PAINS in the assay: chemical mechanisms of assay interference and promiscuous enzymatic inhibition observed during a sulfhydryl-scavenging HTS

An in-silico receptor-pharmacophore based multistep molecular docking and simulation study to evaluate the inhibitory potentials against NS1 of DENV-2

DENV-2 strain is the most fatal and infectious of the five dengue virus serotypes. The non-structural protein NS1 encoded by its genome is the most significant protein required for viral pathogenesis and replication inside the host body. Thus, targeting the NS1 protein and designing an inhibitor to limit its stability and secretion is a propitious attempt in our fight against dengue. Four novel inhibitors are designed to target the conserved cysteine residues (C55, C313, C316, and C329) and glycosylation sites (N130 and N207) of the NS1 protein in an attempt to halt the spread of the dengue infection in the host body altogether. Numerous computer-aided drug designing techniques including molecular docking, molecular dynamics simulation, virtual screening, principal component analysis, and dynamic cross-correlation matrix were employed to determine the structural and functional activity of the NS1-inhibitor complexes. From our analysis, it was evident that the extent of structural and atomic level fluctuations of the ligand-bound protein exhibited a declining trend in contrast to unbound protein which was prominently noticeable through the RMSD, RMSF, Rg, and SASA graphs. The ADMET analysis of the four ligands revealed a promising pharmacokinetics and pharmacodynamic profile, along with good bioavailability and toxicity properties. The proposed drugs when bound to the targeted cavities resulted in stable conformations in comparison to their unbound state, implying they have good affinity promising effective drug action. Thus, they can be tested in vitro and used as potential anti-dengue drugs.Communicated by Ramaswamy H. Sarma.

DeLA-DrugSelf: Empowering multi-objective de novo design through SELFIES molecular representation

In this paper, we introduce DeLA-DrugSelf, an upgraded version of DeLA-Drug [J. Chem. Inf. Model. 62 (2022) 1411-1424], which incorporates essential advancements for automated multi-objective de novo design. Unlike its predecessor, which relies on SMILES notation for molecular representation, DeLA-DrugSelf employs a novel and robust molecular representation string named SELFIES (SELF-referencing Embedded String). The generation process in DeLA-DrugSelf not only involves substitutions to the initial string representing the starting query molecule but also incorporates insertions and deletions. This enhancement makes DeLA-DrugSelf significantly more adept at executing data-driven scaffold decoration and lead optimization strategies. Remarkably, DeLA-DrugSelf explicitly addresses the SELFIES-related collapse issue, considering only collapse-free compounds during generation. These compounds undergo a rigorous quality metrics evaluation, highlighting substantial advancements in terms of drug-likeness, uniqueness, and novelty compared to the molecules generated by the previous version of the algorithm. To evaluate the potential of DeLA-DrugSelf as a mutational operator within a genetic algorithm framework for multi-objective optimization, we employed a fitness function based on Pareto dominance. Our objectives focused on target-oriented properties aimed at optimizing known cannabinoid receptor 2 (CB2R) ligands. The results obtained indicate that DeLA-DrugSelf, available as a user-friendly web platform (https://www.ba.ic.cnr.it/softwareic/delaself/), can effectively contribute to the data-driven optimization of starting bioactive molecules based on user-defined parameters.

Targeting Leishmania infantum Mannosyl-oligosaccharide glucosidase with natural products: potential pH-dependent inhibition explored through computer-aided drug design

Visceral Leishmaniasis (VL) is a serious public health issue, documented in more than ninety countries, where an estimated 500,000 new cases emerge each year. Regardless of novel methodologies, advancements, and experimental interventions, therapeutic limitations, and drug resistance are still challenging. For this reason, based on previous research, we screened natural products (NP) from Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBEDB), Mexican Compound Database of Natural Products (BIOFACQUIM), and Peruvian Natural Products Database (PeruNPDB) databases, in addition to structural analogs of Miglitol and Acarbose, which have been suggested as treatments for VL and have shown encouraging action against parasite's N-glycan biosynthesis. Using computer-aided drug design (CADD) approaches, the potential inhibitory effect of these NP candidates was evaluated by inhibiting the Mannosyl-oligosaccharide Glucosidase Protein (MOGS) from Leishmania infantum, an enzyme essential for the protein glycosylation process, at various pH to mimic the parasite's changing environment. Also, computational analysis was used to evaluate the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profile, while molecular dynamic simulations were used to gather information on the interactions between these ligands and the protein target. Our findings indicated that Ocotillone and Subsessiline have potential antileishmanial effects at pH 5 and 7, respectively, due to their high binding affinity to MOGS and interactions in the active center. Furthermore, these compounds were non-toxic and had the potential to be administered orally. This research indicates the promising anti-leishmanial activity of Ocotillone and Subsessiline, suggesting further validation through in vitro and in vivo experiments.

Tackling assay interference associated with small molecules

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

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.

Challenges for the Discovery of Non-Covalent WRN Helicase Inhibitors

The Werner Syndrome RecQ helicase (WRN) is a synthetic lethal target of interest for the treatment of cancers with microsatellite instability (MSI). Different hit finding approaches were initially tested. The identification of WRN inhibitors proved challenging due to a high propensity for artefacts via protein interference, i. e., hits inhibiting WRN enzymatic activities through multiple, unspecific mechanisms. Previously published WRN Helicase inhibitors (ML216, NSC19630 or NSC617145) were characterized in an extensive set of biochemical and biophysical assays and could be ruled out as specific WRN helicase probes. More innovative screening strategies need to be developed for successful drug discovery of non-covalent WRN helicase inhibitors.

Discovery and optimization of (2-naphthylthio)acetic acid derivative as selective Bfl-1 inhibitor

Bcl-2 anti-apoptotic protein family suppresses cell death by deploying a surface groove to capture the critical BH3 α-helix of pro-apoptotic members. Bfl-1 is a relatively understudied member of this family, though it has been implicated in the pathogenesis and chemoresistance of a variety of human cancers. Reported small molecular Bfl-1 inhibitors encountered the issue of either lack in potency or poor selectivity against its most homologous member Mcl-1. In order to tackle this issue, compound library was screened and a hit compound UMI-77 was identified. We modified its chemical structure to remove the characteristic of PAINS (pan-assay interference compounds), demonstrated the real binding affinity and achieved selectivity against Mcl-1 under the guidance of computational modeling. After optimization 15 was obtained as leading compound to block Bfl-1/BIM interaction in vitro with more than 10-fold selectivity over Mcl-1. We believe 15 is of great value for the exploration of Bfl-1 biological function and its potential as therapeutic target.

Machine learning-based virtual screening and molecular modelling studies for identification of butyrylcholinesterase inhibitors as anti-Alzheimer's agent

Butyrylcholinesterase (BChE) is a hydrolase involved in the metabolism and detoxification of specific esters in the blood. It is also implicated in the progression of Alzheimer's disease, a type of dementia. As the disease progresses, the level of BChE tends to increase, opting for a major role as an acetylcholine-degrading enzyme and surpassing the role of acetylcholinesterase. Hence, the development of BChE inhibitors could be beneficial for the latter stages of the disease. In the present study, machine learning (ML) models were developed and employed to identify new BChE inhibitors. Further, the identified molecules were subjected to molecular property filters. The filtered ligands were studied through molecular modelling techniques, viz. molecular docking and molecular dynamics (MD). Support vector machine-based ML models resulted in the identification of 3291 compounds that would have predicted IC50 values less than 200 nM. The docking study showed that compounds ART13069594, ART17350769 and LEG19710163 have mean binding energies of -9.62, -9.26 and -8.93 kcal/mol, respectively. The MD study displayed that all the selected ligands showed stable complexes with BChE. The trajectories of all the ligands were stable similar to the standard BChE inhibitors.Communicated by Ramaswamy H. Sarma.

