate the toxicity of the major chemical constituents of I. setifera and I. asarifolia EOs, we predicted the inhibition activity against the cytochrome P450 (CYP450) and P-glycoprotein 1 (P-gp) using a machine learning-based (ML-based) algorithm. In silico analyses were also applied to evaluate the pharmacokinetics properties related to the penetration in the blood-brain barrier (BBB) and gastrointestinal absorption. The chemical composition of the EO of I. setifera was characterized by high levels of (E)-caryophyllene (36.7%) and β-elemene (20.49%). The I. asarifolia EO showed a phytol derivative as the main chemical constituent in the dry season (35.49%), and its content was reduced in the sample collected during the wet season (10.67%). The constituent (E)-caryophyllene was also present in the leaves of I. asarifolia, but at lower levels (15.93-19.93%) when compared to the EOs of I. setifera. Our computational analyses indicated that the constituents caryophyllene oxide, cedroxyde, pentadecanal, and phytol can be related to the toxicity of these weeds. This is the first study to report the chemical composition of I. asarifolia and I. setifera EOs and correlate their molecular mechanism of toxicity using in silico approaches.

A multifunctional cross-validation high-throughput screening protocol enabling the discovery of new SHP2 inhibitors

The protein tyrosine phosphatase Src homology phosphotyrosyl phosphatase 2 (SHP2) is implicated in various cancers, and targeting SHP2 has become a promising therapeutic approach. We herein described a robust cross-validation high-throughput screening protocol that combined the fluorescence-based enzyme assay and the conformation-dependent thermal shift assay for the discovery of SHP2 inhibitors. The established method can effectively exclude the false positive SHP2 inhibitors with fluorescence interference and was also successfully employed to identify new protein tyrosine phosphatase domain of SHP2 (SHP2-PTP) and allosteric inhibitors. Of note, this protocol showed potential for identifying SHP2 inhibitors against cancer-associated SHP2 mutation SHP2-E76A. After initial screening of our in-house compound library (∼2300 compounds), we identified 4 new SHP2-PTP inhibitors (0.17% hit rate) and 28 novel allosteric SHP2 inhibitors (1.22% hit rate), of which SYK-85 and WS-635 effectively inhibited SHP2-PTP (SYK-85: IC50 = 0.32 μmol/L; WS-635: IC50 = 4.13 μmol/L) and thus represent novel scaffolds for designing new SHP2-PTP inhibitors. TK-147, an allosteric inhibitor, inhibited SHP2 potently (IC50 = 0.25 μmol/L). In structure, TK-147 could be regarded as a bioisostere of the well characterized SHP2 inhibitor SHP-099, highlighting the essential structural elements for allosteric inhibition of SHP2. The principle underlying the cross-validation protocol is potentially feasible to identify allosteric inhibitors or those inactivating mutants of other proteins.

DockCoV2: a drug database against SARS-CoV-2

The current state of the COVID-19 pandemic is a global health crisis. To fight the novel coronavirus, one of the best-known ways is to block enzymes essential for virus replication. Currently, we know that the SARS-CoV-2 virus encodes about 29 proteins such as spike protein, 3C-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), Papain-like protease (PLpro), and nucleocapsid (N) protein. SARS-CoV-2 uses human angiotensin-converting enzyme 2 (ACE2) for viral entry and transmembrane serine protease family member II (TMPRSS2) for spike protein priming. Thus in order to speed up the discovery of potential drugs, we develop DockCoV2, a drug database for SARS-CoV-2. DockCoV2 focuses on predicting the binding affinity of FDA-approved and Taiwan National Health Insurance (NHI) drugs with the seven proteins mentioned above. This database contains a total of 3,109 drugs. DockCoV2 is easy to use and search against, is well cross-linked to external databases, and provides the state-of-the-art prediction results in one site. Users can download their drug-protein docking data of interest and examine additional drug-related information on DockCoV2. Furthermore, DockCoV2 provides experimental information to help users understand which drugs have already been reported to be effective against MERS or SARS-CoV. DockCoV2 is available at https://covirus.cc/drugs/.

Structure-Based Discovery and Bioactivity Evaluation of Novel Aurora-A Kinase Inhibitors as Anticancer Agents via Docking-Based Comparative Intermolecular Contacts Analysis (dbCICA)

Aurora-A kinase plays a central role in mitosis, where aberrant activation contributes to cancer by promoting cell cycle progression, genomic instability, epithelial-mesenchymal transition, and cancer stemness. Aurora-A kinase inhibitors have shown encouraging results in clinical trials but have not gained Food and Drug Administration (FDA) approval. An innovative computational workflow named Docking-based Comparative Intermolecular Contacts Analysis (dbCICA) was applied-aiming to identify novel Aurora-A kinase inhibitors-using seventy-nine reported Aurora-A kinase inhibitors to specify the best possible docking settings needed to fit into the active-site binding pocket of Aurora-A kinase crystal structure, in a process that only potent ligands contact critical binding-site spots, distinct from those occupied by less-active ligands. Optimal dbCICA models were transformed into two corresponding pharmacophores. The optimal one, in capturing active hits and discarding inactive ones, validated by receiver operating characteristic analysis, was used as a virtual in-silico search query for screening new molecules from the National Cancer Institute database. A fluorescence resonance energy transfer (FRET)-based assay was used to assess the activity of captured molecules and five promising Aurora-A kinase inhibitors were identified. The activity was next validated using a cell culture anti-proliferative assay (MTT) and revealed a most potent lead 85(NCI 14040) molecule after 72 h of incubation, scoring IC50 values of 3.5-11.0 μM against PANC1 (pancreas), PC-3 (prostate), T-47D and MDA-MB-231 (breast)cancer cells, and showing favorable safety profiles (27.5 μM IC50 on fibroblasts). Our results provide new clues for further development of Aurora-A kinase inhibitors as anticancer molecules.

Screening Malaria-box compounds to identify potential inhibitors against SARS-CoV-2 Mpro, using molecular docking and dynamics simulation studies

Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) Main protease (M^pro) is one of the vital drug targets amongst all the coronaviruses, as the protein is indispensable for virus replication. The study aimed to identify promising lead molecules against M^pro enzyme through virtual screening of Malaria Venture (MMV) Malaria Box (MB) comprising of 400 experimentally proven compounds. The binding affinities were studied using virtual screening based molecular docking, which revealed five molecules having the highest affinity scores compared to the reference molecules. Utilizing the established 3D structure of M^pro the binding affinity conformations of the docked complexes were studied by Molecular Dynamics (MD) simulations. The MD simulation trajectories were analysed to monitor protein deviation, relative fluctuation, atomic gyration, compactness covariance, residue-residue map and free energy landscapes. Based on the present study outcome, we propose three Malaria_box (MB) compounds, namely, MB_241, MB_250 and MB_266 to be the best lead compounds against M^pro activity. The compounds may be evaluated for their inhibitory activities using experimental techniques.