Hierarchical virtual screening approaches in small molecule drug discovery

Pharmacophore-based virtual screening of commercial databases against β-secretase 1 for drug development against Alzheimer's disease

β-secretase 1, one of the most important proteins, is an aspartate protease. This membrane-associated protein is used for treating Alzheimer's disease (AD). Several inhibitors have been pursued against β-secretase 1, but they still have not resulted effectively. Virtual screening based on pharmacophores has been shown to be useful for lead optimization and hit identification in the preliminary phase of developing a new drug. Here, we screen the commercially available databases to find the hits against β-secretase 1 for drug discovery against AD. Virtual screening for 200,000 compounds was done using the database from the Vitas-M Laboratory. The phase screen score was utilized to assess the screened hits. Molecular docking was performed on compounds with phase scores >1.9. According to the study, the 66H ligand of the crystal structure has the maximum performance against β-secretase 1. The redocking of the co-crystal ligand showed that the docked ligand was seamlessly united with the crystal structure. The reference complex had three hydrogen bonds with Asp93, Asp289, and Gly291; one van der Waals interaction with Gly74; and three hydrophobic interactions. After equilibration, the RMSD of the reference compound sustained a value of ∼1.5 Å until 30 ns and then boosted to 2.5 Å. On comparison, the RMSD of the S1 complex steadily increased to ∼2.5 Å at 15 ns, displayed slight aberrations at approximately ∼2.5-3 Å until 80 ns, and then achieved steadiness toward the end of the simulation. The arrangements of proteins stayed condensed during the mockup when bonded to these complexes as stable Rg values showed. Furthermore, the MM/GBSA technique was employed to analyze both compounds' total binding free energies (ΔGtotal). Our research study provides a new understanding of using 66H as anti-β-secretase 1 for drug development against AD.

Advances in machine intelligence-driven virtual screening approaches for big-data

Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

A domain-label-guided translation model for molecular optimization

Molecular optimization, which aims to improve molecular properties by modifying complex molecular structures, is a crucial and challenging task in drug discovery. In recent years, translation models provide a promising way to transform low-property molecules to high-property molecules, which enables molecular optimization to achieve remarkable progress. However, most existing models require matched molecular pairs, which are prone to be limited by the datasets. Although some models do not require matched molecular pairs, their performance is usually sacrificed due to the lack of useful supervising information. To address this issue, a domain-label-guided translation model is proposed in this paper, namely DLTM. In the model, the domain label information of molecules is exploited as a control condition to obtain different embedding representations, enabling the model to generate diverse molecules. Besides, the model adopts a classifier network to identify the property categories of transformed molecules, guiding the model to generate molecules with desired properties. The performance of DLTM is verified on two optimization tasks, namely the quantitative estimation of drug-likeness and penalized logP. Experimental results show that the proposed DLTM is superior to the compared baseline models.

Dnmt3b ablation affects fracture repair process by regulating apoptosis

PURPOSE

Previous studies have shown that DNA methyltransferase 3b (Dnmt3b) is the only Dnmt responsive to fracture repair and Dnmt3b ablation in Prx1-positive stem cells and chondrocyte cells both delayed fracture repair. Our study aims to explore the influence of Dnmt3b ablation in Gli1-positive stem cells in fracture healing mice and the underlying mechanism.

METHODS

We generated Gli1-CreERT2; Dnmt3bflox/flox (Dnmt3bGli1ER) mice to operated tibia fracture. Fracture callus tissues of Dnmt3bGli1ER mice and control mice were collected and analyzed by X-ray, micro-CT, biomechanical testing, histopathology and TUNEL assay.

RESULTS

The cartilaginous callus significantly decrease in ablation of Dnmt3b in Gli1-positive stem cells during fracture repair. The chondrogenic and osteogenic indicators (Sox9 and Runx2) in the fracture healing tissues in Dnmt3bGli1ER mice much less than control mice. Dnmt3bGli1ER mice led to delayed bone callus remodeling and decreased biomechanical properties of the newly formed bone during fracture repair. Both the expressions of Caspase-3 and Caspase-8 were upregulated in Dnmt3bGli1ER mice as well as the expressions of BCL-2.

CONCLUSIONS

Our study provides an evidence that Dnmt3b ablation Gli1-positive stem cells can affect fracture healing and lead to poor fracture healing by regulating apoptosis to decrease chondrocyte hypertrophic maturation.

Bioactivity predictions and virtual screening using machine learning predictive model

Recently, there has been significant attention on machine learning algorithms for predictive modeling. Prediction models for enzyme inhibitors are limited, and it is essential to account for chemical biases while developing them. The lack of repeatability in available models and chemical bias issues constrain drug discovery and development. A new prediction model for enzyme inhibitors has been developed, and the model efficacy was checked using Dipeptidyl peptidase 4 (DPP-4) inhibitors. A Python script was prepared and can be provided for personal use upon request. Among various machine learning algorithms, it was found that Random Forest offers the best accuracy. Two models were compared, one with diverse training and test data and the other with a random split. It was concluded that machine learning predictive models based on the Murcko scaffold can address chemical bias concerns. In-silico screening of the Drug Bank database identified two molecules against DPP-4, which are previously proven hit molecules. The approach was further validated through molecular docking studies and molecular dynamics simulations, demonstrating the credibility and relevance of the developed model for future investigations and potential translation into clinical applications.Communicated by Ramaswamy H. Sarma.

Al-tameemi Z. In silico comparison between the mutated and wild-type androgen receptors and their influence on the selection of optimum androgenic receptor blockers for the treatment of prostate cancer

Background: Prostate cancer is a disease that occurs in men aged more than 50 years. In Iraq, 8.89 men per 100,000 population suffer from prostate cancer, with the incidence being 14,016 cases and mortality being 6,367 cases. Despite advances in treatment against prostate cancer, it can become resistant to drugs. Therefore, the aim of current study was to search and identify binding sites for the repositioning of drugs by computational methods (docking).  Methods: Based on the protein structure of the wild androgen receptor, the analysis parameters (22x22x22 on the X, Y, and Z axes) were established. Results: The interactions of the natural ligands with androgen receptor were 10.0 (testosterone) and 10.8 (dihydrotestosterone) while mutated androgen receptor (T877A) had a low affinity with testosterone and dihydrotestosterone (-5.3 and -6.7, respectively). In the interactions of both receptors with the reported inhibitors (antagonists), a decrease with Bicalutamide (-8.3 and -4.3, respectively) and an increase in affinity with Flutamide and Nilutamide (-7.7 and 8.6, wild AR; -8.7 and -9.3 AR T877A) were observed. As for Enzalutamide and Apalutamide (second-generation antagonists), the change was minimal between wild androgen receptor and T877A (-7.6 and -7.7; -7.3 and -7.3, respectively). The change in the affinity of the ligands with androgen receptor and androgen receptor T877A shows how a mutation alters the bonds between these molecules. Conclusion: The identification of key sites and potent inhibitors against abnormal androgen receptor functions will enrich prostate cancer treatments.