The European Lead Factory: Results from a decade of collaborative, public-private, drug discovery programs

The European Lead Factory (ELF) is a consortium of universities and small and medium-sized enterprises (SMEs) dedicated to drug discovery, and the pharmaceutical industry. This unprecedented consortium provides high-throughput screening, triage, and hit validation, including to non-consortium members. The ELF library was created through a novel compound-sharing model between nine pharmaceutical companies and expanded through library synthesis by chemistry-specialized SMEs. The library has been screened against ∼270 different targets and 15 phenotypic assays, and hits have been developed to form the basis of patents and spin-off companies. Here, we review the outcome of screening campaigns of the ELF, including the performance and physicochemical properties of the library, identification of possible frequent hitter compounds, and the effectiveness of the compound-sharing model.

Pharmacological advances in anti-retroviral therapy for human immunodeficiency virus-1 infection: A comprehensive review

The discovery of anti-retroviral (ARV) drugs over the past 36 years has introduced various classes, including nucleoside/nucleotide reverse transcriptase inhibitors, non-nucleoside reverse transcriptase inhibitors, protease inhibitor, fusion, and integrase strand transfer inhibitors inhibitors. The introduction of combined highly active anti-retroviral therapies in 1996 was later proven to combat further ARV drug resistance along with enhancing human immunodeficiency virus (HIV) suppression. As though the development of ARV therapies was continuously expanding, the variation of action caused by ARV drugs, along with its current updates, was not comprehensively discussed, particularly for HIV-1 infection. Thus, a range of HIV-1 ARV medications is covered in this review, including new developments in ARV therapy based on the drug's mechanism of action, the challenges related to HIV-1, and the need for combination therapy. Optimistically, this article will consolidate the overall updates of HIV-1 ARV treatments and conclude the significance of HIV-1-related pharmacotherapy research to combat the global threat of HIV infection.

Structural basis for specific inhibition of salicylate synthase from Mycobacterium abscessus

Blocking iron uptake and metabolism has been emerging as a promising therapeutic strategy for the development of novel antimicrobial compounds. Like all mycobacteria, M. abscessus (Mab) has evolved several countermeasures to scavenge iron from host carrier proteins, including the production of siderophores, which play a crucial role in these processes. In this study, we solved, for the first time, the crystal structure of Mab-SaS, the first enzyme involved in the biosynthesis of siderophores. Moreover, we screened a small, focused library and identified a compound exhibiting a potent inhibitory effect against Mab-SaS (IC50 ≈ 2 μM). Its binding mode was investigated by means of Induced Fit Docking simulations, performed on the crystal structure presented herein. Furthermore, cytotoxicity data and pharmacokinetic predictions revealed the safety and drug-likeness of this class of compounds. Finally, the crystallographic data were used to optimize the model for future virtual screening campaigns. Taken together, the findings of our study pave the way for the identification of potent Mab-SaS inhibitors, based on both established and unexplored chemotypes.

In-silico guided chemical exploration of KDM4A fragments hits

BACKGROUND

Lysine demethylase enzymes (KDMs) are an emerging class of therapeutic targets, that catalyse the removal of methyl marks from histone lysine residues regulating chromatin structure and gene expression. KDM4A isoform plays an important role in the epigenetic dysregulation in various cancers and is linked to aggressive disease and poor clinical outcomes. Despite several efforts, the KDM4 family lacks successful specific molecular inhibitors.

RESULTS

Herein, starting from a structure-based fragments virtual screening campaign we developed a synergic framework as a guide to rationally design efficient KDM4A inhibitors. Commercial libraries were used to create a fragments collection and perform a virtual screening campaign combining docking and pharmacophore approaches. The most promising compounds were tested in-vitro by a Homogeneous Time-Resolved Fluorescence-based assay developed for identifying selective substrate-competitive inhibitors by means of inhibition of H3K9me3 peptide demethylation. 2-(methylcarbamoyl)isonicotinic acid was identified as a preliminary active fragment, displaying inhibition of KDM4A enzymatic activity. Its chemical exploration was deeply investigated by computational and experimental approaches which allowed a rational fragment growing process. The in-silico studies guided the development of derivatives designed as expansion of the primary fragment hit and provided further knowledge on the structure-activity relationship.

CONCLUSIONS

Our study describes useful insights into key ligand-KDM4A protein interaction and provides structural features for the development of successful selective KDM4A inhibitors.

Discovery of a fragment hit compound targeting D-Ala:D-Ala ligase of bacterial peptidoglycan biosynthesis

Bacterial resistance is an increasing threat to healthcare systems, highlighting the need for discovering new antibacterial agents. An established technique, fragment-based drug discovery, was used to target a bacterial enzyme Ddl involved in the biosynthesis of peptidoglycan. We assembled general and focused fragment libraries that were screened in a biochemical inhibition assay. Screening revealed a new fragment-hit inhibitor of DdlB with a Ki value of 20.7 ± 4.5 µM. Binding to the enzyme was confirmed by an orthogonal biophysical method, surface plasmon resonance, making the hit a promising starting point for fragment development.

The promising impact of Bemcentinib and Repotrectinib on sleep impairment in Alzheimer's disease

Alzheimer's disease (AD), the most prevalent neurodegenerative disease, demands effective medication to alleviate symptoms. This study focused on sleep impairment as an overt clinical symptom and tauopathy as a prominent molecular symptom of this disease. Multiple compounds from three biomolecule libraries (719 compounds; ChemDiv:366 - ChEMBL:180 - PubChem:173) were evaluated for potential binding affinity and safety using AutoDock Vina and pkCSM, respectively, resulting in the selection of four candidate compounds (Lestaurtinib, Repotrectinib, Bemcentinib, and Zotiraciclib). Due to the similarity of Repotrectinib and Bemcentinib binding sites to ATP, 300 ns Martini 3 coarse-grained molecular dynamics (MD) was performed on these two molecules and ATP by NAMD. The stability of tau protein in the presence of drugs was assessed using a 200 ns Martini 3 MD simulation. Binding site analysis discloses Bemcentinib and Repotrectinib as two inhibitors occupying most amino acids in binding with ATP. The RMSD and RMS average correlation results revealed protein containing Bemcentinib and Repotrectinib to have a more stable state compared to ATP in the first 220 ns simulation. There was only a single detachment of Bemcentinib, while Repotrictinib detached twice at the end of the simulation. Eventually, adding Bemcentinib and Repotrectinib to the enzyme-tau complex significantly increased the number of tau detachments during the 200 ns simulation. We report Bemcentinib and Repotrectinib, formerly prescribed for cancer, as potential inhibitors of the CK1 δ. Besides their high binding affinity compared to ATP, they can inhibit all ATP-binding sites and alter the tau binding stability.Communicated by Ramaswamy H. Sarma.

