Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B

Exploring Natural Compounds as Potential CDK4 Inhibitors for Therapeutic Intervention in Neurodegenerative Diseases through Computational Analysis

CDK4 is a member of the serine-threonine kinase family, which has been found to be overexpressed in a plethora of studies related to neurodegenerative diseases. CDK4 is one of the most validated therapeutic targets for neurodegenerative diseases. Hence, the discovery of potent inhibitors of CDK4 is a promising candidate in the drug discovery field. Firstly, the reference drug Palbociclib was identified from the available literature as a potential candidate against target CDK4. In the present study, the Collection of Open Natural Products (COCONUT) database was accessed for determining potential CDK4 inhibitors using computational approaches based on the Tanimoto algorithm for similarity with the target drug, i.e., Palbociclib. The potential candidates were analyzed using SWISSADME, and the best candidates were filtered based on Lipinski's Rule of 5, Brenk, blood-brain barrier permeability, and Pains parameter. Further, the molecular docking protocol was accessed for the filtered compounds to anticipate the CDK4-ligand binding score, which was validated by the fastDRH web-based server. Based on the best docking score so obtained, the best four natural compounds were chosen for further molecular dynamic simulation to assess their stability with CDK4. In this study, two natural products, with COCONUT Database compound ID-CNP0396493 and CNP0070947, have been identified as the most suitable candidates for neuroprotection.

QSAR Study, Molecular Docking and Molecular Dynamic Simulation of Aurora Kinase Inhibitors Derived from Imidazo[4,5-b]pyridine Derivatives

Cancer is a serious threat to human life and social development and the use of scientific methods for cancer prevention and control is necessary. In this study, HQSAR, CoMFA, CoMSIA and TopomerCoMFA methods are used to establish models of 65 imidazo[4,5-b]pyridine derivatives to explore the quantitative structure-activity relationship between their anticancer activities and molecular conformations. The results show that the cross-validation coefficients q2 of HQSAR, CoMFA, CoMSIA and TopomerCoMFA are 0.892, 0.866, 0.877 and 0.905, respectively. The non-cross-validation coefficients r2 are 0.948, 0.983, 0.995 and 0.971, respectively. The externally validated complex correlation coefficients r2pred of external validation are 0.814, 0.829, 0.758 and 0.855, respectively. The PLS analysis verifies that the QSAR models have the highest prediction ability and stability. Based on these statistics, virtual screening based on R group is performed using the ZINC database by the Topomer search technology. Finally, 10 new compounds with higher activity are designed with the screened new fragments. In order to explore the binding modes and targets between ligands and protein receptors, these newly designed compounds are conjugated with macromolecular protein (PDB ID: 1MQ4) by molecular docking technology. Furthermore, to study the nature of the newly designed compound in dynamic states and the stability of the protein-ligand complex, molecular dynamics simulation is carried out for N3, N4, N5 and N7 docked with 1MQ4 protease structure for 50 ns. A free energy landscape is computed to search for the most stable conformation. These results prove the efficient and stability of the newly designed compounds. Finally, ADMET is used to predict the pharmacology and toxicity of the 10 designed drug molecules.

Targeting with Structural Analogs of Natural Products the Purine Salvage Pathway in Leishmania (Leishmania) infantum by Computer-Aided Drug-Design Approaches

Visceral Leishmaniasis (VL) has a high death rate, with 500,000 new cases and 50,000 deaths occurring annually. Despite the development of novel strategies and technologies, there is no adequate treatment for the disease. Therefore, the purpose of this study is to find structural analogs of natural products as potential novel drugs to treat VL. We selected structural analogs from natural products that have shown antileishmanial activities, and that may impede the purine salvage pathway using computer-aided drug-design (CADD) approaches. For these, we started with the vastly studied target in the pathway, the adenine phosphoribosyl transferase (APRT) protein, which alone is non-essential for the survival of the parasite. Keeping this in mind, we search for a substance that can bind to multiple targets throughout the pathway. Computational techniques were used to study the purine salvage pathway from Leishmania infantum, and molecular dynamic simulations were used to gather information on the interactions between ligands and proteins. Because of its low homology to human proteins and its essential role in the purine salvage pathway proteins network interaction, the findings further highlight the significance of adenylosuccinate lyase protein (ADL) as a therapeutic target. An analog of the alkaloid Skimmianine, N,N-diethyl-4-methoxy-1-benzofuran-6-carboxamide, demonstrated a good binding affinity to APRT and ADL targets, no expected toxicity, and potential for oral route administration. This study indicates that the compound may have antileishmanial activity, which was granted in vitro and in vivo experiments to settle this finding in the future.