Ligand and structure based hierarchical virtual screening cascade for finding novel epidermal growth factor receptor inhibitors

The epidermal growth factor receptor (EGFR) tyrosine kinase plays an important role in tumor formation and growth by mediating cell growth and other physiological processes. Therefore, EGFR is a promising target for the treatment of cancer. In this work, we combined ligand-based and structure-based virtual screening methods to identify novel EGFR inhibitors from a library of more than 103 thousand compounds. We first obtained hundreds of compounds with similar physiochemical properties through 3D molecular shape and electrostatic similarity screening with potent inhibitors AEE788 and Afatinib as queries. Next, we identified compounds with strong binding affinities to the EGFR pocket through molecular docking, which makes good use of the structure information of the receptor. After molecular scaffold analysis, our bioassay confirmed 13 compounds with EGFR inhibitory activity and three compounds had IC50 values below 1000 nM. In addition, we collected 5371 EGFR inhibitors from online databases, and clustered them into 7 groups by K-means method using their ECFP4 fingerprints as input. Each cluster had typical molecular fragments and corresponding activity characteristics, which could guide the design of EGFR inhibitors, and we concluded that the fragments from some of the hits are indicated in the highly active scaffolds.

A novel in silico scaffold-hopping method for drug repositioning in rare and intractable diseases

In the field of rare and intractable diseases, new drug development is difficult and drug repositioning (DR) is a key method to improve this situation. In this study, we present a new method for finding DR candidates utilizing virtual screening, which integrates amino acid interaction mapping into scaffold-hopping (AI-AAM). At first, we used a spleen associated tyrosine kinase inhibitor as a reference to evaluate the technique, and succeeded in scaffold-hopping maintaining the pharmacological activity. Then we applied this method to five drugs and obtained 144 compounds with diverse structures. Among these, 31 compounds were known to target the same proteins as their reference compounds and 113 compounds were known to target different proteins. We found that AI-AAM dominantly selected functionally similar compounds; thus, these selected compounds may represent improved alternatives to their reference compounds. Moreover, the latter compounds were presumed to bind to the targets of their references as well. This new "compound-target" information provided DR candidates that could be utilized for future drug development.

Optimization of small molecule degraders and antagonists for targeting estrogen receptor based on breast cancer: current status and future

The estrogen receptor (ER) is a classical receptor protein that plays a crucial role in mediating multiple signaling pathways in various target organs. It has been shown that ER-targeting therapies inhibit breast cancer cell proliferation, enhance neuronal protection, and promote osteoclast formation. Several drugs have been designed to specifically target ER in ER-positive (ER+) breast cancer, including selective estrogen receptor modulators (SERM) such as Tamoxifen. However, the emergence of drug resistance in ER+ breast cancer and the potential side effects on the endometrium which has high ER expression has posed significant challenges in clinical practice. Recently, novel ER-targeted drugs, namely, selective estrogen receptor degrader (SERD) and selective estrogen receptor covalent antagonist (SERCA) have shown promise in addressing these concerns. This paper provides a comprehensive review of the structural functions of ER and highlights recent advancements in SERD and SERCA-related small molecule drugs, especially focusing on their structural optimization strategies and future optimization directions. Additionally, the therapeutic potential and challenges of novel SERDs and SERCAs in breast cancer and other ER-related diseases have been discussed.

Promising disruptors of p53-MDM2 dimerization from some medicinal plant phytochemicals: a molecular modeling study

Cancer is a major global health issue that has a high mortality rate. p53, which functions as a tumor suppressor, is critical in preventing tumor development by regulating the cell cycle and inducing apoptosis in damaged cells. However, the tumor suppressor function of p53 is effectively inhibited by its direct interaction with the hydrophobic cleft of MDM2 protein via multiple mechanisms As a result, restoring p53 activity by blocking the p53-MDM2 protein-protein interaction has been proposed as a compelling therapeutic strategy for cancer treatment. The use of molecular docking and phytochemical screening procedures are appraised to inhibit MDM2's hydrophobic cleft and disrupt the p53-MDM2 interaction. For this purpose, a library of 51 bioactive compounds from 10 medicinal plants was compiled and subjected to structure-based virtual screening. Out of these, only 3 compounds (Atalantoflavone, Cudraxanthone 1, and Ursolic acid) emerged as promising inhibitors of MDM2-p53 based on their binding affinities (-9.1 kcal/mol, -8.8 kcal/mol, and -8.8 kcal/mol respectively) when compared to the standard (-8.8 kcal/mol). Moreover, these compounds showed better pharmacokinetic and drug-like profiling than the standard inhibitor (Chromonotriazolopyrimidine 1). Finally, the 100 ns MD simulation analysis confirmed no significant perturbation in the conformational dynamics of the simulated binary complexes when compared to the standard. In particular, Ursolic acid was found to satisfy the molecular enumeration the most compared to the other inhibitors. Our overall molecular modeling finding shows why these compounds may emerge as potent arsenals for cancer therapeutics. Nonetheless, extensive experimental and clinical research is needed to augment their use in clinics.Communicated by Ramaswamy H. Sarma.

Molecular Multi-Target Approach for Human Acetylcholinesterase, Butyrylcholinesterase and β-Secretase 1: Next Generation for Alzheimer's Disease Treatment

Alzheimer's Disease (AD) is a neurodegenerative condition characterized by progressive memory loss and other affected cognitive functions. Pharmacological therapy of AD relies on inhibitors of the enzymes acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), offering only a palliative effect and being incapable of stopping or reversing the neurodegenerative process. However, recent studies have shown that inhibiting the enzyme β-secretase 1 (BACE-1) may be able to stop neurodegeneration, making it a promising target. Considering these three enzymatic targets, it becomes feasible to apply computational techniques to guide the identification and planning of molecules capable of binding to all of them. After virtually screening 2119 molecules from a library, 13 hybrids were built and further screened by triple pharmacophoric model, molecular docking, and molecular dynamics (t = 200 ns). The selected hybrid G meets all stereo-electronic requirements to bind to AChE, BChE, and BACE-1 and offers a promising structure for future synthesis, enzymatic testing, and validation.