Design, synthesis, antiviral evaluation, and In silico studies of acrylamides targeting nsP2 from Chikungunya virus

The Togaviridae family comprises several New- and Old-World Alphaviruses that have been responsible for thousands of human illnesses, including the RNA arbovirus Chikungunya virus (CHIKV). Firstly, it was reported in Tanzania in 1952 but rapidly it spread to several countries from Europe, Asia, and the Americas. Since then, CHIKV has been circulating in diverse countries around the world, leading to increased morbidity rates. Currently, there are no FDA-approved drugs or licensed vaccines to specifically treat CHIKV infections. Thus, there is a lack of alternatives to fight against this viral disease, making it an unmet need. Structurally, CHIKV is composed of five structural proteins (E3, E2, E1, C, and 6k) and four non-structural proteins (nsP1-4), in which nsP2 represents an attractive antiviral target for designing novel inhibitors since it has an essential role in the virus replication and transcription. Herein, we used a rational drug design strategy to select some acrylamide derivatives to be synthesized and evaluated against CHIKV nsP2 and also screened on CHIKV-infected cells. Thus, two regions of modifications were considered for these types of inhibitors, based on a previous study of our group, generating 1560 possible inhibitors. Then, the 24 most promising ones were synthesized and screened by using a FRET-based enzymatic assay protocol targeting CHIKV nsP2, identifying LQM330, 333, 336, and 338 as the most potent inhibitors, with Ki values of 48.6 ± 2.8, 92.3 ± 1.4, 2.3 ± 1.5, and 181.8 ± 2.5 μM, respectively. Still, their Km and Vmax kinetic parameters were also determined, along with their competitive binding modes of CHIKV nsP2 inhibition. Then, ITC analyses revealed KD values of 127, 159, 198, and 218 μM for LQM330, 333, 336, and 338, respectively. Also, their ΔH, ΔS, and ΔG physicochemical parameters were determined. MD simulations demonstrated that these inhibitors present a stable binding mode with nsP2, interacting with important residues of this protease, according to docking analyzes. Moreover, MM/PBSA calculations displayed that van der Waals interactions are mainly responsible for stabilizing the inhibitor-nsP2 complex, and their binding energies corroborated with their Ki values, having -198.7 ± 15.68, -124.8 ± 17.27, -247.4 ± 23.78, and -100.6 ± 19.21 kcal/mol for LQM330, 333, 336, and 338, respectively. Since Sindbis (SINV) nsP2 is similar to CHIKV nsP2, these best inhibitors were screened against SINV-infected cells, and it was verified that LQM330 presented the best result, with an EC50 value of 0.95 ± 0.09 μM. Even at 50 μM concentration, LQM338 was found to be cytotoxic on Vero cells after 48 h. Then, LQM330, 333, and 336 were evaluated against CHIKV-infected cells in antiviral assays, in which LQM330 was found to be the most promising antiviral candidate in this study, exhibiting an EC50 value of 5.2 ± 0.52 μM and SI of 31.78. The intracellular flow cytometry demonstrated that LQM330 is able to reduce the CHIKV cytopathogenic effect on cells, and also reduce the percentage of CHIKV-positive cells from 66.1% ± 7.05 to 35.8% ± 5.78 at 50 μM concentration. Finally, qPCR studies demonstrated that LQM330 was capable of reducing the number of viral RNA copies/μL, suggesting that CHIKV nsP2 is targeted by this inhibitor as its mechanism of action.

In Silico Interactions of Natural and Synthetic Compounds with Key Proteins Involved in Alzheimer's Disease: Prospects for Designing New Therapeutics Compound

Memory impairment is a result of multiple factors including amyloid-beta (Aβ) accumulation. Several receptors are mediated for Aβ transport and signaling. Moreover, blood lipids are involved in Aβ signaling pathway through these receptors. Mediated blood lipid level by statins aims to regulate Aβ signaling cascade. First, the structure of receptors was taken from the RCSB PDB database and prepared with MGLTools and AutoDock tool 4. Second, the ligand was prepared for docking through AutoDock Vina. The binding affinity was calculated, and the binding sites were determined through LigPlot+ software. Besides, pharmacokinetic properties were calculated through multiple software. Finally, a molecular dynamics (MD) simulation was conducted to evaluate ligands stability along with clustering analysis to evaluate proteins connection. Our molecular docking and dynamic analyses revealed silymarin as a potential inhibitor of acetylcholinesterase (AChE), P-glycoprotein, and angiotensin-converting enzyme 2 (ACE2) with 0.704, 0.85, and 0.83 Å for RMSD along with -114.27, -107.44, and -122.51 kcal/mol for free binding energy, respectively. Moreover, rosuvastatin and quercetin have more stability compared to silymarin and donepezil in complex with P-glycoprotein and ACE2, respectively. Eventually, based on clustering and pharmacokinetics analysis, silymarin, rosuvastatin, and quercetin are suggested to be involved in peripheral clearance of Aβ. The bioactivity effects of mentioned statins and antioxidants are predicted to be helpful in treating memory impairment in Alzheimer's disease (AD). Nevertheless, mentioned drug effect could be improved by nanoparticles to facilitate penetration of the blood-brain barrier (BBB).

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.

Chromone-embedded peptidomimetics and furopyrimidines as highly potent SARS-CoV-2 infection inhibitors: docking and MD simulation study

BACKGROUND

COVID-19 is a respiratory illness caused by SARS-CoV-2. Pharmaceutical companies aim to control virus spread through effective drugs. This study investigates chromone compound derivatives' ability to inhibit viral entry and prevent replication.

METHOD

This study investigated the inhibitory effect of chromone-embedded peptidomimetics and furopyrimidines on 7BZ5 from Severe Acute Respiratory Syndrome CoV-2, Homo sapiens, and 6LU7 from Bat SARS-like CoV using molecular docking. The crystal structure of these proteins was obtained from the Protein Data Bank, and the inhibition site was determined using ligand binding interaction options. The 3D structure was protonated and energetically minimised using MOE software. Chromone derivatives were designed in three dimensions, and their energy was minimised using MOE 2019. The molecular drug-likeness was calculated using SwissADME, Lipinski and Benigni-Bossa's rule, and toxicity was calculated using Toxtree v3.1.0 software. Compounds with pharmacological properties were selected for molecular docking, and interactions were assessed using MOE 2019. MD simulations of Mpro-ch-p complexes were performed to evaluate root mean square fluctuations (RMSF) and measure protein stability.

RESULT

The pharmacokinetic tests revealed that chromone derivatives of the peptidomimetic family have acceptable pharmacokinetic activity in the human body. Some compounds, such as Ch-p1, Ch-p2, Ch-p6, Ch-p7, Ch-p12, and Ch-p13, have pronounced medicinal properties. Molecular docking revealed high affinity for binding to SARS-CoV-2 protease. Ch-p7 had the highest binding energy, likely due to its inhibitory property. A 10 ns molecular dynamics study confirmed the stability of the protein-ligand complex, resulting in minimal fluctuations in the system's backbone. The MM-GBSA analysis revealed free energies of binding of - 19.54 kcal/mol.

CONCLUSIONS

The study investigated the inhibition of viral replication using chromone derivatives, finding high inhibitory effects in the peptidomimetic family compared to other studies.