Advances in Drug Discovery and Design using Computer-aided Molecular Modeling

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

Discovery of Novel Rhizoctonia solani DHFR Inhibitors as Fungicides Using Virtual Screening

Dihydrofolate reductase (DHFR) is an essential enzyme in the folate pathway and has been recognized as a well-known target for antibacterial and antifungal drugs. We discovered eight compounds from the ZINC database using virtual screening to inhibit Rhizoctonia solani (R. solani), a fungal pathogen in crops. These compounds were evaluated with in vitro assays for enzymatic and antifungal activity. Among these, compound Hit8 is the most active R. solani DHFR inhibitor, with the IC50 of 10.2 μM. The selectivity of inhibition is 22.3 against human DHFR with the IC50 of 227.7 μM. Moreover, Hit8 has higher antifungal activity against R. solani (EC50 of 38.2 mg L-1) compared with validamycin A (EC50 of 67.6 mg L-1), a well-documented fungicide. These results suggest that Hit8 may be a potential fungicide. Our study exemplifies a computer-aided method to discover novel inhibitors that could target plant pathogenic fungi.

A practical guide to machine-learning scoring for structure-based virtual screening

Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.

Navigating the Chemical Space and Chemical Multiverse of a Unified Latin American Natural Product Database: LANaPDB

The number of databases of natural products (NPs) has increased substantially. Latin America is extraordinarily rich in biodiversity, enabling the identification of novel NPs, which has encouraged both the development of databases and the implementation of those that are being created or are under development. In a collective effort from several Latin American countries, herein we introduce the first version of the Latin American Natural Products Database (LANaPDB), a public compound collection that gathers the chemical information of NPs contained in diverse databases from this geographical region. The current version of LANaPDB unifies the information from six countries and contains 12,959 chemical structures. The structural classification showed that the most abundant compounds are the terpenoids (63.2%), phenylpropanoids (18%) and alkaloids (11.8%). From the analysis of the distribution of properties of pharmaceutical interest, it was observed that many LANaPDB compounds satisfy some drug-like rules of thumb for physicochemical properties. The concept of the chemical multiverse was employed to generate multiple chemical spaces from two different fingerprints and two dimensionality reduction techniques. Comparing LANaPDB with FDA-approved drugs and the major open-access repository of NPs, COCONUT, it was concluded that the chemical space covered by LANaPDB completely overlaps with COCONUT and, in some regions, with FDA-approved drugs. LANaPDB will be updated, adding more compounds from each database, plus the addition of databases from other Latin American countries.

Human ACE2 orthologous peptide sequences show better binding affinity to SARS-CoV-2 RBD domain: Implications for drug design

Computational methods coupled with experimental validation play a critical role in the identification of novel inhibitory peptides that interact with viral antigenic determinants. The interaction between the receptor binding domain (RBD) of SARS-CoV-2 spike protein and the helical peptide of human angiotensin-converting enzyme-2 (ACE2) is a necessity for the initiation of viral infection. Herein, natural orthologs of human ACE2 helical peptide were evaluated for competitive inhibitory binding to the viral RBD by use of a computational approach, which was experimentally validated. A total of 624 natural ACE2 orthologous 32-amino acid long peptides were identified through a similarity search. Molecular docking was used to virtually screen and rank the peptides based on binding affinity metrics, benchmarked against human ACE2 peptide docked to the RBD. Molecular dynamics (MD) simulations were done for the human reference and the Nipponia nippon peptide as it exhibited the highest binding affinity (Gibbs free energy; -14 kcal/mol) predicted from the docking results. The MD simulation confirmed the stability of the assessed peptide in the complex (-12.3 kcal/mol). The top three docked-peptides (from Chitinophaga sancti, Nipponia nippon, and Mus musculus) and the human reference were experimentally validated by use of surface plasmon resonance technology. The human reference exhibited the weakest binding affinity (Kd of 318-441 pM) among the peptides tested, in agreement with the docking prediction, while the peptide from Nipponia nippon was the best, with 267-538-fold higher affinity than the reference. The validated peptides merit further investigation. This work showcases that the approach herein can aid in the identification of inhibitory biosimilar peptides for other viruses.

