Virtual Screening Research Articles

AlphaFold Meets De Novo Drug Design: Leveraging Structural Protein Information in Multitarget Molecular Generative Models.

Recent advancements in deep learning and generative models have significantly expanded the applications of virtual screening for drug-like compounds. Here, we introduce a multitarget transformer model, PCMol, that leverages the latent protein embeddings derived from AlphaFold2 as a means of conditioning a de novo generative model on different targets. Incorporating rich protein representations allows the model to capture their structural relationships, enabling the chemical space interpolation of active compounds and target-side generalization to new proteins based on embedding similarities. In this work, we benchmark against other existing target-conditioned transformer models to illustrate the validity of using AlphaFold protein representations over raw amino acid sequences. We show that low-dimensional projections of these protein embeddings cluster appropriately based on target families and that model performance declines when these representations are intentionally corrupted. We also show that the PCMol model generates diverse, potentially active molecules for a wide array of proteins, including those with sparse ligand bioactivity data. The generated compounds display higher similarity known active ligands of held-out targets and have comparable molecular docking scores while maintaining novelty. Additionally, we demonstrate the important role of data augmentation in bolstering the performance of generative models in low-data regimes. Software package and AlphaFold protein embeddings are freely available at https://github.com/CDDLeiden/PCMol.

Identification of compounds from natural Peruvian sources as potential inhibitors of SARS-CoV-2 Mpro mutations by virtual screening and computational simulations

Background Although the COVID-19 pandemic has diminished in intensity, the virus continues to circulate globally. The SARS-CoV-2 main protease (Mpro) is a key enzyme in the life cycle of the virus, making it important for the development of treatments against future variants of the virus. In this work, Peruvian natural compounds were evaluated against different mutations of the SARS-CoV-2 Mpro. Methods In silico techniques such as virtual screening, all-atom molecular dynamics simulations, and energy estimation analysis were applied. Results Of the tested compounds by virtual screening, rutin was identified as the best binding agent against the different proposed Mpro mutations. In addition, computational simulations and energy estimation analysis demonstrated the high structural and energetic stability between the Mpro-rutin systems. Conclusions Overall, our study identified rutin as the most promising compound with a strong affinity for various Mpro mutations, potentially playing a key role in the development of new treatments for emerging viral variants.

Repurposed pharmacotherapy: targeting cathepsin L with repurposed drugs in virtual screening.

Proteolytic enzymes are closely associated with cancer and are important in different phases, including tumor growth, angiogenesis, and metastasis. Despite efforts to target matrix metalloproteases (MMPs), clinical trials have often resulted in various side effects such as musculoskeletal pain, joint stiffness, and tendinitis, making them less optimal for chronic cancer treatment. Thus, there is a need for the identification of other protease targets that would provide different approaches towards the management of cancer. Of these targets, Cathepsin L (CatL) is a lysosomal cysteine protease that has been identified as a therapeutic target that is implicated in cancer development and metastasis. In this study, we performed an integrated approach of virtual screening and molecular dynamics (MD) simulations to identify the potential inhibitors of CatL from a library of drugs that have been used for different treatments. Towards this goal, we performed virtual screening of the DrugBank database and found two repurposed drugs, Irinotecan and Nilotinib, against CatL based on their docking profiles, favorable docking scores, and specific interaction with the CatL binding pocket. MD simulations of the Irinotecan and Nilotinib bound structures with CatL were carried out, and the analysis showed that both these compounds could function as CatL inhibitors as the protein-ligand interactions were stable for 300 ns. This study highlights the robustness of these drugs bound to CatL and indicates that they could be repurposed for the treatment of cancer. These findings endorse the use of computer-based approaches for the identification of new inhibitors, and the present study will be a useful resource for future experimental research towards the targeting of CatL in cancer therapeutics.