Uni-Dock: GPU-Accelerated Docking Enables Ultralarge Virtual Screening

Molecular docking, a structure-based virtual screening method, is a reliable tool to enrich potential bioactive molecules from molecular databases. With the rapid expansion of compound library sizes, the speed of existing molecular docking programs becomes less than adequate to meet the demand for screening ultralarge libraries containing tens of millions or billions of molecules. Here, we propose Uni-Dock, a GPU-accelerated molecular docking program that supports various scoring functions including vina, vinardo, and ad4. Uni-Dock achieves more than 1000-fold speedup with high accuracy compared with the AutoDock Vina running in single CPU core, outperforming reported GPU-accelerated docking programs including AutoDock-GPU and Vina-GPU based on head-to-head experiments. Uni-Dock docks molecules in batches simultaneously using concurrent threads of each molecule. The data flow between GPU and CPU is optimized to eliminate CPU hotspots and maximize GPU utility. Additionally, Uni-Dock also supports hydrogen bond biased docking for all scoring functions and can be migrated to multiple GPUs of different architectures and manufacturers. We analyzed the improved performance of Uni-Dock on the CASF-2016 and DUD-E datasets and recommend three combinations of hyperparameters corresponding to different docking scenarios. To demonstrate Uni-Dock's capability on routinely screening ultralarge libraries, we performed hierarchical virtual screening experiments with Uni-Dock on the Enamine Diverse REAL druglike set containing 38.2 million molecules to a popular target KRAS G12D in 12 h using 100 NVIDIA V100 GPUs. To the best of our knowledge, Uni-Dock should be the fastest GPU-accelerated docking program to date.

Identification of EGFR inhibitors as potential agents for cancer therapy: pharmacophore-based modeling, molecular docking, and molecular dynamics investigations

CONTEXT

As a member of a large family of proteins that together regulate various aspects of cell growth and development, the epidermal growth factor receptor (EGFR) is a validated target for the development of new drugs. Herein, we compiled a library of 62 compounds from the PubChem database with similar pharmacophores as osimertinib, which to our knowledge represents the only drug capable of overcoming EGFR-T790M-mutated NSCLC until date. Subsequently, we launched a docking-based virtual screening campaign against the EGFR kinase with the compiled chemical entities. The virtual screen identified 3 hit candidates (CID_126667097, CID_137660592, and CID_137659061) with lower binding energy/higher affinity (- 8.7 kcal/mol, - 8.6 kcal/mol, and - 8.5 kcal/mol, respectively) than the standard osimertinib (- 8.4 kcal/mol). Molecular dynamics metrics such as RMSD, RMSF, ROG, and intermolecular H-bond were used to substantiate the stability of the promising drug candidates at the binding pocket of EGFR after 100,000 ps production run. Overall, our molecular modeling study portrayed CID_126667097, CID_137660592, and CID_137659061 as lead-like drug candidates that may be further developed for the treatment of EGFR-associated cancer disease.

METHODS

Molecular docking was conducted with Autodock Vina. A total of 62 compounds were compiled for the docking screen, which were then downloaded in SMILE format and converted to Protein Data Bank (PDB) format using the Openbabel online server. Finally, Gromacs 2022.3 was used to perform MD simulation to substantiate the stability of the hit candidates.

Pivotal role for S-nitrosylation of DNA methyltransferase 3B in epigenetic regulation of tumorigenesis

DNA methyltransferases (DNMTs) catalyze methylation at the C5 position of cytosine with S-adenosyl-L-methionine. Methylation regulates gene expression, serving a variety of physiological and pathophysiological roles. The chemical mechanisms regulating DNMT enzymatic activity, however, are not fully elucidated. Here, we show that protein S-nitrosylation of a cysteine residue in DNMT3B attenuates DNMT3B enzymatic activity and consequent aberrant upregulation of gene expression. These genes include Cyclin D2 (Ccnd2), which is required for neoplastic cell proliferation in some tumor types. In cell-based and in vivo cancer models, only DNMT3B enzymatic activity, and not DNMT1 or DNMT3A, affects Ccnd2 expression. Using structure-based virtual screening, we discovered chemical compounds that specifically inhibit S-nitrosylation without directly affecting DNMT3B enzymatic activity. The lead compound, designated DBIC, inhibits S-nitrosylation of DNMT3B at low concentrations (IC₅₀ ≤ 100 nM). Treatment with DBIC prevents nitric oxide (NO)-induced conversion of human colonic adenoma to adenocarcinoma in vitro. Additionally, in vivo treatment with DBIC strongly attenuates tumor development in a mouse model of carcinogenesis triggered by inflammation-induced generation of NO. Our results demonstrate that de novo DNA methylation mediated by DNMT3B is regulated by NO, and DBIC protects against tumor formation by preventing aberrant S-nitrosylation of DNMT3B.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine

BACKGROUND

Traditional Chinese medicine (TCM), as a significant part of the global pharmaceutical science, the abundant molecular compounds it contains is a valuable potential source of designing and screening new drugs. However, due to the un-estimated quantity of the natural molecular compounds and diversity of the related problems drug discovery such as precise screening of molecular compounds or the evaluation of efficacy, physicochemical properties and pharmacokinetics, it is arduous for researchers to design or screen applicable compounds through old methods. With the rapid development of computer technology recently, especially artificial intelligence (AI), its innovation in the field of virtual screening contributes to an increasing efficiency and accuracy in the process of discovering new drugs.

PURPOSE

This study systematically reviewed the application of computational approaches and artificial intelligence in drug virtual filtering and devising of TCM and presented the potential perspective of computer-aided TCM development.

STUDY DESIGN

We made a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Then screening the most typical articles for our research.

METHODS

The systematic review was performed by following the PRISMA guidelines. The databases PubMed, EMBASE, Web of Science, CNKI were used to search for publications that focused on computer-aided drug virtual screening and design in TCM.

RESULT

Totally, 42 corresponding articles were included in literature reviewing. Aforementioned studies were of great significance to the treatment and cost control of many challenging diseases such as COVID-19, diabetes, Alzheimer's Disease (AD), etc. Computational approaches and AI were widely used in virtual screening in the process of TCM advancing, which include structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS). Besides, computational technologies were also extensively applied in absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction of candidate drugs and new drug design in crucial course of drug discovery.

CONCLUSIONS

The applications of computer and AI play an important role in the drug virtual screening and design in the field of TCM, with huge application prospects.

A Generative Approach to Materials Discovery, Design, and Optimization

Despite its potential to transform society, materials research suffers from a major drawback: its long research timeline. Recently, machine-learning techniques have emerged as a viable solution to this drawback and have shown accuracies comparable to other computational techniques like density functional theory (DFT) at a fraction of the computational time. One particular class of machine-learning models, known as "generative models", is of particular interest owing to its ability to approximate high-dimensional probability distribution functions, which in turn can be used to generate novel data such as molecular structures by sampling these approximated probability distribution functions. This review article aims to provide an in-depth understanding of the underlying mathematical principles of popular generative models such as recurrent neural networks, variational autoencoders, and generative adversarial networks and discuss their state-of-the-art applications in the domains of biomaterials and organic drug-like materials, energy materials, and structural materials. Here, we discuss a broad range of applications of these models spanning from the discovery of drugs that treat cancer to finding the first room-temperature superconductor and from the discovery and optimization of battery and photovoltaic materials to the optimization of high-entropy alloys. We conclude by presenting a brief outlook of the major challenges that lie ahead for the mainstream usage of these models for materials research.