An overview of current strategies and future prospects in drug repurposing in tuberculosis

A large number of the population faces mortality as an effect of tuberculosis (TB). The line of treatment in the management of TB faces a jolt with ever-increasing multi-drug resistance (DR) cases. Further, the drugs engaged in the treatment of TB are associated with different toxicities, such as renal and hepatic toxicity. Different combinations are sought for effective anti-tuberculosis (anti-TB) effects with a decrease in toxicity. In this regard, drug repurposing has been very promising in improving the efficacy of drugs by enhancement of bioavailability and widening the safety margin. The success in drug repurposing lies in specified binding and inhibition of a particular target in the drug molecule. Different drugs have been repurposed for various ailments like cancer, Alzheimer’s disease, acquired immunodeficiency syndrome (AIDS), hair loss, etc. Repurposing in anti-TB drugs holds great potential too. The use of whole-cell screening assays and the availability of large chemical compounds for testing against Mycobacterium tuberculosis poses a challenge in this development. The target-based discovery of sites has emerged in the form of phenotypic screening as ethionamide R (EthR) and malate synthase inhibitors are similar to pharmaceuticals. In this review, the authors have thoroughly described the drug repurposing techniques on the basis of pharmacogenomics and drug metabolism, pathogen-targeted therapy, host-directed therapy, and bioinformatics approaches for the identification of drugs. Further, the significance of repurposing of drugs elaborated on large databases has been revealed. The role of genomics and network-based methods in drug repurposing has been also discussed in this article.

Chemokine Receptors-Structure-Based Virtual Screening Assisted by Machine Learning

Chemokines modulate the immune response by regulating the migration of immune cells. They are also known to participate in such processes as cell-cell adhesion, allograft rejection, and angiogenesis. Chemokines interact with two different subfamilies of G protein-coupled receptors: conventional chemokine receptors and atypical chemokine receptors. Here, we focused on the former one which has been linked to many inflammatory diseases, including: multiple sclerosis, asthma, nephritis, and rheumatoid arthritis. Available crystal and cryo-EM structures and homology models of six chemokine receptors (CCR1 to CCR6) were described and tested in terms of their usefulness in structure-based drug design. As a result of structure-based virtual screening for CCR2 and CCR3, several new active compounds were proposed. Known inhibitors of CCR1 to CCR6, acquired from ChEMBL, were used as training sets for two machine learning algorithms in ligand-based drug design. Performance of LightGBM was compared with a sequential Keras/TensorFlow model of neural network for these diverse datasets. A combination of structure-based virtual screening with machine learning allowed to propose several active ligands for CCR2 and CCR3 with two distinct compounds predicted as CCR3 actives by all three tested methods: Glide, Keras/TensorFlow NN, and LightGBM. In addition, the performance of these three methods in the prediction of the CCR2/CCR3 receptor subtype selectivity was assessed.

Study on the solubilization effect of 7-ethyl-10-hydroxycamptothecin based on molecular docking and molecular dynamics simulation

CONTEXT

With the continuous improvement of anticancer drugs, the condition of patients has been controlled to a certain extent, but the problem that still needs to be urgently solved is that most anticancer drug candidates' solubility is low. On the one hand, the low solubility of anticancer drugs may lead to a decrease in the absorption rate of anticancer drugs, poor treatment effect, and even death in severe cases. On the other hand, it will also lead to a waste of medical resources. At the same time, the rapid and scientific screening of ideal anticancer drugs has become a difficult problem that researchers have to face in the research process. In this study, we found two kinds of SN38-ligand complexes that solubilize 7-ethyl-10-hydroxycamptothecin (SN38) through molecular docking and molecular dynamics simulation methods. This process not only provided valuable information on improving the solubility of SN38, but also helped to discover effective potential complexes that solubilize SN38 quickly and scientifically.