Idiopathic pulmonary fibrosis (IPF): disease pathophysiology, targets, and potential therapeutic interventions

Idiopathic pulmonary fibrosis (IPF) is a progressive, degenerative pulmonary condition. Transforming growth factor (TGF)-β, platelet-derived growth factor (PDGF), and tumor necrosis factor-α (TNF-α) are the major modulators of IPF that mediate myofibroblast differentiation and promote fibrotic remodeling of the lung. Cigarette smoke, asbestos fiber, drugs, and radiation are known to favor fibrotic remodeling of the lungs. Oxidative stress in the endoplasmic reticulum (ER) also leads to protein misfolding and promotes ER stress, which is predominant in IPF. This phenomenon further results in excess reactive oxygen species (ROS) aggregation, increasing oxidative stress. During protein folding in the ER, thiol groups on the cysteine residue are oxidized and disulfide bonds are formed, which leads to the production of hydrogen peroxide (H2O2) as a by-product. With the accumulation of misfolded proteins in the ER, multiple signaling cascades are initiated by the cell, collectively termed as the unfolded protein response (UPR). UPR also induces ROS production within the ER and mitochondria and promotes both pro-apoptotic and pro-survival pathways. The prevalence of post-COVID-19 pulmonary fibrosis (PCPF) is 44.9%, along with an alarming increase in "Coronavirus Disease 2019" (COVID-19) comorbidities. Fibrotic airway remodeling and declined lung function are the common endpoints of SARS-CoV-2 infection and IPF. Flavonoids are available in our dietary supplements and exhibit medicinal properties. Apigenin is a flavonoid found in plants, including chamomile, thyme, parsley, garlic, guava, and broccoli, and regulates several cellular functions, such as oxidative stress, ER stress, and fibrotic responses. In this study, we focus on the IPF and COVID-19 pathogenesis and the potential role of Apigenin in addressing disease progression.

Advanced virtual screening enables the discovery of a host-targeting and broad-spectrum antiviral agent

We employed an advanced virtual screening (AVS) approach to identify potential inhibitors of human dihydroorotate dehydrogenase (DHODH), a validated target for development of broad-spectrum antivirals. We screened a library of 495118 compounds and identified 495 compounds that exhibited better binding scores than the reference ligands involved in the screening. From the top 100 compounds, we selected 28 based on their consensus docking scores and structural novelty. Then, we conducted in vitro experiments to investigate the antiviral activity of selected compounds on HSV-1 infection, which is susceptible to DHODH inhibitors. Among the tested compounds, seven displayed statistically significant antiviral effects, with Comp 19 being the most potent inhibitor. We found that Comp 19 exerted its antiviral effect in a dose-dependent manner (IC50 = 1.1 μM) and exhibited the most significant antiviral effect when added before viral infection. In the biochemical assay, Comp 19 inhibited human DHODH in a dose-dependent manner with the IC50 value of 7.3 μM. Long-timescale molecular dynamics simulations (1000 ns) revealed that Comp 19 formed a very stable complex with human DHODH. Comp 19 also displayed broad-spectrum antiviral activity and suppressed cytokine production in THP-1 cells. Overall, our study provides evidence that AVS could be successfully implemented to discover novel DHODH inhibitors with broad-spectrum antiviral activity.

Practical guidelines for the use of gradient boosting for molecular property prediction

Decision tree ensembles are among the most robust, high-performing and computationally efficient machine learning approaches for quantitative structure-activity relationship (QSAR) modeling. Among them, gradient boosting has recently garnered particular attention, for its performance in data science competitions, virtual screening campaigns, and bioactivity prediction. However, different variants of gradient boosting exist, the most popular being XGBoost, LightGBM and CatBoost. Our study provides the first comprehensive comparison of these approaches for QSAR. To this end, we trained 157,590 gradient boosting models, which were evaluated on 16 datasets and 94 endpoints, comprising 1.4 million compounds in total. Our results show that XGBoost generally achieves the best predictive performance, while LightGBM requires the least training time, especially for larger datasets. In terms of feature importance, the models surprisingly rank molecular features differently, reflecting differences in regularization techniques and decision tree structures. Thus, expert knowledge must always be employed when evaluating data-driven explanations of bioactivity. Furthermore, our results show that the relevance of each hyperparameter varies greatly across datasets and that it is crucial to optimize as many hyperparameters as possible to maximize the predictive performance. In conclusion, our study provides the first set of guidelines for cheminformatics practitioners to effectively train, optimize and evaluate gradient boosting models for virtual screening and QSAR applications.

Exploring chemical space for lead identification by propagating on chemical similarity network

Motivation

Lead identification is a fundamental step to prioritize candidate compounds for downstream drug discovery process. Machine learning (ML) and deep learning (DL) approaches are widely used to identify lead compounds using both chemical property and experimental information. However, ML or DL methods rarely consider compound similarity information directly since ML and DL models use abstract representation of molecules for model construction. Alternatively, data mining approaches are also used to explore chemical space with drug candidates by screening undesirable compounds. A major challenge for data mining approaches is to develop efficient data mining methods that search large chemical space for desirable lead compounds with low false positive rate.

Results

In this work, we developed a network propagation (NP) based data mining method for lead identification that performs search on an ensemble of chemical similarity networks. We compiled 14 fingerprint-based similarity networks. Given a target protein of interest, we use a deep learning-based drug target interaction model to narrow down compound candidates and then we use network propagation to prioritize drug candidates that are highly correlated with drug activity score such as IC50. In an extensive experiment with BindingDB, we showed that our approach successfully discovered intentionally unlabeled compounds for given targets. To further demonstrate the prediction power of our approach, we identified 24 candidate leads for CLK1. Two out of five synthesizable candidates were experimentally validated in binding assays. In conclusion, our framework can be very useful for lead identification from very large compound databases such as ZINC.

Discovery of novel and selective SIK2 inhibitors by the application of AlphaFold structures and generative models

Salt-inducible kinase 2 (SIK2) has been recognized as a potential target for anti-inflammation and anti-cancer therapy. In this paper, based on the binding pose of the reported compound (GLPG-3970, 3) with AlphaFold protein structure, a series of hinge cores were generated via AI-generative models (Chemistry42). After the molecular docking, synthesis, and biological evaluation, a hit molecule (7f) targeting SIK2 was obtained with a novel scaffold. Further SAR exploration led to the discovery of compound 8g with superior potency against SIK2 compared with the reported inhibitors. Furthermore, 8g also demonstrated excellent selectivity over other AMPK kinases, favorable in vitro ADMET profiles and decent cellular activities. This work provides an alternative approach to the discovery of novel and selective kinase inhibitors.

Discovery of Novel SIRT1/2 Inhibitors with Effective Cytotoxicity against Human Leukemia Cells

The sirtuin enzyme family members, SIRT1 and SIRT2, play both tumor-promoting and tumor-suppressing roles, depending on the context and experimental conditions. Compounds that inhibit either SIRT1 or SIRT2 show promising antitumor effects in several types of cancer models, both in vitro and in vivo. The simultaneous inhibition of SIRT1 and SIRT2 is helpful in treating cancer by completely blocking p53 deacetylation, leading to cell death. However, only a few SIRT1/2 dual inhibitors have been developed. Here, we report the discovery of a novel series of SIRT1/2 dual inhibitors via a rational drug design that involved virtual screening and a substructure search. Eleven of the derived compounds exhibited high inhibitory activities, with IC50 < 5 μM and high specificity for both SIRT1 and SIRT2. Compounds hsa55 and PS9 strongly induced apoptosis and showed antiproliferative effects against human leukemia cell lines, which could be due to their ability to increase of p53 and α-tubulin acetylation, as we observed in MOLM-13 cells. Therefore, the new scaffolds of these compounds and their efficacy in leukemia cell lines provide important clues for the further development of novel anti-leukemia drugs.