An Extensive Computational Investigation of Mycobacterium tuberculosis Pantothenate Synthetase Inhibitors from Diverse‐Lib Compounds Library

AbstractAntibiotics have played a crucial role in significantly reducing the incidence of tuberculosis (TB) infection worldwide. Even before the mid‐20th century, the mortality rate of TB onset within five years was around 50 %. So, the introduction of antibiotics has changed the scenario of TB from a serious threat to a manageable one. However, the emergence of resistance to anti‐TB drugs poses a significant challenge. So, to overcome this situation the therapeutic approaches and drug targets need to be reformed. This study focused on finding potential inhibitors by targeting Pantothenate Synthetase, a crucial enzyme for Mycobacterium tuberculosis (Mtb) survival, through computational drug discovery methods. Molecular docking and virtual screening were employed to identify potential inhibitors from Diverse‐lib. Four compounds, namely CID2813602, 24357538, CID753354, and CID4798023, exhibited strong binding energies and stable interaction with the target protein. Further assessment of these compounds through MD simulation and Post MD simulation showed significant dynamic stability. The minimum energy transition calculated using the free energy landscape analysis of these compounds when docked with Pantothenate Synthetase confirmed the stability of each complex due to its minimum energy production. The free binding energy calculation of each complex also showed the intramolecular interaction contributes to the strong binding affinity of the compounds within the enzyme's active site clarifying their mechanisms of action. This research showcases the effectiveness of computational methods in promptly identifying potential anti‐TB drugs, paving the way for future experimental validation and optimization. It holds promise for the development of new treatments targeting drug‐resistant TB strains.

The Application and Challenges of Artificial Intelligence in G Protein-Coupled Receptor Drug Virtual Screening

G protein-coupled receptors play important roles in various diseases and are key targets for drug discovery. Virtual screening has traditionally been used to identify potential drug candidates from large compound libraries but faces challenges in computational resources and time constraints. The integration of artificial intelligence technologies has improved screening precision and reduced costs. This review discusses current developments, challenges, and future directions in leveraging artificial intelligence to advance GPCR drug screening.

Potential novel HIV-1 reverse transcriptase inhibitors: a modeling and evaluation approach

Human immunodeficiency viru (HIV) is the causative agent of acquired immunodeficiency syndrome (AIDS), a disease that severely weakens the immune system and makes patients more susceptible to infections. Although there is no definitive cure for HIV, advances in drug development offer promising prospects. In this study, we targeted HIV-1 reverse transcriptase by performing virtual screening (VS) to identify novel candidate compounds. From a database of compounds similar to the inhibitor thymidine-5'-triphosphate (TTP), three compounds (CID441663, CID123650073, and CID123789980) were selected for their docking scores, which outperformed those of the reference compound TTP (-6.2302 kcal/mol). These compounds were then subjected to ADMET, PASS, and DFT analyses. Interestingly, all three ligands showed a broad spectrum of predicted antiviral activity, including targets related to human herpes virus and HIV. Specifically, while TTP primarily targets HIV-1 reverse transcriptase, the top three ligands were predicted to target HIV-1 integrase, with CID441663 and CID123789980 displaying higher confidence in this target compared to CID123650073. These findings suggest that the candidate ligands should undergo further in vitro validation to determine their precise roles as inhibitors or antagonists, and to confirm their selective targeting of HIV-related proteins.

Protective effects of Sphaeranthus indicus floral extract against BPS-induced testicular damage in rats occurs through downregulation of RIPK1/3-MLK-driven necroptosis and Fas-FasL-mediated apoptosis