Small Molecular Drug Screening Based on Clinical Therapeutic Effect

Virtual screening can significantly save experimental time and costs for early drug discovery. Drug multi-classification can speed up virtual screening and quickly predict the most likely class for a drug. In this study, 1019 drug molecules with actual therapeutic effects are collected from multiple databases and documents, and molecular sets are grouped according to therapeutic effect and mechanism of action. Molecular descriptors and molecular fingerprints are obtained through SMILES to quantify molecular structures. After using the Kennard–Stone method to divide the data set, a better combination can be obtained by comparing the combined results of five classification algorithms and a fusion method. Furthermore, for a specific data set, the model with the best performance is used to predict the validation data set. The test set shows that prediction accuracy can reach 0.862 and kappa coefficient can reach 0.808. The highest classification accuracy of the validation set is 0.873. The more reliable molecular set has been found, which could be used to predict potential attributes of unknown drug compounds and even to discover new use for old drugs. We hope this research can provide a reference for virtual screening of multiple classes of drugs at the same time in the future.

Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling

Computer modeling is a method that is widely used in scientific investigations to predict the biological activity, toxicity, pharmacokinetics, and synthesis strategy of compounds based on the structure of the molecule. This work is a systematic review of articles performed in accordance with the recommendations of PRISMA and contains information on computer modeling of the interaction of classical flavonoids with different biological targets. The review of used computational approaches is presented. Furthermore, the affinities of flavonoids to different targets that are associated with the infection, cardiovascular, and oncological diseases are discussed. Additionally, the methodology of bias risks in molecular docking research based on principles of evidentiary medicine was suggested and discussed. Based on this data, the most active groups of flavonoids and lead compounds for different targets were determined. It was concluded that flavonoids are a promising object for drug development and further research of pharmacology by in vitro, ex vivo, and in vivo models is required.

How Structural Biology Has Directly Impacted Our Understanding of P2X Receptor Function and Gating

P2X receptors are ATP-gated ion channels expressed in a wide variety of eukaryotic cells. They play key roles in diverse processes such as platelet activation, smooth muscle contraction, synaptic transmission, nociception, cell proliferation, and inflammation making this receptor family an important pharmacological target. Structures of P2X receptors solved by X-ray crystallography have been instrumental in helping to define mechanisms of molecular P2X receptor function. In 2009, the first X-ray structure of the P2X4 receptor subtype confirmed a trimeric stoichiometry and revealed the overall architecture of the functional ion channel. Subsequent X-ray structures have provided the molecular details to define the orthosteric ATP binding pocket, the orthosteric antagonist binding pocket, an allosteric antagonist binding pocket, and the pore architecture in each of the major conformational states of the receptor gating cycle. Moreover, the unique gating mechanism by which P2X receptor subtypes desensitize at differing rates, referred to as the helical recoil model of receptor desensitization, was discovered directly from X-ray structures of the P2X3 receptor. However, structures of P2X receptors solved by X-ray crystallography have only been able to provide limited information on the cytoplasmic domain of this receptor family, as this domain was always truncated to varying degrees in order to facilitate crystallization. Because the P2X7 receptor subtype has a significantly larger cytoplasmic domain that has been shown to be necessary for its ability to initiate apoptosis, an absence of structural information on the P2X7 receptor cytoplasmic domain has limited our understanding of its complex signaling pathways as well as its unusual ability to remain open without undergoing desensitization. This absence of cytoplasmic structural information for P2X7 receptors was recently overcome when the first full-length P2X7 receptor structures were solved by single-particle cryogenic electron microscopy. These structures finally provide insight into the large and unique P2X7 receptor cytoplasmic domain and revealed two novel structural elements and several surprising findings: first, a cytoplasmic structural element called the cytoplasmic ballast was identified that contains a dinuclear zinc ion complex and a high affinity guanosine nucleotide binding site and second, a palmitoylated membrane proximal structural element called the C-cys anchor was identified which prevents P2X7 receptor desensitization. This chapter will highlight the major structural and functional aspects of P2X receptors discovered through structural biology, with a key emphasis on the most recent cryogenic electron microscopy structures of the full-length, wild-type P2X7 receptor.

Identification of lead inhibitors of TMPRSS2 isoform 1 of SARS-CoV-2 target using neural network, random forest, and molecular docking

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was responsible for over 4 million confirmed cases of severe acute respiratory syndrome, of which more than 300,000 cases were confirmed to be dead as of May 2020. The virulent endocytotic activities of SARS-CoV-2 have been associated with angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2). Previous studies on the viral activation of TMPRSS2 focused most often than not on the isoform 2 of TMPRSS2, but the isoform 1 (529 residues) has also been shown to be expressed in target cells and contribute to viral activation in host. The inhibition of TMPRSS2 has been reported to grossly reduce the pathogenic effects of SARS-CoV-2 endocytotic activities.

In this study therefore, we developed two machine learning models using random forest classifier (RFC) and neural networks (NNs) based on 2251 serine protease inhibitors to screen a database of 21,000,000 virtual compounds. We screened the hit compounds using absorption, distribution, metabolism, and excretion (ADME) properties and finally docked the filtered compounds into the predicted binding site of TMPRSS2 isoform 1 homology model to determine their corresponding binding affinity and plausible molecular interactions.

One (ASONN) and four (ASOIRFC1–4) lead compounds were obtained from the ADME-NN and RFC filtered hits, respectively, having better binding affinity and lead-likeness properties than those of camostat; this could be due to extensive hydrogen and hydrophobic interactions.

ePharmer: An integrated and graphical software for pharmacophore-based virtual screening

Pharmacophore-based virtual screening (VS) has emerged as an efficient computer-aided drug design technique when appraising multiple ligands with similar structures or targets with unknown crystal structures. Current pharmacophore modeling and analysis software suffers from inadequate integration of mainstream methods and insufficient user-friendly program interface. In this study, we propose a stand-alone, integrated, graphical software for pharmacophore-based VS, termed ePharmer. Both ligand-based and structure-based pharmacophore generation methods were integrated into a compact architecture. Fine-grained modules were carefully organized into the computing, integration, and visualization layers. Graphical design covered the global user interface and specific user operations including editing, evaluation, and task management. Metabolites prediction analysis with the chosen VS result is provided for preselection of wet experiments. Moreover, the underlying computing units largely adopted the preliminary work of our research team. The presented software is currently in client use and will be released for both professional and nonexpert users. Experimental results verified the favorable computing capability, user convenience, and case performance of the proposed software.