In Silico Screening of Inhibitors of the Venezuelan Equine Encephalitis Virus Nonstructural Protein 2 Cysteine Protease

The Venezuelan equine encephalitis virus (VEEV) nonstructural protein 2 (nsP2) cysteine protease (EC 3.4.22.B79) is essential for viral replication. High throughput in silico/in vitro screening using a focused set of known cysteine protease inhibitors identified two epoxysuccinyl prodrugs, E64d and CA074 methyl ester (CA074me) and a reversible oxindole inhibitor. Here, we determined the X-ray crystal structure of the CA074-inhibited nsP2 protease and compared it with our E64d-inhibited structure. We found that the two inhibitors occupy different locations in the protease. We designed hybrid inhibitors with improved potency. Virus yield reduction assays confirmed that the viral titer was reduced by >5 logs with CA074me. Cell-based assays showed reductions in viral replication for CHIKV, VEEV, and WEEV, and weaker inhibition of EEEV by the hybrid inhibitors. The most potent was NCGC00488909-01 which had an EC₅₀ of 1.76 µM in VEEV-Trd-infected cells; the second most potent was NCGC00484087 with an EC₅₀ = 7.90 µM. Other compounds from the NCATS libraries such as the H1 antihistamine oxatomide (>5-log reduction), emetine, amsacrine an intercalator (NCGC0015113), MLS003116111-01, NCGC00247785-13, and MLS00699295-01 were found to effectively reduce VEEV viral replication in plaque assays. Kinetic methods demonstrated time-dependent inhibition by the hybrid inhibitors of the protease with NCGC00488909-01 (K_i = 3 µM) and NCGC00484087 (K_i = 5 µM). Rates of inactivation by CA074 in the presence of 6 mM CaCl₂, MnCl₂, or MgCl₂ were measured with varying concentrations of inhibitor, Mg²⁺ and Mn²⁺ slightly enhanced inhibitor binding (3 to 6-fold). CA074 inhibited not only the VEEV nsP2 protease but also that of CHIKV and WEEV.

Targeting multi-drug-resistant Acinetobacter baumannii: a structure-based approach to identify the promising lead candidates against glutamate racemase

CONTEXT

Acinetobacter baumannii, one of the critical ESKAPE pathogens, is a highly resilient, multi-drug-resistant, Gramnegative, rod-shaped, highly pathogenic bacteria. It is responsible for almost 1-2% of all hospital-borne infections in immunocompromised patients and causes community outbreaks. Because of its resilience and MDR characteristics, looking for new strategies to check the infections related to this pathogen becomes paramount. The enzymes involved in the peptidoglycan biosynthetic pathway are attractive and the most promising drug targets. They contribute to the formation of the bacterial envelope and help to maintain the rigidity and integrity of the cell. The MurI (glutamate racemase) is one of the crucial enzymes that aid in the formation of the pentapeptide responsible for the interlinkage of peptidoglycan chains. It converts L-glutamate to D-glutamate, which is required to synthesise the pentapeptide chain.

METHODS

In this study, the MurI protein of A. baumannii (strain AYE) was modelled and subjected to high-throughput virtual screening against the enamine-HTSC library, taking UDP-MurNAc-Ala binding site as the targeted site. Four ligand molecules, Z1156941329 (N-(1-methyl-2-oxo-3,4-dihydroquinolin-6-yl)-1-phenyl-3,4-dihydro-1H-isoquinoline-2-carboxamide), Z1726360919 (1-[2-[3-(benzimidazol-1-ylmethyl)piperidin-1-yl]-2-oxo-1-phenylethyl]piperidin-2-one), Z1920314754 (N-[[3-(3-methylphenyl)phenyl]methyl]-8-oxo-2,7-diazaspiro[4.4]nonane-2-carboxamide) and Z3240755352 (4R)-4-(2,5-difluorophenyl)-1-(4-fluorophenyl)-1,3a,4,5,7,7a-hexahydro-6H-pyrazolo[3,4-b]pyridin-6-one), were identified to be the lead candidates based on Lipinski's rule of five, toxicity, ADME properties, estimated binding affinity and intermolecular interactions. The complexes of these ligands with the protein molecule were then subjected to MD simulations to scrutinise their dynamic behaviour, structural stability and effects on protein dynamics. The molecular mechanics/Poisson-Boltzmann surface area-based binding free energy analysis was also performed to compute the binding free energy of protein-ligand complexes, which offered the following values -23.32 ± 3.04 kcal/mol, -20.67 ± 2.91kcal/mol, -8.93 ± 2.90 kcal/mol and -26.73 ± 2.95 kcal/mol for MurI-Z1726360919, MurI-Z1156941329, MurI-Z3240755352 and MurI-Z3240755354 complexes respectively. Together, the results from various computational analyses utilised in this study proposed that Z1726360919, Z1920314754 and Z3240755352 could act as potential lead molecules to suppress the function of MurI protein from Acinetobacter baumannii.

Discovery of quinazolin-4-one-based non-covalent inhibitors targeting the severe acute respiratory syndrome coronavirus 2 main protease (SARS-CoV-2 Mpro)

The COVID-19 pandemic caused by SARS-CoV-2 continues to pose a great threat to public health while various vaccines are available worldwide. Main protease (M^pro) has been validated as an effective anti-COVID-19 drug target. Using medicinal chemistry and rational drug design strategies, we identified a quinazolin-4-one series of nonpeptidic, noncovalent SARS-CoV-2 M^pro inhibitors based on baicalein, 5,6,7-trihydroxy-2-phenyl-4H-chromen-4-one. In particular, compound C7 exhibits superior inhibitory activity against SARS-CoV-2 M^pro relative to baicalein (IC₅₀ = 0.085 ± 0.006 and 0.966 ± 0.065 μM, respectively), as well as improved physicochemical and drug metabolism and pharmacokinetics (DMPK) properties. In addition, C7 inhibits viral replication in SARS-CoV-2-infected Vero E6 cells more effectively than baicalein (EC₅₀ = 1.10 ± 0.12 and 5.15 ± 1.64 μM, respectively) with low cytotoxicity (CC₅₀ ＞ 50 μM). An X-ray co-crystal structure reveals a non-covalent mechanism of action, and a noncanonical binding mode not observed by baicalein. These results suggest that C7 represents a promising lead for development of more effective SARS-CoV-2 M^pro inhibitors and anti-COVID-19 drugs.

Identification of multidentate tyrosyl-DNA phosphodiesterase 1 (TDP1) inhibitors that simultaneously access the DNA, protein and catalytic-binding sites by oxime diversification

Tyrosyl-DNA phosphodiesterase 1 (TDP1) is a member of the phospholipase D family that can downregulate the anticancer effects of the type I topoisomerase (TOP1) inhibitors by hydrolyzing the 3'-phosphodiester bond between DNA and the TOP1 residue Y723 in the critical stalled intermediate that is the foundation of TOP1 inhibitor mechanism of action. Thus, TDP1 antagonists are attractive as potential enhancers of TOP1 inhibitors. However, the open and extended nature of the TOP1-DNA substrate-binding region has made the development of TDP1 inhibitors extremely challenging. In this study, starting from our recently identified small molecule microarray (SMM)-derived TDP1-inhibitory imidazopyridine motif, we employed a click-based oxime protocol to extend the parent platform into the DNA and TOP1 peptide substrate-binding channels. We applied one-pot Groebke-Blackburn-Bienayme multicomponent reactions (GBBRs) to prepare the needed aminooxy-containing substrates. By reacting these precursors with approximately 250 aldehydes in microtiter format, we screened a library of nearly 500 oximes for their TDP1 inhibitory potencies using an in vitro florescence-based catalytic assay. Select hits were structurally explored as their triazole- and ether-based isosteres. We obtained crystal structures of two of the resulting inhibitors bound to the TDP1 catalytic domain. The structures reveal that the inhibitors form hydrogen bonds with the catalytic His-Lys-Asn triads ("HKN" motifs: H263, K265, N283 and H493, K495, N516), while simultaneously extending into both the substrate DNA and TOP1 peptide-binding grooves. This work provides a structural model for developing multivalent TDP1 inhibitors capable of binding in a tridentate fashion with a central component situated within the catalytic pocket and extensions that project into both the DNA and TOP1 peptide substrate-binding regions.