METHODS

The interaction of the SN38 with folic acid and isoproterenol hydrochloride was rapidly determined by molecular docking and molecular dynamics simulation methods. We used Discovery Studio software to perform molecular docking. And then, we used Gromacs 2019.3 software to perform molecular dynamics, analyzing and comparing the hydrogen bonds, solvent-accessible surface areas, energies, and so on between SN38 and SN38-ligand complexes. And the force field adopted the Gromos 54a7.

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

Searching for potential inhibitors of SARS-COV-2 main protease using supervised learning and perturbation calculations

Inhibiting the biological activity of SARS-CoV-2 Mpro can prevent viral replication. In this context, a hybrid approach using knowledge- and physics-based methods was proposed to characterize potential inhibitors for SARS-CoV-2 Mpro. Initially, supervised machine learning (ML) models were trained to predict a ligand-binding affinity of ca. 2 million compounds with the correlation on a test set of R = 0.748 ± 0.044 . Atomistic simulations were then used to refine the outcome of the ML model. Using LIE/FEP calculations, nine compounds from the top 100 ML inhibitors were suggested to bind well to the protease with the domination of van der Waals interactions. Furthermore, the binding affinity of these compounds is also higher than that of nirmatrelvir, which was recently approved by the US FDA to treat COVID-19. In addition, the ligands altered the catalytic triad Cys145 - His41 - Asp187, possibly disturbing the biological activity of SARS-CoV-2.

Drug Repurposing at the Interface of Melanoma Immunotherapy and Autoimmune Disease

Cancer cells have a remarkable ability to evade recognition and destruction by the immune system. At the same time, cancer has been associated with chronic inflammation, while certain autoimmune diseases predispose to the development of neoplasia. Although cancer immunotherapy has revolutionized antitumor treatment, immune-related toxicities and adverse events detract from the clinical utility of even the most advanced drugs, especially in patients with both, metastatic cancer and pre-existing autoimmune diseases. Here, the combination of multi-omics, data-driven computational approaches with the application of network concepts enables in-depth analyses of the dynamic links between cancer, autoimmune diseases, and drugs. In this review, we focus on molecular and epigenetic metastasis-related processes within cancer cells and the immune microenvironment. With melanoma as a model, we uncover vulnerabilities for drug development to control cancer progression and immune responses. Thereby, drug repurposing allows taking advantage of existing safety profiles and established pharmacokinetic properties of approved agents. These procedures promise faster access and optimal management for cancer treatment. Together, these approaches provide new disease-based and data-driven opportunities for the prediction and application of targeted and clinically used drugs at the interface of immune-mediated diseases and cancer towards next-generation immunotherapies.

Nanoparticle Emulsions Enhance the Inhibition of NLRP3

Antibacterial delivery emulsions are potential materials for treating bacterial infections. Few studies have focused on the role and mechanism of emulsions in inflammation relief. Therefore, based on our previous analysis, in which the novel and natural Pickering emulsions stabilized by antimicrobial peptide nanoparticles were prepared, the regulation effect of emulsion on inflammasome was explored in silico, in vitro and in vivo. Firstly, the interactions between inflammasome components and parasin I or Pickering emulsion were predicted by molecular docking. Then, the inflammasome stimulation by different doses of the emulsion was tested in RAW 264.7 and THP-1 cells. Finally, in Kunming mice with peritonitis, NLRP3 and IL-1β expression in the peritoneum were evaluated. The results showed that the Pickering emulsion could combine with ALK, casp-1, NEK7, or NLRP3 to affect the assembly of the NLRP3 and further relieve inflammation. LPNE showed a dose–dependent inhibition effect on the release of IL-1β and casp-1. With the concentration of parasin I increased from 1.5 mg/mL to 3 mg/mL, the LDH activity decreased in the chitosan peptide-embedded nanoparticles emulsion (CPENE) and lipid/peptide nanoparticles emulsion (LPNE) groups. However, from 1.5 to 6 mg/mL, LPNE had a dose–dependent effect on the release of casp-1. The CPENE and parasin I-conjugated chitosan nanoparticles emulsion (PCNE) may decrease the release of potassium and chloride ions. Therefore, it can be concluded that the LPNE may inhibit the activation of the inflammasome by decreasing LDH activity, potassium and chloride ions through binding with compositions of NLRP3.