AbstractBisphenol S (BPS) is one of the monomers preferred in the manufacturing of polycarbonate plastics. Unfortunately, its estrogenic and genotoxic effects are similar to those of bisphenol A. The protective effects of Sphaeranthus indicus floral extract (SFE) against reprotoxic effects of BPS (50 µg/kg per body weight) in rats exposed to it via drinking water was investigated. Different SFE doses (25, 50, and 100 mg/kg) were administered via oral gavage for 10 weeks. High-performance liquid chromatography (HPLC) results indicated that SFE was rich in polyphenols, with rutin and quercetin being important bioactive molecules modulating BPS-induced necroptosis and apoptosis. Biochemical analyses unveiled that rats administered BPS only exhibited considerable elevation of biomarkers of nitro-oxidative stress, necroptotic (RIPK1/RIPK3 and MLKL), and apoptotic mediators (Fas/FasL and caspase 3/caspase-8). These events caused changes in sperm characteristics (sperm motility, sperm head, and sperm viability), sperm count, and hormonal profile (thyroid stimulating hormone, luteinizing hormone, and follicle-stimulating hormone) of the rats. Histological analysis suggested that there was pronounced sloughing of Sertoli cells, reduced spermatogenic cell density, increased levels of seminiferous tubules, and disorganized morphometric parameters related to seminiferous tubules. The SFE supplementation in rats with BPS-containing water restored nitro-oxidative stress biomarkers, which led to the reduction of necroptosis and apoptosis. Reinstatement of all the biomarkers of oxidative stress, inflammation, necroptosis, and apoptosis after SFE supplementations restored the hormonal profile and normal histoarchitecture of the testes. Virtual screening elucidated that the key regulators of the necroptosis are RIPK3-rutin and RIPK1-quercetin complexes. Further studies are needed to assess its pharmacodynamics, kinetics, and effective concentration for daily use in humans.

Digital Phenotypes of Mobile Keyboard Backspace Rates and Their Associations With Symptoms of Mood Disorder: Algorithm Development and Validation.

Passive sensing through smartphone keyboard data can be used to identify and monitor symptoms of mood disorders with low participant burden. Behavioral phenotyping based on mobile keystroke data can aid in clinical decision-making and provide insights into the individual symptoms of mood disorders. This study aims to derive digital phenotypes based on smartphone keyboard backspace use among 128 community adults across 2948 observations using a Bayesian mixture model. Eligible study participants completed a virtual screening visit where all eligible participants were instructed to download the custom-built BiAffect smartphone keyboard (University of Illinois). The BiAffect keyboard unobtrusively captures keystroke dynamics. All eligible and consenting participants were instructed to use this keyboard exclusively for up to 4 weeks of the study in real life, and participants' compliance was checked at the 2 follow-up visits at week 2 and week 4. As part of the research protocol, every study participant underwent evaluations by a study psychiatrist during each visit. We found that derived phenotypes were associated with not only the diagnoses and severity of depression and mania but also specific individual symptoms. Using a linear mixed-effects model with random intercepts accounting for the nested data structure from daily data, the backspace rates on the continuous scale did not differ between participants in the healthy control and in the mood disorders groups (P=.11). The 3-class model had mean backspace rates of 0.112, 0.180, and 0.268, respectively, with a SD of 0.048. In total, 3 classes, respectively, were estimated to comprise 37.5% (n=47), 54.4% (n=72), and 8.1% (n=9) of the sample. We grouped individuals into Low, Medium, and High backspace rate groups. Individuals with unipolar mood disorder were predominantly in the Medium group (n=54), with some in the Low group (n=27) and a few in the High group (n=6). The Medium group, compared with the Low group, had significantly higher ratings of depression (b=2.32, P=.008). The High group was not associated with ratings of depression with (P=.88) or without (P=.27) adjustment for medication and diagnoses. The High group, compared with the Low group, was associated with both nonzero ratings (b=1.91, P=.02) and higher ratings of mania (b=1.46, P<.001). The High group, compared with the Low group, showed significantly higher odds of elevated mood (P=.03), motor activity (P=.04), and irritability (P<.05). This study demonstrates the promise of mobile typing kinematics in mood disorder research and practice. Monitoring a single mobile typing kinematic feature, that is, backspace rates, through passive sensing imposes a low burden on the participants. Based on real-life keystroke data, our derived digital phenotypes from this single feature can be useful for researchers and practitioners to distinguish between individuals with and those without mood disorder symptoms.

N-[4-(Benzyloxy)-3-methoxybenzyl)]adamantane-1-amine (DZH2), a dual CCR5 and CXCR4 inhibitor as a potential agent against triple negative breast cancer.