High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery

Due to the continued emergence of resistance and a lack of new and promising antibiotics, bacterial infection has become a major public threat. High-throughput screening (HTS) allows rapid screening of a large collection of molecules for bioactivity testing and holds promise in antibacterial drug discovery. More than 50% of the antibiotics that are currently available on the market are derived from natural products. However, with the easily discoverable antibiotics being found, finding new antibiotics from natural sources has seen limited success. Finding new natural sources for antibacterial activity testing has also proven to be challenging. In addition to exploring new sources of natural products and synthetic biology, omics technology helped to study the biosynthetic machinery of existing natural sources enabling the construction of unnatural synthesizers of bioactive molecules and the identification of molecular targets of antibacterial agents. On the other hand, newer and smarter strategies have been continuously pursued to screen synthetic molecule libraries for new antibiotics and new druggable targets. Biomimetic conditions are explored to mimic the real infection model to better study the ligand-target interaction to enable the designing of more effective antibacterial drugs. This narrative review describes various traditional and contemporaneous approaches of high-throughput screening of natural products and synthetic molecule libraries for antibacterial drug discovery. It further discusses critical factors for HTS assay design, makes a general recommendation, and discusses possible alternatives to traditional HTS of natural products and synthetic molecule libraries for antibacterial drug discovery.

Evaluating the utility of a high throughput thiol-containing fluorescent probe to screen for reactivity: A case study with the Tox21 library

High-throughput screening (HTS) assays for bioactivity in the Tox21 program aim to evaluate an array of different biological targets and pathways, but a significant barrier to interpretation of these data is the lack of high-throughput screening (HTS) assays intended to identify non-specific reactive chemicals. This is an important aspect for prioritising chemicals to test in specific assays, identifying promiscuous chemicals based on their reactivity, as well as addressing hazards such as skin sensitisation which are not necessarily initiated by a receptor-mediated effect but act through a non-specific mechanism. Herein, a fluorescence-based HTS assay that allows the identification of thiol-reactive compounds was used to screen 7,872 unique chemicals in the Tox21 10K chemical library. Active chemicals were compared with profiling outcomes using structural alerts encoding electrophilic information. Random Forest classification models based on chemical fingerprints were developed to predict assay outcomes and evaluated through 10-fold stratified cross validation (CV). The mean CV Balanced Accuracy of the validation set was 0.648. The model developed shows promise as a tool to screen untested chemicals for their potential electrophilic reactivity based solely on chemical structural features.

Reference compounds for characterizing cellular injury in high-content cellular morphology assays

Robust, generalizable approaches to identify compounds efficiently with undesirable mechanisms of action in complex cellular assays remain elusive. Such a process would be useful for hit triage during high-throughput screening and, ultimately, predictive toxicology during drug development. Here we generate cell painting and cellular health profiles for 218 prototypical cytotoxic and nuisance compounds in U-2 OS cells in a concentration-response format. A diversity of compounds that cause cellular damage produces bioactive cell painting morphologies, including cytoskeletal poisons, genotoxins, nonspecific electrophiles, and redox-active compounds. Further, we show that lower quality lysine acetyltransferase inhibitors and nonspecific electrophiles can be distinguished from more selective counterparts. We propose that the purposeful inclusion of cytotoxic and nuisance reference compounds such as those profiled in this resource will help with assay optimization and compound prioritization in complex cellular assays like cell painting.

Discovery, synthesis and mechanism study of 2,3,5-substituted [1,2,4]-thiadiazoles as covalent inhibitors targeting 3C-Like protease of SARS-CoV-2

The 3C-like protease (3CL^pro) is essential for the replication and transcription of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), making it a promising target for the treatment of corona virus disease 2019 (COVID-19). In this study, a series of 2,3,5-substituted [1,2,4]-thiadiazole analogs were discovered to be able to inhibit 3CL^pro as non-peptidomimetic covalent binders at submicromolar levels, with IC₅₀ values ranging from 0.118 to 0.582 μM. Interestingly, these compounds were also shown to inhibit PL^pro with the same level of IC₅₀ values, but had negligible effect on proteases such as chymotrypsin, cathepsin B, and cathepsin L. Subsequently, the antiviral abilities of these compounds were evaluated in cell-based assays, and compound 6g showed potent antiviral activity with an EC₅₀ value of 7.249 μM. It was proposed that these compounds covalently bind to the catalytic cysteine 145 via a ring-opening metathesis reaction mechanism. To understand this covalent-binding reaction, we chose compound 6a, one of the identified hit compounds, as a representative to investigate the reaction mechanism in detail by combing several computational predictions and experimental validation. The process of ring-opening metathesis was theoretically studied using quantum chemistry calculations according to the transition state theory. Our study revealed that the 2,3,5-substituted [1,2,4]-thiadiazole group could covalently modify the catalytic cysteine in the binding pocket of 3CL^pro as a potential warhead. Moreover, 6a was a known GPCR modulator, and our study is also a successful computational method-based drug-repurposing study.

Targeting Siderophore-Mediated Iron Uptake in M. abscessus: A New Strategy to Limit the Virulence of Non-Tuberculous Mycobacteria

Targeting pathogenic mechanisms, rather than essential processes, represents a very attractive approach for the development of new antimycobacterial drugs. In this context, iron acquisition routes have recently emerged as potentially druggable pathways. However, the importance of siderophore biosynthesis in the virulence and pathogenicity of M. abscessus (Mab) is still poorly understood. In this study, we investigated the Salicylate Synthase (SaS) of Mab as an innovative molecular target for the development of inhibitors of siderophore production. Notably, Mab-SaS does not have any counterpart in human cells, making it an interesting candidate for drug discovery. Starting from the analysis of the binding of a series of furan-based derivatives, previously identified by our group as inhibitors of MbtI from M. tuberculosis (Mtb), we successfully selected the lead compound 1, exhibiting a strong activity against Mab-SaS (IC₅₀ ≈ 5 µM). Computational studies characterized the key interactions between 1 and the enzyme, highlighting the important roles of Y387, G421, and K207, the latter being one of the residues involved in the first step of the catalytic reaction. These results support the hypothesis that 5-phenylfuran-2-carboxylic acids are also a promising class of Mab-SaS inhibitors, paving the way for the optimization and rational design of more potent derivatives.

Probing the bioactive compounds of Kigelia africana as novel inhibitors of TNF-α converting enzyme using HPLC/GCMS analysis, FTIR and molecular modelling

Tumor Necrosis Factor Alpha Converting Enzyme (TACE) mediates inflammatory disorder and contributes to the pathophysiology of a variety of illnesses, such as chronic inflammation and cancer. This study identified metabolites in solvent extracts of Kigelia africana as putative TACE inhibitors due to the plant's known anti-inflammatory properties. HPLC-MS/GCMS analysis was used to characterize tentative phytochemicals from K. africana. The identified metabolites (n = 123) were docked with TACE to reveal the lead compounds. Binding free energy, ADMET prediction, molecular dynamics simulation at 100 ns, and DFT calculation were further conducted. The results revealed that K. africana contains sterol, phenols, alkaloids, terpenes and flavonoids. The FTIR shows that the extracts had peaks that correspond to the presence of different functional groups. The quantum polarized ligand docking (QPLD) analysis identified compound (n = 3) with binding affinity higher than standard compound IK-682. The hits also had modest ADMET profiles, interacted with essential residues within TACE binding pockets, and formed stable complexes with the protein. The 100 ns MD simulation shows that the compounds formed fairly stable interactions and complex with the protein as evidenced through RMSF, RMSD and MM-GBA results. The HOMO/LUMO, global descriptive molecular electrostatic potential Fukui function aid in the identification of the compounds' atomic sites prone to electrophilic/neutrophilic attacks, and non-covalent interactions. This study suggests that K. africana's bioactive compounds are capable of mitigating inflammation by inhibiting TACE.Communicated by Ramaswamy H. Sarma.