Progress and Impact of Latin American Natural Product Databases

Natural products (NPs) are a rich source of structurally novel molecules, and the chemical space they encompass is far from being fully explored. Over history, NPs have represented a significant source of bioactive molecules and have served as a source of inspiration for developing many drugs on the market. On the other hand, computer-aided drug design (CADD) has contributed to drug discovery research, mitigating costs and time. In this sense, compound databases represent a fundamental element of CADD. This work reviews the progress toward developing compound databases of natural origin, and it surveys computational methods, emphasizing chemoinformatic approaches to profile natural product databases. Furthermore, it reviews the present state of the art in developing Latin American NP databases and their practical applications to the drug discovery area.

In-silico probing of AML related RUNX1 cancer-associated missense mutations: Predicted relationships to DNA binding and drug interactions

The molecular consequences of cancer associated mutations in Acute myeloid leukemia (AML) linked factors are not very well understood. Here, we interrogated the COSMIC database for missense mutations associated with the RUNX1 protein, that is frequently mis-regulated in AML, where we sought to identify recurrently mutated positions at the DNA-interacting interface. Indeed, six of the mutated residues, out of a total 417 residues examined within the DNA binding domain, evidenced reduced DNA association in in silico predictions. Further, given the prominence of RUNX1’s compromised function in AML, we asked the question if the mutations themselves might alter RUNX1’s interaction (off-target) with known FDA-approved drug molecules, including three currently used in treating AML. We identified several AML-associated mutations in RUNX1 that were calculated to enhance RUNX1’s interaction with specific drugs. Specifically, we retrieved data from the COSMIC database for cancer-associated mutations of RUNX1 by using R package “data.table” and “ggplot2” modules. In the presence of DNA and/or drug, we used docking scores and energetics of the complexes as tools to evaluate predicted interaction strengths with RUNX1. For example, we performed predictions of drug binding pockets involving Enasidenib, Giltertinib, and Midostaurin (AML associated), as well as ten different published cancer associated drug compounds. Docking of wild type RUNX1 with these 13 different cancer-associated drugs indicates that wild-type RUNX1 has a lower efficiency of binding while RUNX1 mutants R142K, D171N, R174Q, P176H, and R177Q suggested higher affinity of drug association. Literature evidence support our prediction and suggests the mutation R174Q affects RUNX1 DNA binding and could lead to compromised function. We conclude that specific RUNX1 mutations that lessen DNA binding facilitate the binding of a number of tested drug molecules. Further, we propose that molecular modeling and docking studies for RUNX1 in the presence of DNA and/or drugs enables evaluation of the potential impact of RUNX1 cancer associated mutations in AML.

Computational Methods in Cooperation with Experimental Approaches to Design Protein Tyrosine Phosphatase 1B Inhibitors in Type 2 Diabetes Drug Design: A Review of the Achievements of This Century

Protein tyrosine phosphatase 1B (PTP1B) dephosphorylates phosphotyrosine residues and is an important regulator of several signaling pathways, such as insulin, leptin, and the ErbB signaling network, among others. Therefore, this enzyme is considered an attractive target to design new drugs against type 2 diabetes, obesity, and cancer. To date, a wide variety of PTP1B inhibitors that have been developed by experimental and computational approaches. In this review, we summarize the achievements with respect to PTP1B inhibitors discovered by applying computer-assisted drug design methodologies (virtual screening, molecular docking, pharmacophore modeling, and quantitative structure–activity relationships (QSAR)) as the principal strategy, in cooperation with experimental approaches, covering articles published from the beginning of the century until the time this review was submitted, with a focus on studies conducted with the aim of discovering new drugs against type 2 diabetes. This review encourages the use of computational techniques and includes helpful information that increases the knowledge generated to date about PTP1B inhibition, with a positive impact on the route toward obtaining a new drug against type 2 diabetes with PTP1B as a molecular target.