Overcoming the Dependence on Animal Models for Osteoarthritis Therapeutics - The Promises and Prospects of In Vitro Models

Osteoarthritis (OA) is a musculoskeletal disease characterized by progressive degeneration of osteochondral tissues. Current treatment is restricted to the reduction of pain and loss of function of the joint. To better comprehend the OA pathophysiological conditions, several models are employed, however; there is no consensus on a suitable model. In this review, different in vitro models being developed for possible therapeutic intervention of OA are outlined. Herein, various in vitro OA models starting from 2D model, co-culture model, 3D models, dynamic culture model to advanced technologies-based models such as 3D bioprinting, bioassembly, organoids, and organ-on-chip-based models are discussed with their advantages and disadvantages. Besides, different growth factors, cytokines, and chemicals being utilized for induction of OA condition are reviewed in detail. Furthermore, there is focus on scrutinizing different molecular and possible therapeutic targets for better understanding the mechanisms and OA therapeutics. Finally, the underlying challenges associated with in vitro models are discussed followed by future prospective. Taken together, a comprehensive overview of in vitro OA models, factors to induce OA-like conditions, and intricate molecular targets with the potential to develop personalized osteoarthritis therapeutics in the future with clinical translation is provided.

Computer-aided screening for potential TMPRSS2 inhibitors: a combination of pharmacophore modeling, molecular docking and molecular dynamics simulation approaches

Transmembrane serine protease 2 (TMPRSS2) has been established as one of the host proteins that facilitate entry of coronaviruses into host cells. One of the approaches often employed towards preventing the entry and proliferation of viruses is computer-aided inhibition studies to identify potent compounds that can inhibit activity of viral targets in the host through binding at the active site. In this study, we developed a pharmacophore model of reportedly potent drugs against severe acute respiratory syndrome coronaviruses 1 and 2 (SARS-CoV-1 and -2). The model was used to screen the ZINC database for commercially available compounds having similar features with the experimentally tested drugs. The top 3000 compounds retrieved were docked into the active sites of a homology-modelled TMPRSS2. Docking scores of the top binders were validated and the top-ranked compounds were subjected to ADME, Lipinski's and medicinal Chemistry property predictions for druglikeness analyses. Two lead compounds, ZINC64606047 and ZINC05296775, were identified having binding affinities higher than those of the reference inhibitors, favorable interactions with TMPRSS2 active site residues and good ADME and medicinal chemistry properties. Molecular dynamics simulation was used to assess the stability and dynamics of the interactions of these compounds with TMPRSS2. Binding free energy and contribution energy evaluations were determined using MMPBSA method. Analyses of the trajectory dynamics collectively established further that the lead compounds bound and interacted stably with active site residues of TMPRSS2. Nonetheless, experimental studies are needed to further assess the potentials of these compounds as possible therapeutics against coronaviruses.Communicated by Ramaswamy H. Sarma.

Comprehensive virtual screening of 4.8 k flavonoids reveals novel insights into allosteric inhibition of SARS-CoV-2 MPRO

SARS-CoV-2 main protease is a common target for inhibition assays due to its high conservation among coronaviruses. Since flavonoids show antiviral activity, several in silico works have proposed them as potential SARS-CoV-2 main protease inhibitors. Nonetheless, there is reason to doubt certain results given the lack of consideration for flavonoid promiscuity or main protease plasticity, usage of short library sizes, absence of control molecules and/or the limitation of the methodology to a single target site. Here, we report a virtual screening study where dorsilurin E, euchrenone a11, sanggenol O and CHEMBL2171598 are proposed to inhibit main protease through different pathways. Remarkably, novel structural mechanisms were observed after sanggenol O and CHEMBL2171598 bound to experimentally proven allosteric sites. The former drastically affected the active site, while the latter triggered a hinge movement which has been previously reported for an inactive SARS-CoV main protease mutant. The use of a curated database of 4.8 k flavonoids, combining two well-known docking software (AutoDock Vina and AutoDock4.2), molecular dynamics and MMPBSA, guaranteed an adequate analysis and robust interpretation. These criteria can be considered for future screening campaigns against SARS-CoV-2 main protease.

Discovery of novel tubulin inhibitors targeting taxanes site by virtual screening, molecular dynamic simulation, and biological evaluation

Microtubules play crucial role in process of mitosis and cell proliferation, which have been considered as attractive drug targets for anticancer therapy. The aim of this study was to discover novel and chemically diverse tubulin inhibitors for treatment of cancer. In this investigation, the multilayer virtual screening methods, including common feature pharmacophore model, structure-based pharmacophore model and molecular docking, were developed to screen BioDiversity database with 30,000 compounds. A total of 102 compounds were obtained by the virtual screening, and further filtered by diverse chemical clusters with desired properties and PAINS analysis. Finally, 50 compounds were selected and submitted to the biological evaluation. Among these hits, hits 8 and 30 with novel scaffolds displayed stronger antiproliferative activity on four human tumor cells including Hela, A549, MCF-7, and HepG2. Moreover, the two hits were subsequently submitted to molecular dynamic simulations of 90 ns with the aim of exploring the stability of ligand-protein interactions into the binding pocket, and further probing the mechanism of the interaction between tubulin and hits. The molecular dynamic simulation results revealed there had stronger interactions between tubulin and hits in equilibrium state. Therefore, the hits 8 and 30 have been well characterized as lead compounds for developing new tubulin inhibitors with potential anticancer activity.

Drug Repurposing: A Strategy for Discovering Inhibitors against Emerging Viral Infections

BACKGROUND

Viral diseases are responsible for several deaths around the world. Over the past few years, the world has seen several outbreaks caused by viral diseases that, for a long time, seemed to possess no risk. These are diseases that have been forgotten for a long time and, until nowadays, there are no approved drugs or vaccines, leading the pharmaceutical industry and several research groups to run out of time in the search for new pharmacological treatments or prevention methods. In this context, drug repurposing proves to be a fast and economically viable technique, considering the fact that it uses drugs that have a well-established safety profile. Thus, in this review, we present the main advances in drug repurposing and their benefit for searching new treatments against emerging viral diseases.

METHODS

We conducted a search in the bibliographic databases (Science Direct, Bentham Science, PubMed, Springer, ACS Publisher, Wiley, and NIH's COVID-19 Portfolio) using the keywords "drug repurposing", "emerging viral infections" and each of the diseases reported here (CoV; ZIKV; DENV; CHIKV; EBOV and MARV) as an inclusion/exclusion criterion. A subjective analysis was performed regarding the quality of the works for inclusion in this manuscript. Thus, the selected works were those that presented drugs repositioned against the emerging viral diseases presented here by means of computational, high-throughput screening or phenotype-based strategies, with no time limit and of relevant scientific value.