DZH2, a dual inhibitor of the chemokine receptors CCR5 and CXCR4, was discovered from virtual screening for CCR5 antagonists. In specific Ca2+ chemokine signaling assays, DZH2 displayed low micromolar IC50 values at both chemokine receptors. Its binding to intracellular allosteric binding sites of CCR5 and CXCR4 was confirmed by MD simulations and binding free-energy calculations. DZH2 is superior to the CCR5 antagonist maraviroc in terms of its inhibitory activity on the growth of two breast cancer cell lines. In MCF7 and MDA-MB-231 cells, DZH2 was a >100-fold more potent inhibitor of cell viability compared to maraviroc. DZH2 (6.7 µM) reduced migration of MDA-MB-231 cells to 4% compared to 50% inhibition of migration caused by maraviroc (780 µM). Also, DZH2 was a significantly more potent inhibitor of colony formation in MDA-MB-231 cells than maraviroc. In MCF10 cells, DZH2 caused no alteration in the gene expression with respect to cellular pathways mediating cell death, indicating its selectivity to breast cancer cells.

NGT: Generative AI with Synthesizability Guarantees Discovers MC2R Inhibitors from a Tera-Scale Virtual Screen.

Commercially available, synthesis-on-demand virtual libraries contain upward of trillions of readily synthesizable compounds for drug discovery campaigns. These libraries are a critical resource for rapid cycles of in silico discovery, property optimization and in vitro validation. However, as these libraries continue to grow exponentially in size, traditional search strategies encounter significant limitations. Here we present NeuralGenThesis (NGT), an efficient reinforcement learning approach to generate compounds from ultralarge libraries that satisfy user-specified constraints. Our method first trains a generative model over a virtual library and subsequently trains a normalizing flow to learn a distribution over latent space that decodes constraint-satisfying compounds. NGT allows multiple constraints simultaneously without dictating how molecular properties are calculated. Using NGT, we generated potent and selective inhibitors for the melanocortin-2 receptor (MC2R) from a three trillion compound library. NGT offers a powerful and scalable solution for navigating ultralarge virtual libraries, accelerating drug discovery efforts.

Integration of Consensus‐Based Pharmacophore Mapping and Molecular Docking in Sequential Virtual Screening: Toward the Discovery of Novel PI3Kγ Inhibitors

AbstractNumerous research studies have demonstrated the significant correlation of phosphatidylinositol‐3 kinase gamma (PI3Kγ) with the onset and progression of various human diseases, highlighting PI3Kγ as a promising therapeutic target. However, PI3Kγ demonstrates considerable similarity with other isoforms in the PI3K family, presenting significant challenges in the creation of PI3Kγ inhibitors. This study presents an ensemble‐based virtual screening approach to discover novel inhibitors targeting PI3Kγ. Eganelisib (IPI‐549) is the sole selective PI3Kγ inhibitor that has progressed to clinical trials, making it a significant model for the advancement of novel PI3Kγ inhibitors. Initially, common feature pharmacophore and receptor–ligand pharmacophore models were independently developed using IPI‐549 and its potent derivatives, in conjunction with the crystal complex of PI3Kγ/IPI‐549. Both qualitative pharmacophore models proved highly effective at distinguishing between active and inactive compounds. Then, four widely utilized docking programs were chosen for assessment, where the Glide_SP mode demonstrated superior predictive accuracy in sampling ligand conformations during binding, effectively distinguishing between PI3Kγ inhibitors and noninhibitors. Finally, a virtual screening protocol was conducted to screen the ChEMBL database, utilizing similarity search, consensus‐based pharmacophore mapping, and sequential molecular docking. This process resulted in the identification of multiple molecules exhibiting notable promise as potent PI3Kγ inhibitors.

In‐Silico Design of Novel Indole‐Selenide Derivatives as Potential P‐Glycoprotein Inhibitors Against Multi‐Drug Resistance in MCF‐7/ADR Cells: 2D‐QSAR, Molecular Docking, and Dynamics Simulations