Docking-Based Virtual Screening Enables Prioritizing Protein Kinase Inhibitors With In Vitro Phenotypic Activity Against Schistosoma mansoni

Schistosomiasis is a parasitic neglected disease with praziquantel (PZQ) utilized as the main drug for treatment, despite its low effectiveness against early stages of the worm. To aid in the search for new drugs to tackle schistosomiasis, computer-aided drug design has been proved a helpful tool to enhance the search and initial identification of schistosomicidal compounds, allowing fast and cost-efficient progress in drug discovery. The combination of high-throughput in silico data followed by in vitro phenotypic screening assays allows the assessment of a vast library of compounds with the potential to inhibit a single or even several biological targets in a more time- and cost-saving manner. Here, we describe the molecular docking for in silico screening of predicted homology models of five protein kinases (JNK, p38, ERK1, ERK2, and FES) of Schistosoma mansoni against approximately 85,000 molecules from the Managed Chemical Compounds Collection (MCCC) of the University of Nottingham (UK). We selected 169 molecules predicted to bind to SmERK1, SmERK2, SmFES, SmJNK, and/or Smp38 for in vitro screening assays using schistosomula and adult worms. In total, 89 (52.6%) molecules were considered active in at least one of the assays. This approach shows a much higher efficiency when compared to using only traditional high-throughput in vitro screening assays, where initial positive hits are retrieved from testing thousands of molecules. Additionally, when we focused on compound promiscuity over selectivity, we were able to efficiently detect active compounds that are predicted to target all kinases at the same time. This approach reinforces the concept of polypharmacology aiming for “one drug-multiple targets”. Moreover, at least 17 active compounds presented satisfactory drug-like properties score when compared to PZQ, which allows for optimization before further in vivo screening assays. In conclusion, our data support the use of computer-aided drug design methodologies in conjunction with high-throughput screening approach.

Designing of potent anti-diabetic molecules by targeting SIK2 using computational approaches

Diabetes mellitus (DM) is one of the major health problems worldwide. WHO have estimated that 439 million people may have DM by the year 2030. Several classes of drugs such as sulfonylureas, meglitinides, thiazolidinediones etc. are available to manage this disease, however, there is no cure for this disease. Salt inducible kinase 2 (SIK2) is expressed several folds in adipose tissue than in normal tissues and thus SIK2 is one of the attractive targets for DM treatment. SIK2 inhibition improves glucose homeostasis. Several analogues have been reported and experimentally proven against SIK for DM treatment. But, identifying potential SIK2 inhibitors with improved efficacy and good pharmacokinetic profiles will be helpful for the effective treatment of DM. The objective of the present study is to identify selective SIK2 inhibitors with good pharmacokinetic profiles. Due to the unavailability of SIK2 structure, the modeled structure of SIK2 will be an important to understand the atomic level of SIK2 inhibitors in the binding site pocket. In this study, different molecular modeling studies such as Homology Modeling, Molecular Docking, Pharmacophore-based virtual screening, MD simulations, Density Functional Theory calculations and WaterMap analysis were performed to identify potential SIK2 inhibitors. Five molecules from different databases such as Binding_4067, TosLab_837067, NCI_349155, Life chemicals_ F2565-0113, Enamine_7623111186 molecules were identified as possible SIK2 inhibitors.

What are the challenges with multi-targeted drug design for complex diseases?

INTRODUCTION

Current findings on multifactorial diseases with a complex pathomechanism confirm that multi-target drugs are more efficient ways in treating them as opposed to single-target drugs. However, to design multi-target ligands, a number of factors and challenges must be taken into account.

AREAS COVERED

In this perspective, we summarize the concept of application of multi-target drugs for the treatment of complex diseases such as neurodegenerative diseases, schizophrenia, diabetes, and cancer. We discuss the aspects of target selection for multifunctional ligands and the application of in silico methods in their design and optimization. Furthermore, we highlight other challenges such as balancing affinities to different targets and drug-likeness of obtained compounds. Finally, we present success stories in the design of multi-target ligands for the treatment of common complex diseases.

EXPERT OPINION

Despite numerous challenges resulting from the design of multi-target ligands, these efforts are worth making. Appropriate target selection, activity balancing, and ligand drug-likeness belong to key aspects in the design of ligands acting on multiple targets. It should be emphasized that in silico methods, in particular inverse docking, pharmacophore modeling, machine learning methods and approaches derived from network pharmacology are valuable tools for the design of multi-target drugs.

A Review on Parallel Virtual Screening Softwares for High-Performance Computers

Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein-ligand complexes (on the order of 106 to 1012), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures.