RESULTS

291 papers were selected, 24 of which were CHIKV; 52 for ZIKV; 43 for DENV; 35 for EBOV; 10 for MARV; and 56 for CoV and the rest (72 papers) related to the drugs repurposing and emerging viral diseases. Among CoV-related articles, most were published in 2020 (31 papers), updating the current topic. Besides, between the years 2003 - 2005, 10 articles were created, and from 2011 - 2015, there were 7 articles, portraying the outbreaks that occurred at that time. For ZIKV, similar to CoV, most publications were during the period of outbreaks between the years 2016 - 2017 (23 articles). Similarly, most CHIKV (13 papers) and DENV (14 papers) publications occur at the same time interval. For EBOV (13 papers) and MARV (4 papers), they were between the years 2015 - 2016. Through this review, several drugs were highlighted that can be evolved in vivo and clinical trials as possible used against these pathogens showed that remdesivir represent potential treatments against CoV. Furthermore, ribavirin may also be a potential treatment against CHIKV; sofosbuvir against ZIKV; celgosivir against DENV, and favipiravir against EBOV and MARV, representing new hopes against these pathogens.

CONCLUSION

The conclusions of this review manuscript show the potential of the drug repurposing strategy in the discovery of new pharmaceutical products, as from this approach, drugs could be used against emerging viral diseases. Thus, this strategy deserves more attention among research groups and is a promising approach to the discovery of new drugs against emerging viral diseases and also other diseases.

Off-Target-Based Design of Selective HIV-1 PROTEASE Inhibitors

The approval of the first HIV-1 protease inhibitors (HIV-1 PRIs) marked a fundamental step in the control of AIDS, and this class of agents still represents the mainstay therapy for this illness. Despite the undisputed benefits, the necessary lifelong treatment led to numerous severe side-effects (metabolic syndrome, hepatotoxicity, diabetes, etc.). The HIV-1 PRIs are capable of interacting with "secondary" targets (off-targets) characterized by different biological activities from that of HIV-1 protease. In this scenario, the in-silico techniques undoubtedly contributed to the design of new small molecules with well-fitting selectivity against the main target, analyzing possible undesirable interactions that are already in the early stages of the research process. The present work is focused on a new mixed-hierarchical, ligand-structure-based protocol, which is centered on an on/off-target approach, to identify the new selective inhibitors of HIV-1 PR. The use of the well-established, ligand-based tools available in the DRUDIT web platform, in combination with a conventional, structure-based molecular docking process, permitted to fast screen a large database of active molecules and to select a set of structure with optimal on/off-target profiles. Therefore, the method exposed herein, could represent a reliable help in the research of new selective targeted small molecules, permitting to design new agents without undesirable interactions.

Computational representations of protein-ligand interfaces for structure-based virtual screening

Introduction: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies have shown that computational representation of protein-ligand interfaces and the later establishment of machine learning models are efficacious in improving the accuracy of SBVS.Areas covered: The authors review the computational methods for representing protein-ligand interfaces, which include the traditional ones that use deliberately designed fingerprints and descriptors and the more recent methods that automatically extract features with deep learning. The effects of these methods on the performance of machine learning models are briefly discussed. Additionally, case studies that applied various computational representations to machine learning are cited with remarks.Expert opinion: It has become a trend to extract binding features automatically by deep learning, which uses a completely end-to-end representation. However, there is still plenty of scope for improvement . The interpretability of deep-learning models, the organization of data management, the quantity and quality of available data, and the optimization of hyperparameters could impact the accuracy of feature extraction. In addition, other important structural factors such as water molecules and protein flexibility should be considered.

Structure-based virtual screening of highly potent inhibitors of the nematode chitinase CeCht1

Nematode chitinases play vital roles in various physiological processes, including egg hatching, larva moulting, and reproduction. Small-molecule inhibitors of nematode chitinases have potential applications for controlling nematode pests. On the basis of the crystal structure of CeCht1, a representative chitinase indispensable to the eggshell chitin degradation of the model nematode Caenorhabditis elegans, we have discovered a series of novel inhibitors bearing a (R)-3,4-diphenyl-4,5-dihydropyrrolo[3,4-c]pyrazol-6(2H)-one scaffold by hierarchical virtual screening. The crystal structures of CeCht1 complexed with two of these inhibitors clearly elucidated their interactions with the enzyme active site. Based on the inhibitory mechanism, several analogues with improved inhibitory activities were identified, among which the compound PP28 exhibited the most potent activity with a K_i value of 0.18 μM. This work provides the structural basis for the development of novel nematode chitinase inhibitors.

Racemases and epimerases operating through a 1,1-proton transfer mechanism: reactivity, mechanism and inhibition

Racemases and epimerases catalyse changes in the stereochemical configurations of chiral centres and are of interest as model enzymes and as biotechnological tools. They also occupy pivotal positions within metabolic pathways and, hence, many of them are important drug targets. This review summarises the catalytic mechanisms of PLP-dependent, enolase family and cofactor-independent racemases and epimerases operating by a deprotonation/reprotonation (1,1-proton transfer) mechanism and methods for measuring their catalytic activity. Strategies for inhibiting these enzymes are reviewed, as are specific examples of inhibitors. Rational design of inhibitors based on substrates has been extensively explored but there is considerable scope for development of transition-state mimics and covalent inhibitors and for the identification of inhibitors by high-throughput, fragment and virtual screening approaches. The increasing availability of enzyme structures obtained using X-ray crystallography will facilitate development of inhibitors by rational design and fragment screening, whilst protein models will facilitate development of transition-state mimics.

Targeting p53-MDM2 interactions to identify small molecule inhibitors for cancer therapy: beyond "Failure to rescue"

At present, disrupting p53-MDM2 interactions through small molecule ligands is a promising approach to safe treatment and management of human cancer. Tumor cells unlike the normal cells, are rapidly evolving affecting the efficacy of many approved anti-cancer agents due to drug resistance. Therefore, identifying a potential anticancer compound is crucial. Pharmacophore based virtual screening, followed by molecular docking, ADMET evaluation, and molecular dynamics studies against MDM2 protein was investigated to identify potential ligands that may act as inhibitors. The model (AHRR_1) with survival score (4.176) was selected among the top ranked generated Pharmacophore hypothesis. Validation of the model hypothesis by an external dataset of actives and inactive compounds produced significant validation attributes including; AUC = 0.85, BEDROC = 0.56 at α = 20.0, RIE = 8.18, AUAC = 0.88, and EF of 6.2 at the top 2% of the dataset. The model was use for screening the ZINC database, and the top 1375 hits satisfying the model hypothesis were subjected to molecular docking studies to understand the molecular and structural basis of selectivity of compounds for MDM2 protein. A sub-set of 25 compounds with binding energy lower than the reference inhibitors were evaluated for pharmacokinetic properties. Four compounds (ZINC02639178, ZINC06752762, ZINC38933175, and ZINC77969611) showed the most desired pharmacokinetic profile. Lastly, investigation of the dynamic behaviour of leads-protein complexes through MD simulation showed similar RMSD, RMSF, and H-bond occupancy profile compared to a reference inhibitor, suggesting stability throughout the simulation time. However, ZINC02639178 was found to satisfy the molecular enumeration the most compared to the other three leads. It may emerge as potential treatment option after extensive experimental studies. Communicated by Ramaswamy H. Sarma.