AbstractIn this work, a set of thirty‐one novel indole‐selenide derivatives (C1–C31), reported recently as P‐glycoprotein inhibitors against multi‐drug resistance in MCF‐7/ADR cells, have been computationally investigated for the first time by in‐silico approaches, combining 2D‐quantitative structure‐activity relationship based virtual screening (2D‐QSAR‐VS) model, molecular docking, and dynamics simulations. The in‐silico study aims to design new potent molecules with higher anticancer inhibitory activity than observed with in‐vitro assays. The 2D‐QSAR model is built using multiple linear regression (MLR) techniques, and cross‐validated by internal and external parameters, applicability domain (AD) analysis, and Y‐randomization tests, corroborating the Golbreikh and Tropsha criteria. Subsequently, virtual screening was performed on the generated database, considering higher pIC50 values than the most effective in‐vivo C27 inhibitor. Subsequently, molecular docking and dynamics simulations were applied on the selected higher‐scoring ligands showing the best interactions with the PHE643, TYR745, and PRO571 amino acids of the P‐gp receptor (7 A6E), predicting dynamically stable complexes at the time‐scale of 200 ns. The in‐silico outcomes indicate that the selected new ligands have shown promising inhibitory activity for future anti‐cancer therapies, with perspective validating by in‐vitro and in‐vivo studies.

Structure-based virtual screening and drug repurposing studies indicate potential inhibitors of bovine papillomavirus E6 oncoprotein.

Bovine papillomavirus type 1 (BPV1) is an oncogenic virus that causes lesions and cancer in infected cattle. Despite being one of the most studied genotypes in the family and occurring in herds worldwide, there are currently no vaccines or drugs for its control. The viral E6 oncoprotein plays a crucial role in infection by this virus, making it a promising target for the development of new therapies. In this regard, we integrated structure-based virtual screening approaches, drug repositioning, and molecular dynamics to identify approved drugs with the potential to inhibit BPV1 E6. Our results reveal that Lumacaftor and MK-3207 are promising candidates for controlling BPV1 infection. The findings of this study may contribute to the development of E6 oncoprotein blockers in an accelerated and cost-effective manner.

Developing p21-activated kinase 1 (PAK1) activators to treat hypertrophic cardiomyopathy (HCM)

Abstract Despite significant progress in comprehending the genetic and metabolic underpinnings of cardiomyocyte dysfunction, there is still a pressing need for targeted treatments to address hypertrophy and progressive remodelling (e.g., fibrosis), seen in HCM1 . PAK1, a regulator of ion channels and myofilaments in cardiomyocytes, has shown promise in curbing pathological hypertrophy2 . Our hypothesis centres on the potential therapeutic benefits of pharmacologically activating PAK1 in managing hypertrophy and adverse remodelling in HCM. First, we conducted virtual screening and kinase assays to identify potent small molecule PAK1 activators, which significantly increased PAK1 activity by 3 to 5-fold, with an EC50 range between 0.5 and 2.5 μΜ. We then evaluated these compounds on cellular hypertrophy in an in vitro model using neonatal rat cardiomyocytes exposed to isoprenaline (ISO). Among those, JB2020A not only prevented ISO-induced hypertrophy, but also reversed pre-existing cellular hypertrophy induced by ISO 24 hours earlier. Subsequently, we assessed the therapeutic potential of JB2020A in a well-characterised HCM mouse model (i.e., Actc1E99K) 3 , known for its rapid disease progression. We initiated a six-week oral treatment regime at 4 weeks of age, dosing at 10 mg/kg per day. Echocardiography and fibrosis evaluation confirmed that JB2020A led to a significant reduction in cardiac hypertrophy and fibrosis with preserved contractile function. Western blotting analysis revealed increased PAK1 phosphorylation and activation in JB2020A-treated E99K hearts, along with a reduction in pro-apoptotic CHOP expression and an upregulation of protective endoplasmic reticulum (ER) response (e.g., ATF4 and Xbp1), indicating an amelioration of ER stress in HCM. These findings underscore the therapeutic potential of small molecule PAK1 activators as a novel approach for addressing hypertrophy and progressive remodelling in HCM.