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of "nonbioavailable substructures", referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability.

Discovery of allosteric SHP2 inhibitors through ensemble-based consensus molecular docking, endpoint and absolute binding free energy calculations

SHP2 (Src homology-2 domain-containing protein tyrosine phosphatase-2) is a cytoplasmic protein -tyrosine phosphatase encoded by the gene PTPN11. It plays a crucial role in regulating cell growth and differentiation. Specifically, SHP2 is an oncoprotein associated with developmental pathologies and several different cancer types, including gastric, leukemia and breast cancer and is of great therapeutic interest. Given these roles, current research efforts have focused on developing SHP2 inhibitors. Allosteric SHP2 inhibitors have been shown to be more selective and pharmacologically appealing compared to competitive catalytic inhibitors targeting SHP2. Nevertheless, there remains a need for novel allosteric inhibitor scaffolds targeting SHP2 to develop compounds with improved selectivity, cell permeability, and bioavailability. Towards this goal, this study applied various computational tools to screen over 6 million compounds against the allosteric site within SHP2. The top-ranked hits from our in-silico screening were validated using protein thermal shift and biolayer interferometry assays, revealing three potent compounds. Kinetic binding assays were employed to measure the binding affinities of the top-ranked compounds and demonstrated that they all bind to SHP2 with a nanomolar affinity. Hence the compounds and the computational workflow described herein provide an effective approach for identifying and designing a generation of improved allosteric inhibitors of SHP2.

The Highly Potent AhR Agonist Picoberin Modulates Hh-Dependent Osteoblast Differentiation

Identification and analysis of small molecule bioactivity in target-agnostic cellular assays and monitoring changes in phenotype followed by identification of the biological target are a powerful approach for the identification of novel bioactive chemical matter in particular when the monitored phenotype is disease-related and physiologically relevant. Profiling methods that enable the unbiased analysis of compound-perturbed states can suggest mechanisms of action or even targets for bioactive small molecules and may yield novel insights into biology. Here we report the enantioselective synthesis of natural-product-inspired 8-oxotetrahydroprotoberberines and the identification of Picoberin, a low picomolar inhibitor of Hedgehog (Hh)-induced osteoblast differentiation. Global transcriptome and proteome profiling revealed the aryl hydrocarbon receptor (AhR) as the molecular target of this compound and identified a cross talk between Hh and AhR signaling during osteoblast differentiation.

Tuning gradient boosting for imbalanced bioassay modelling with custom loss functions

While in the last years there has been a dramatic increase in the number of available bioassay datasets, many of them suffer from extremely imbalanced distribution between active and inactive compounds. Thus, there is an urgent need for novel approaches to tackle class imbalance in drug discovery. Inspired by recent advances in computer vision, we investigated a panel of alternative loss functions for imbalanced classification in the context of Gradient Boosting and benchmarked them on six datasets from public and proprietary sources, for a total of 42 tasks and 2 million compounds. Our findings show that with these modifications, we achieve statistically significant improvements over the conventional cross-entropy loss function on five out of six datasets. Furthermore, by employing these bespoke loss functions we are able to push Gradient Boosting to match or outperform a wide variety of previously reported classifiers and neural networks. We also investigate the impact of changing the loss function on training time and find that it increases convergence speed up to 8 times faster. As such, these results show that tuning the loss function for Gradient Boosting is a straightforward and computationally efficient method to achieve state-of-the-art performance on imbalanced bioassay datasets without compromising on interpretability and scalability.

Graphical Abstract

Using Advanced Bioinformatics Tools to Identify Novel Therapeutic Candidates for Age-Related Macular Degeneration

Purpose

Age-related macular degeneration (AMD) is the most common cause of aging-related blindness in the developing world. Although medications can slow progressive wet AMD, currently, no drugs to treat dry-AMD are available. We use a systems or in silico biology analysis to identify chemicals and drugs approved by the Food and Drug Administration for other indications that can be used to treat and prevent AMD.

Methods

We queried National Center for Biotechnology Information to identify genes associated with AMD, wet AMD, dry AMD, intermediate AMD, and geographic atrophy to date. We combined genes from various AMD subtypes to reflect distinct stages of disease. Enrichment analysis using the ToppGene platform predicted molecules that can influence AMD genes. Compounds without clinical indications or with deleterious effects were manually filtered.

Results

We identified several drug/chemical classes that can affect multiple genes involved in AMD. The drugs predicted from this analysis include antidiabetics, lipid-lowering agents, and antioxidants, which could theoretically be repurposed for AMD. Metformin was identified as the drug with the strongest association with wet AMD genes and is among the top candidates in all dry AMD subtypes. Curcumin, statins, and antioxidants are also among the top drugs correlating with AMD-risk genes.

Conclusions

We use a systematic computational process to discover potential therapeutic targets for AMD. Our systematic and unbiased approach can be used to guide targeted preclinical/clinical studies for AMD and other ocular diseases.

Translational Relevance

Advanced bioinformatics models identify novel chemicals and approved drug candidates that can be efficacious for different subtypes of AMD.

In Silico Screening of Quorum Sensing Inhibitor Candidates Obtained by Chemical Similarity Search

Quorum sensing (QS) is a bacterial communication using signal molecules, by which they sense population density of their own species, leading to group behavior such as biofilm formation and virulence. Autoinducer-2 (AI2) is a QS signal molecule universally used by both gram-positive and gram-negative bacteria. Inhibition of QS mediated by AI2 is important for various practical applications, including prevention of gum-disease caused by biofilm formation of oral bacteria. In this research, molecular docking and molecular dynamics (MD) simulations were performed for molecules that are chemically similar to known AI2 inhibitors that might have a potential to be quorum sensing inhibitors. The molecules that form stable complexes with the AI2 receptor protein were found, suggesting that they could be developed as a novel AI2 inhibitors after further in vitro validation. The result suggests that combination of ligand-based drug design and computational methods such as MD simulation, and experimental verification, may lead to development of novel AI inhibitor, with a broad range of practical applications.

Pharmacophore-based virtual screening, molecular docking and molecular dynamics simulations study for the identification of LIM kinase-1 inhibitors

LIM kinases (LIMKs) are a family of protein kinases involved in the regulation of actin dynamics. There are two isoforms of LIMKs i.e., LIMK1 and LIMK2. LIMK1 is expressed abundantly in neuronal tissues. LIMK1 plays an essential role in the degradation of dendritic spines, which are important for our higher brain functions, such as memory and learning. The inhibition of LIMK1 improves the size and density of dendritic spines and acts as a protective effect against Alzheimer's disease. In this study, we have adopted ligand-based drug design and molecular modelling methods to identify virtual hits. The pharmacophoric features of PF-00477736 were used to screen the Zinc15 compounds library. The identified hits were then passed through drug-likeliness and PAINS filters. Further, comprehensive docking and rigorous molecular dynamics simulation study afforded three virtual hits viz., ZINC504485634, ZINC16940431 and ZINC1091071. The hits showed a better docking score than the standard ligand, PF-00477736. The docking score was found to be -8.85, -7.50 and -7.68 kcal/mol. These hits exhibited optimal binding properties with the target in docking study, blood-brain barrier permeability, in silico pharmacokinetics and low predicted toxicity.Communicated by Ramaswamy H. Sarma.