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

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

Deep Learning in Virtual Screening: Recent Applications and Developments

Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed.

Lean-Docking: Exploiting Ligands' Predicted Docking Scores to Accelerate Molecular Docking

In structure-based virtual screening (SBVS), a binding site on a protein structure is used to search for ligands with favorable nonbonded interactions. Because it is computationally difficult, docking is time-consuming and any docking user will eventually encounter a chemical library that is too big to dock. This problem might arise because there is not enough computing power or because preparing and storing so many three-dimensional (3D) ligands requires too much space. In this study, however, we show that quality regressors can be trained to predict docking scores from molecular fingerprints. Although typical docking has a screening rate of less than one ligand per second on one CPU core, our regressors can predict about 5800 docking scores per second. This approach allows us to focus docking on the portion of a database that is predicted to have docking scores below a user-chosen threshold. Herein, usage examples are shown, where only 25% of a ligand database is docked, without any significant virtual screening performance loss. We call this method "lean-docking". To validate lean-docking, a massive docking campaign using several state-of-the-art docking software packages was undertaken on an unbiased data set, with only wet-lab tested active and inactive molecules. Although regressors allow the screening of a larger chemical space, even at a constant docking power, it is also clear that significant progress in the virtual screening power of docking scores is desirable.

Hybrid In Silico and TR-FRET-Guided Discovery of Novel BCL-2 Inhibitors

B-Cell lymphoma 2 (BCL-2) regulates cell death in humans. In this study, combined multiscale in silico approaches and in vitro studies were employed. A small-molecule library that includes more than 210 000 compounds was used. The predicted therapeutic activity value (TAV) of the compounds in this library was computed with the binary cancer quantitative structure-activity relationships (QSAR) model. The molecules with a high calculated TAV were used in 26 individual toxicity QSAR models. As a result of this screening protocol, 288 nontoxic molecules with high predicted TAV were identified. These selected hits were then screened against the BCL-2 target protein using hybrid docking and molecular dynamics (MD) simulations. The interaction energies of identified compounds were compared with two known BCL-2 inhibitors. Then, the short MD simulations were carried out by initiating the best docking poses of 288 molecules. Average MM/GBSA energies were computed, and long MD simulations were employed to selected hits. The same calculations were also applied for two known BCL-2 inhibitors. Moreover, a five-site (AHRRR) structure-based pharmacophore model was constructed, and this model was used in the screening of the same database. On the basis of hybrid data-driven ligand identification study, final hits were selected and used in in vitro studies. Based on results of the time-resolved fluorescence resonance energy transfer (TR-FRET) analysis, further filtration was carried out for the U87-MG cell line tests. MTT cell proliferation assay analysis results showed that selected three potent compounds were significantly effective on glioma cells.

Crystal Structure and Structure-Based Discovery of Inhibitors of the Nematode Chitinase CeCht1

Nematode chitinases play crucial roles in various processes of the nematode lifecycle, including hatching, molting, and reproduction. Small-molecule inhibitors of nematode chitinases have shown promise for controlling nematode pests. However, the lack of structural information makes it a challenge to develop nematicides targeting nematode chitinases. Here, we report the first crystal structure of a representative nematode chitinase, that of CeCht1 from the model nematode Caenorhabditis elegans, to a 1.7 Å resolution. CeCht1 is a highly conserved chitinase among nematodes, and structural comparison with other chitinases revealed that CeCht1 has a classical TIM-barrel fold with some subtle structural differences in the substrate-binding cleft. Benefiting from the obtained crystal structure, we identified a series of novel inhibitors by hierarchical virtual screening. Analysis of the structure-activity relationships of these compounds provided insight into their interactions with the enzyme active site, which may inform future work in improving the potencies of their inhibitory activities. This work gives an insight into the structural features of nematode chitinases and provides a solid basis for the development of inhibitors.

Molecular Modeling of Histamine Receptors-Recent Advances in Drug Discovery

The recent developments of fast reliable docking, virtual screening and other algorithms gave rise to discovery of many novel ligands of histamine receptors that could be used for treatment of allergic inflammatory disorders, central nervous system pathologies, pain, cancer and obesity. Furthermore, the pharmacological profiles of ligands clearly indicate that these receptors may be considered as targets not only for selective but also for multi-target drugs that could be used for treatment of complex disorders such as Alzheimer's disease. Therefore, analysis of protein-ligand recognition in the binding site of histamine receptors and also other molecular targets has become a valuable tool in drug design toolkit. This review covers the period 2014-2020 in the field of theoretical investigations of histamine receptors mostly based on molecular modeling as well as the experimental characterization of novel ligands of these receptors.

Identification of a Selective RelA Inhibitor Based on DSE-FRET Screening Methods

Nuclear factor-κB (NF-κB) is an important transcription factor involved in various biological functions, including tumorigenesis. Hence, NF-κB has attracted attention as a target factor for cancer treatment, leading to the development of several inhibitors. However, existing NF-κB inhibitors do not discriminate between its subunits, namely, RelA, RelB, cRel, p50, and p52. Conventional methods used to evaluate interactions between transcription factors and DNA, such as electrophoretic mobility shift assay and luciferase assays, are unsuitable for high-throughput screening (HTS) and cannot distinguish NF-κB subunits. We developed a HTS method named DNA strand exchange fluorescence resonance energy transfer (DSE-FRET). This assay is suitable for HTS and can discriminate a NF-κB subunit. Using DSE-FRET, we searched for RelA-specific inhibitors and verified RelA inhibition for 32,955 compounds. The compound A55 (2-(3-carbamoyl-6-hydroxy-4-methyl-2-oxopyridin-1(2H)-yl) acetic acid) selectively inhibited RelA–DNA binding. We propose that A55 is a seed compound for RelA-specific inhibition and could be used in clinical applications.

In-silico Studies and Biological Activity of Potential BACE-1 Inhibitors

BACKGROUND

Alzheimer's disease is a neurological condition causing cognitive inability and dementia. The pathological lesions and neuronal damage in the brain are caused by self-aggregated fragments of mutated Amyloidal precursor protein (APP).

OBJECTIVE

The controlled APP processing by inhibition of secretase is the strategy to reduce Aβ load to treat Alzheimer's disease.

METHODS

A QSAR study was performed on 55 Pyrrolidine based ligands as BACE-1 inhibitors with an activity magnitude greater than 4 of compounds.

RESULTS

In the advent of designing new BACE-1 inhibitors, the pharmacophore model with correlation (r = 0.90) and root mean square deviation (RMSD) of 0.87 was developed and validated. Further, the hits retrieved by the in-silico approach were evaluated by docking interactions.