Present and future challenges in therapeutic designing using computational approaches

Currently, various computational methods are being used for the purpose of therapeutic design. The advent of the Coronavirus disease-2019 (COVID-19) pandemic has created a lot of problems due to which the development of effective treatment options is urgently needed. Computational intelligence is used in the control, prevention, prediction, diagnosis, and treatment of the disease. Several important drug targets have been identified in severe acute respiratory syndrome-Coronavirus-2 using in silico methods. Computer-aided drug design includes a variety of theoretical and computational approaches that are part of modern drug discovery. Advances in machine learning methods and their applications speed up the drug discovery process. Exploration of nucleic acid-based therapeutics is playing an important role in healthcare also. But a lot of challenges have also been seen that complicate the therapeutic design. Therefore, investigation of challenges associated with therapeutic design is important, and the present chapter is aimed to cover various therapeutic design approaches and challenges associated with them. Moreover, the role of computational strategies in the exploration of potential therapeutics against COVID-19 has been investigated.

Discovery of novel antifungal resorcylate aminopyrazole Hsp90 inhibitors based on structural optimization by molecular simulations

Novel antifungal resorcylate aminopyrazole Hsp90 inhibitors were discovered by 3D-QSAR, molecular docking and molecular dynamics simulations.

Commercial SARS-CoV-2 Targeted, Protease Inhibitor Focused and Protein-Protein Interaction Inhibitor Focused Molecular Libraries for Virtual Screening and Drug Design

Since December 2019, the new SARS-CoV-2-related COVID-19 disease has caused a global pandemic and shut down the public life worldwide. Several proteins have emerged as potential therapeutic targets for drug development, and we sought out to review the commercially available and marketed SARS-CoV-2-targeted libraries ready for high-throughput virtual screening (HTVS). We evaluated the SARS-CoV-2-targeted, protease-inhibitor-focused and protein-protein-interaction-inhibitor-focused libraries to gain a better understanding of how these libraries were designed. The most common were ligand- and structure-based approaches, along with various filtering steps, using molecular descriptors. Often, these methods were combined to obtain the final library. We recognized the abundance of targeted libraries offered and complimented by the inclusion of analytical data; however, serious concerns had to be raised. Namely, vendors lack the information on the library design and the references to the primary literature. Few references to active compounds were also provided when using the ligand-based design and usually only protein classes or a general panel of targets were listed, along with a general reference to the methods, such as molecular docking for the structure-based design. No receptor data, docking protocols or even references to the applied molecular docking software (or other HTVS software), and no pharmacophore or filter design details were given. No detailed functional group or chemical space analyses were reported, and no specific orientation of the libraries toward the design of covalent or noncovalent inhibitors could be observed. All libraries contained pan-assay interference compounds (PAINS), rapid elimination of swill compounds (REOS) and aggregators, as well as focused on the drug-like model, with the majority of compounds possessing their molecular mass around 500 g/mol. These facts do not bode well for the use of the reviewed libraries in drug design and lend themselves to commercial drug companies to focus on and improve.

Artificial intelligence unifies knowledge and actions in drug repositioning

Drug repositioning aims to reuse existing drugs, shelved drugs, or drug candidates that failed clinical trials for other medical indications. Its attraction is sprung from the reduction in risk associated with safety testing of new medications and the time to get a known drug into the clinics. Artificial Intelligence (AI) has been recently pursued to speed up drug repositioning and discovery. The essence of AI in drug repositioning is to unify the knowledge and actions, i.e. incorporating real-world and experimental data to map out the best way forward to identify effective therapeutics against a disease. In this review, we share positive expectations for the evolution of AI and drug repositioning and summarize the role of AI in several methods of drug repositioning.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

Vanadate inhibits Feo-mediated iron transport in Vibrio cholerae

Iron is an essential element for Vibrio cholerae to survive, and Feo, the major bacterial system for ferrous iron transport, is important for growth of this pathogen in low-oxygen environments. To gain insight into its biochemical mechanism, we evaluated the effects of widely used ATPase inhibitors on the ATP hydrolysis activity of the N-terminal domain of V. cholerae FeoB. Our results showed that sodium orthovanadate and sodium azide effectively inhibit the catalytic activity of the N-terminal domain of V. cholerae FeoB. Further, sodium orthovanadate was the more effective inhibitor against V. cholerae ferrous iron transport in vivo. These results contribute to a more comprehensive biochemical understanding of Feo function, and shed light on designing effective inhibitors against bacterial FeoB proteins.