Quantum mechanics/molecular mechanics (QM/MM) methods in drug design: a comprehensive review of development and applications

The integration of quantum mechanics (QM) and molecular mechanics (MM) methods, known as QM/MM, has emerged as a powerful computational approach in drug design. This hybrid technique combines the accuracy of QM calculations for the reactive region with the computational efficiency of MM methods for the surrounding environment. QM/MM methods have proven invaluable in studying chemical reactions, exploring enzyme mechanisms, and investigating ligand-protein interactions, all of which are crucial for rational drug design. This review provides a comprehensive overview of the QM/MM methodology, its theoretical foundations, and its applications in various aspects of drug design. We discussed the key components, such as QM and MM region partitioning, link atom schemes, and boundary treatments. Additionally, we highlight recent advancements and challenges in QM/MM methods, including polarizable force fields, implicit solvation models, and enhanced sampling techniques. Furthermore, we present illustrative examples showcasing the successful application of QM/MM methods in lead optimization, virtual screening, and the elucidation of biochemical mechanisms relevant to drug targets. Finally, we provide perspectives on future developments and the potential impact of QM/MM methods on the drug discovery pipeline.

Chemical space-informed machine learning models for rapid predictions of x-ray photoelectron spectra of organic molecules

Abstract We present machine learning models based on kernel-ridge regression for predicting X-ray photoelectron spec- tra of organic molecules originating from the ionization energies of 1s electrons in carbon (C), nitrogen (N), oxygen (O), and fluorine (F) atoms. We constructed training dataset through high-throughput calculations of core-electron binding energies (CEBEs) for 12,880 small organic molecules in the bigQM7ω dataset, employing the ∆-SCF formalism coupled with meta-GGA-DFT and a variationally converged basis set. The models are cost-effective, as they require the atomic coordinates of a molecule generated using universal force fields while estimating the target-level CEBEs corresponding to DFT-level equilibrium geometry. We explore transfer learning by utilizing the atomic environment feature vectors learned using a graph neural network framework in kernel-ridge regression. Additionally, we enhance accuracy using the ∆-machine learning framework by leveraging inexpensive baseline spectra derived from Kohn–Sham eigenvalues. Upon application to 208 com- binatorially substituted uracil molecules, larger than those in the training set, our analyses reveal that while the models may not yield quantitatively accurate predictions of CEBEs on a molecule-by-molecule basis, they do exhibit a strong linear correlation, which proves valuable for virtual high-throughput screening purposes. We present the dataset and models as the Python module, cebeconf, to facilitate further explorations.

Structural and free energy landscape analysis for the discovery of antiviral compounds targeting the cap-binding domain of influenza polymerase PB2

Influenza poses a significant threat to global health, with the ability to cause severe epidemics and pandemics. The polymerase basic protein 2 (PB2) of the influenza virus plays a crucial role in the viral replication process, making the CAP-binding domain of PB2 an attractive target for antiviral drug development. This study aimed to identify and evaluate potential inhibitors of the influenza polymerase PB2 CAP-binding domain using computational drug discovery methods. We employed a comprehensive computational approach involving virtual screening, molecular docking, and 500 ns molecular dynamics (MD) simulations. Compounds were selected from the Diverse lib database and assessed for their binding affinity and stability in interaction with the PB2 CAP-binding domain. The study utilized the generalized amber force field (GAFF) for MD simulations to further evaluate the dynamic behaviour and stability of the interactions. Among the screened compounds, compounds 1, 3, and 4 showed promising binding affinities. Compound 4 demonstrated the highest binding stability and the most favourable free energy profile, indicating strong and consistent interaction with the target domain. Compound 3 displayed moderate stability with dynamic conformational changes, while Compound 1 maintained robust interactions throughout the simulations. Comparative analyses of these compounds against a control compound highlighted their potential efficacy. Compound 4 emerged as the most promising inhibitor, with substantial stability and strong binding affinity to the PB2 CAP-binding domain. These findings suggest that compound 4, along with compounds 1 and 3, holds the potential for further development into effective antiviral agents against influenza. Future studies should focus on experimental validation of these compounds and exploration of resistance mechanisms to enhance their therapeutic utility.