CONCLUSION

Two structurally diverse compounds exhibited Asp32 and Thr232 binding with the BACE-1 receptor. The aryl-substituted carbamate compound exhibited the highest fit value and docking score. The biological activity evaluation by in-vitro assay was found to be >0.1μM.

The flavonoid agathisflavone modulates the microglial neuroinflammatory response and enhances remyelination

Myelin loss is the hallmark of the demyelinating disease multiple sclerosis (MS) and plays a significant role in multiple neurodegenerative diseases. A common factor in all neuropathologies is the central role of microglia, the intrinsic immune cells of the central nervous system (CNS). Microglia are activated in pathology and can have both pro- and anti-inflammatory functions. Here, we examined the effects of the flavonoid agathisflavone on microglia and remyelination in the cerebellar slice model following lysolecithin induced demyelination. Notably, agathisflavone enhances remyelination and alters microglial activation state, as determined by their morphology and cytokine profile. Furthermore, these effects of agathisflavone on remyelination and microglial activation were inhibited by blockade of estrogen receptor α. Thus, our results identify agathisflavone as a novel compound that may act via ER to regulate microglial activation and enhance remyelination and repair.

In silico discovery and biological validation of ligands of FAD synthase, a promising new antimicrobial target

New treatments for diseases caused by antimicrobial-resistant microorganisms can be developed by identifying unexplored therapeutic targets and by designing efficient drug screening protocols. In this study, we have screened a library of compounds to find ligands for the flavin-adenine dinucleotide synthase (FADS) -a potential target for drug design against tuberculosis and pneumonia- by implementing a new and efficient virtual screening protocol. The protocol has been developed for the in silico search of ligands of unexplored therapeutic targets, for which limited information about ligands or ligand-receptor structures is available. It implements an integrative funnel-like strategy with filtering layers that increase in computational accuracy. The protocol starts with a pharmacophore-based virtual screening strategy that uses ligand-free receptor conformations from molecular dynamics (MD) simulations. Then, it performs a molecular docking stage using several docking programs and an exponential consensus ranking strategy. The last filter, samples the conformations of compounds bound to the target using MD simulations. The MD conformations are scored using several traditional scoring functions in combination with a newly-proposed score that takes into account the fluctuations of the molecule with a Morse-based potential. The protocol was optimized and validated using a compound library with known ligands of the Corynebacterium ammoniagenes FADS. Then, it was used to find new FADS ligands from a compound library of 14,000 molecules. A small set of 17 in silico filtered molecules were tested experimentally. We identified five inhibitors of the activity of the flavin adenylyl transferase module of the FADS, and some of them were able to inhibit growth of three bacterial species: C. ammoniagenes, Mycobacterium tuberculosis, and Streptococcus pneumoniae, where the last two are human pathogens. Overall, the results show that the integrative VS protocol is a cost-effective solution for the discovery of ligands of unexplored therapeutic targets.

The impact of compound library size on the performance of scoring functions for structure-based virtual screening

Larger training datasets have been shown to improve the accuracy of machine learning (ML)-based scoring functions (SFs) for structure-based virtual screening (SBVS). In addition, massive test sets for SBVS, known as ultra-large compound libraries, have been demonstrated to enable the fast discovery of selective drug leads with low-nanomolar potency. This proof-of-concept was carried out on two targets using a single docking tool along with its SF. It is thus unclear whether this high level of performance would generalise to other targets, docking tools and SFs. We found that screening a larger compound library results in more potent actives being identified in all six additional targets using a different docking tool along with its classical SF. Furthermore, we established that a way to improve the potency of the retrieved molecules further is to rank them with more accurate ML-based SFs (we found this to be true in four of the six targets; the difference was not significant in the remaining two targets). A 3-fold increase in average hit rate across targets was also achieved by the ML-based SFs. Lastly, we observed that classical and ML-based SFs often find different actives, which supports using both types of SFs on those targets.

Augmenting Hit Identification by Virtual Screening Techniques in Small Molecule Drug Discovery

Two orthogonal approaches for hit identification in drug discovery are large-scale in vitro and in silico screening. In recent years, due to the emergence of new targets and a rapid increase in the size of the readily synthesizable chemical space, there is a growing emphasis on the integration of the two techniques to improve the hit finding efficiency. Here, we highlight three examples of drug discovery projects at Merck & Co., Inc., Kenilworth, NJ, USA in which different virtual screening (VS) techniques, each specifically tailored to leverage knowledge available for the target, were utilized to augment the selection of high-quality chemical matter for in vitro assays and to enhance the diversity and tractability of hits. Central to success is a fully integrated workflow combining in silico and experimental expertise at every stage of the hit identification process. We advocate that workflows encompassing VS as part of an integrated hit finding plan should be widely adopted to accelerate hit identification and foster cross-functional collaborations in modern drug discovery.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

A Series of Compounds Bearing a Dipyrido-Pyrimidine Scaffold Acting as Novel Human and Insect Pest Chitinase Inhibitors

Chitinases not only play vital roles in the human innate immune system but are also essential for the development of pathogenic fungi and pests. Chitinase inhibitors are efficient tools to investigate the elusive role of human chitinases and to control pathogens and pests. Via hierarchical virtual screening, we have discovered a series of chitinase inhibitors with a novel scaffold that have high inhibitory activities and selectivities against human and insect chitinases. The most potent human chitotriosidase inhibitor, compound 40, exhibited a K_i of 49 nM, and the most potent inhibitor of the insect pest chitinase OfChi-h, compound 53, exhibited a K_i of 9 nM. The binding of these two most potent inhibitors was confirmed by X-ray crystallography. In a murine model of bleomycin-induced pulmonary fibrosis, compound 40 was found to suppress the chitotriosidase activity by 60%, leading to a significant increase in inflammatory cells and suggesting that chitotriosidase played a protective role.

Ligand-based pharmacophore modeling and docking studies on vitamin D receptor inhibitors

In recent years, pharmacophore modeling and molecular docking approaches have been extensively used to characterize the structural requirements and explore the conformational space of a ligand in the binding pocket of the selected target protein. Herein, we report a pharmacophore modeling and molecular docking of 45 compounds comprising of the indole scaffold as vitamin D receptor (VDR) inhibitors. Based on the selected best hypothesis (DRRRR.61), an atom-based three-dimensional quantitative structure-activity relationships model was developed to rationalize the structural requirement of biological activity modulating components. The developed model predicted the binding affinity for the training set and test set with R² _(training)  = 0.8869 and R² _(test)  = 0.8139, respectively. Furthermore, molecular docking and dynamics simulation were performed to understand the underpinning of binding interaction and stability of selected VDR inhibitors in the binding pocket. In conclusion, the results presented here, in the form of functional and structural data, agreed well with the proposed pharmacophores and provide further insights into the development of novel VDR inhibitors with better activity.