High-throughput detection of metal contamination in HTS outputs

Large compound libraries utilised for HTS often include metal contaminated compounds which can interfere with assay signal or target biology, and therefore appear as hits. Pursuit of these compounds can divert considerable time and resource away from more propitious hits, yet there is currently no established method of detecting metal impurities in a rapid and effective manner. Here we describe the development and application of a high-throughput method to identify metal contaminants using acoustic mist ionisation mass spectrometry (AMI-MS). Although metals species by themselves are not detectable by AMI-MS, we have identified two compounds that chelate metal ions and enable their detection. 6-(diethylamino)-1,3,5-triazine-2,4(1H,3H)-dithione (DMT) and 1-(3-{[4-(4-cyanophenyl)-1-piperidinyl]carbonyl}-4-methylphenyl)-3-ethylthiourea (TU) can form complexes with a range of metal ions. Using a collection of metal catalysts, we have developed two metal chelator assays that collectively allow for the detection of Ag, Au, Co, Cu, Fe, Pd, Pt and Zn. We employed these assays to profile the hit outputs of a Zn liable target, and a Pd liable target, and identified significant quantities of metal contaminated compounds in the HTS outputs. This work provides a method of rapidly identifying metal impurities in hit compounds and has become part of an established workflow in triaging HTS outputs at AstraZeneca, facilitating faster identification of robust lead series.

Progress and Challenges in Targeting the SARS-CoV-2 Papain-like Protease

SARS-CoV-2 is the causative agent of the COVID-19 pandemic. The approval of vaccines and small-molecule antivirals is vital in combating the pandemic. The viral polymerase inhibitors remdesivir and molnupiravir and the viral main protease inhibitor nirmatrelvir/ritonavir have been approved by the U.S. FDA. However, the emergence of variants of concern/interest calls for additional antivirals with novel mechanisms of action. The SARS-CoV-2 papain-like protease (PLpro) mediates the cleavage of viral polyprotein and modulates the host's innate immune response upon viral infection, rendering it a promising antiviral drug target. This Perspective highlights major achievements in structure-based design and high-throughput screening of SARS-CoV-2 PLpro inhibitors since the beginning of the pandemic. Encouraging progress includes the design of non-covalent PLpro inhibitors with favorable pharmacokinetic properties and the first-in-class covalent PLpro inhibitors. In addition, we offer our opinion on the knowledge gaps that need to be filled to advance PLpro inhibitors to the clinic.

Biophysical Survey of Small-Molecule β-Catenin Inhibitors: A Cautionary Tale

The canonical Wingless-related integration site signaling pathway plays a critical role in human physiology, and its dysregulation can lead to an array of diseases. β-Catenin is a multifunctional protein within this pathway and an attractive yet challenging therapeutic target, most notably in oncology. This has stimulated the search for potent small-molecule inhibitors binding directly to the β-catenin surface to inhibit its protein–protein interactions and downstream signaling. Here, we provide an account of the claimed (and some putative) small-molecule ligands of β-catenin from the literature. Through in silico analysis, we show that most of these molecules contain promiscuous chemical substructures notorious for interfering with screening assays. Finally, and in line with this analysis, we demonstrate using orthogonal biophysical techniques that none of the examined small molecules bind at the surface of β-catenin. While shedding doubts on their reported mode of action, this study also reaffirms β-catenin as a prominent target in drug discovery.

A Set of Experimentally Validated Decoys for the Human CC Chemokine Receptor 7 (CCR7) Obtained by Virtual Screening

We present a state-of-the-art virtual screening workflow aiming at the identification of novel CC chemokine receptor 7 (CCR7) antagonists. Although CCR7 is associated with a variety of human diseases, such as immunological disorders, inflammatory diseases, and cancer, this target is underexplored in drug discovery and there are no potent and selective CCR7 small molecule antagonists available today. Therefore, computer-aided ligand-based, structure-based, and joint virtual screening campaigns were performed. Hits from these virtual screenings were tested in a CCL19-induced calcium signaling assay. After careful evaluation, none of the in silico hits were confirmed to have an antagonistic effect on CCR7. Hence, we report here a valuable set of 287 inactive compounds that can be used as experimentally validated decoys.

Selective optogenetic control of Gq signaling using human Neuropsin

G_q proteins are universally important for signal transduction in mammalian cells. The underlying kinetics and transformation from extracellular stimuli into intracellular signaling, however could not be investigated in detail so far. Here we present the human Neuropsin (hOPN5) for specific and repetitive manipulation of G_q signaling in vitro and in vivo with high spatio-temporal resolution. Properties and G protein specificity of hOPN5 are characterized by UV light induced IP₃ generation, Ca²⁺ transients and inhibition of G_IRK channel activity in HEK cells. In adult hearts from a transgenic animal model, light increases the spontaneous beating rate. In addition, we demonstrate light induced contractions in the small intestine, which are not detectable after pharmacological G_q protein block. All-optical high-throughput screening for TRPC6 inhibitors is more specific and sensitive than conventional pharmacological screening. Thus, we demonstrate specific G_q signaling of hOPN5 and unveil its potential for optogenetic applications.

An extensive computational study to identify potential inhibitors of Acyl-homoserine-lactone synthase from Acinetobacter baumannii (strain AYE)

A member of the ESKAPE family of pathogens, A. baumannii, is an opportunistic gram-negative multidrug-resistant bacterium. A. baumannii is a ubiquitous coccobacillus involved in various hospital-related infections such as wound infections, pneumonia, urinary tract infections, septicaemia, endocarditis and ventilator assisted pneumonia and accounts for approximately 1-2% of all nosocomial bloodstream infections; hence it becomes imperative to identify potential therapeutic agents against the dreadful pathogen. The quorum-sensing pathway becomes an attractive drug target due to its role in biofilm regulation and formation, which provides the bacteria insulation from the harsh environment. A crucial protein in biofilm formation and regulation is Acyl-homoserine-lactone synthase (AHLS), responsible for producing signal molecules that trigger the signalling pathway for biofilm formation and regulation. The current study modeled the three-dimensional structure of AHLS in A. baumannii (strain AYE) followed by high-throughput virtual screening of the enamine-AC small-molecule database to identify lead molecules against its acylated-ACP (Acyl Carrier Protein) substrate-binding site. Based on the estimated binding affinity, estimated inhibition constant, ADME analysis and interaction patterns of the screened molecules, three lead candidates (Z815888654, Z2416029019, Z3766992625) were identified along with a control molecule (J8-C8). These molecules were then subjected to molecular dynamics simulations where the physiological effect of ligand binding on the protein was virtually predicted and analysed. The MM/PBSA based binding free energy calculations showed favourable results for Z815888654 (-22.77 ± 2.94 kcal/mol), Z2416029019 (-33.68 ± 2.63 kcal/mol), Z3766992625 (-21.44 ± 3.40 kcal/mol). The study employed global and essential dynamics analyses, MM/PBSA based binding free energy, free energy landscape and dynamic cross-correlation matrix to suggest Z815888654, Z2416029019 and Z3766992625 as potential inhibitors against the acylated-ACP substrate-binding site in AHLS from A. baumannii.