Screening of BindingDB database ligands against EGFR, HER2, Estrogen, Progesterone and NF-[formula omitted]B receptors based on machine learning and molecular docking

Breast cancer, the second most prevalent cancer among women worldwide, necessitates the exploration of novel therapeutic approaches. To target the four subgroups of breast cancer “hormone receptor-positive and HER2-negative, hormone receptor-positive and HER2-positive, hormone receptor-negative and HER2-positive, and hormone receptor-negative and HER2-negative” it is crucial to inhibit specific targets such as EGFR, HER2, ER, NF-κB, and PR.In this study, we evaluated various methods for binary and multiclass classification. Among them, the GA-SVM-SVM:GA-SVM-SVM model was selected with an accuracy of 0.74, an F1-score of 0.73, and an AUC of 0.92 for virtual screening of ligands from the BindingDB database. This model successfully identified 4454, 803, 438, and 378 ligands with over 90% precision in both active/inactive and target prediction for the classes of EGFR+HER2, ER, NF-κB, and PR, respectively, from the BindingDB database. Based on to the selected ligands, we created a dendrogram that categorizes different ligands based on their targets. This dendrogram aims to facilitate the exploration of chemical space for various therapeutic targets.Ligands that surpassed a 90% threshold in the product of activity probability and correct target selection probability were chosen for further investigation using molecular docking. The binding energy range for these ligands against their respective targets was calculated to be between −15 and −5 kcal/mol. Finally, based on general and common rules in medicinal chemistry, we selected 2, 3, 3, and 8 new ligands with high priority for further studies in the EGFR+HER2, ER, NF-κB, and PR classes, respectively.

Discovery of Two GSK3β Inhibitors from Sophora flavescens Ait. using Structure-based Virtual Screening and Bioactivity Evaluation.

Kushen (Sophora flavescens Ait.) has a long history of medicinal use in China due to its medicinal values, such as antibacterial, antiviral, and anti-inflammatory. Rapid discovery of the components and the medicinal effects exerted by Kushen will help elucidate the science of Kushen in curing diseases. GSK3β (glycogen synthase kinase-3 beta) is a protein kinase with a wide range of physiological functions, such as antibacterial, antiviral, and anti-inflammatory. The discovery of inhibitors targeting GSK3β from Kushen was not only helpful for the rapid discovery of the components responsible for the efficacy of Kushen but also important for the development of novel drugs. In this study, the chemical composition of Kushen was extracted from the TMSCP database. Molecular docking, GSK3β enzyme assay, and molecular dynamics simulations were used to discover the GSK3β inhibitors from the chemical composition of Kushen. A total of 113 chemical compositions of Kushen were extracted from the TMSCP database. Molecular docking indicated that 15 chemical compositions of Kushen scored better than -8 kcal/mol against GSK3β. GSK3β enzyme assay demonstrated several inhibitory activities of kushenol I and kushenol F with IC50 values of 7.53 ± 2.55 μM and 4.96 ± 1.29 μM, respectively. Molecular dynamics simulations were used to reveal the interactions of kushenol I and kushenol F with GSK3β from structural and energetic perspectives. Kushenol I and kushenol F could be the material basis for the antibacterial, antiviral, and anti-inflammatory properties of Kushen.

Structure-based identification of small-molecule inhibitors that target the DIII domain of the Dengue virus glycoprotein E pan-serotypically.

Dengue viral infection is caused by the Dengue virus, which spreads to humans through the bite of infected mosquitos. Dengue affects over half of the global population, with an estimated 500 million infections per year. Despite this, no effective treatment is currently available, however, several promising candidates are undergoing pre-clinical/clinical testing. The existence of four major serotypes is an important challenge in the development of drugs and vaccines to combat Dengue virus infection. Hence, the drug/vaccine thereby developed should neutralize all the four serotypes equally. However, there is no pan-serotype specific treatment for Dengue virus, thereby emphasizing the need for the identification of novel drug-like compounds that can target all serotypes of the Dengue virus equally. To this end, we employed virtual screening methodologies to find drug-like compounds that target the domain III of glycoprotein E. Most importantly, domain III of E protein is involved in viral fusion with the host membrane and is also targeted by neutralizing antibodies. Our study found two small molecule drug-like compounds (out of the 3 million compounds screened) having similar binding affinity with all four serotypes. The compounds thereby identified exhibit favourable drug like properties and can be developed as a treatment for Dengue virus.