Using computers to ESKAPE the antibiotic resistance crisis

Since the discovery of penicillin, the development and use of antibiotics have promoted safe and effective control of bacterial infections. However, the number of antibiotic-resistance cases has been ever increasing over time. Thus, the drug discovery process demands fast, efficient and cost-effective alternative approaches for developing lead candidates with outstanding performance. Computational approaches are appealing techniques to develop lead candidates in an in silico fashion. In this review, we provide an overview of the implementation of current in silico state-of-the-art techniques, including machine learning (ML) and deep learning (DL), in drug discovery. We also discuss the development of quantum computing and its potential benefits for antibiotics research and current bottlenecks that limit computational drug discovery advancement.

Machine learning based energy-free structure predictions of molecules, transition states, and solids

The computational prediction of atomistic structure is a long-standing problem in physics, chemistry, materials, and biology. Conventionally, force-fields or ab initio methods determine structure through energy minimization, which is either approximate or computationally demanding. This accuracy/cost trade-off prohibits the generation of synthetic big data sets accounting for chemical space with atomistic detail. Exploiting implicit correlations among relaxed structures in training data sets, our machine learning model Graph-To-Structure (G2S) generalizes across compound space in order to infer interatomic distances for out-of-sample compounds, effectively enabling the direct reconstruction of coordinates, and thereby bypassing the conventional energy optimization task. The numerical evidence collected includes 3D coordinate predictions for organic molecules, transition states, and crystalline solids. G2S improves systematically with training set size, reaching mean absolute interatomic distance prediction errors of less than 0.2 Å for less than eight thousand training structures - on par or better than conventional structure generators. Applicability tests of G2S include successful predictions for systems which typically require manual intervention, improved initial guesses for subsequent conventional ab initio based relaxation, and input generation for subsequent use of structure based quantum machine learning models.

Appraisal of the Role of In silico Methods in Pyrazole Based Drug Design

Pyrazole and its derivatives are a pharmacologically and significantly active scaffolds that have innumerable physiological and pharmacological activities. They can be very good targets for the discovery of novel anti-bacterial, anti-cancer, anti-inflammatory, anti-fungal, anti-tubercular, antiviral, antioxidant, antidepressant, anti-convulsant and neuroprotective drugs. This review focuses on the importance of in silico manipulations of pyrazole and its derivatives for medicinal chemistry. The authors have discussed currently available information on the use of computational techniques like molecular docking, structure-based virtual screening (SBVS), molecular dynamics (MD) simulations, quantitative structure activity relationship (QSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to drug design using pyrazole moieties. Pyrazole based drug design is mainly dependent on the integration of experimental and computational approaches. The authors feel that more studies need to be done to fully explore the pharmacological potential of the pyrazole moiety and in silico method can be of great help.

Spectrum of deep learning algorithms in drug discovery

Deep learning (DL) algorithms are a subset of machine learning algorithms with the aim of modeling complex mapping between a set of elements and their classes. In parallel to the advance in revealing the molecular bases of diseases, a notable innovation has been undertaken to apply DL in data/libraries management, reaction optimizations, differentiating uncertainties, molecule constructions, creating metrics from qualitative results, and prediction of structures or interactions. From source identification to lead discovery and medicinal chemistry of the drug candidate, drug delivery, and modification, the challenges can be subjected to artificial intelligence algorithms to aid in the generation and interpretation of data. Discovery and design approach, both demand automation, large data management and data fusion by the advance in high-throughput mode. The application of DL can accelerate the exploration of drug mechanisms, finding novel indications for existing drugs (drug repositioning), drug development, and preclinical and clinical studies. The impact of DL in the workflow of drug discovery, design, and their complementary tools are highlighted in this review. Additionally, the type of DL algorithms used for this purpose, and their pros and cons along with the dominant directions of future research are presented.

Genome-Wide Transcriptomic Analysis of Non-Tumorigenic Tissues Reveals Aging-Related Prognostic Markers and Drug Targets in Renal Cell Carcinoma

The relationship between expression of aging-related genes in normal tissues and cancer patient survival has not been assessed. We developed a genome-wide transcriptomic analysis approach for normal tissues adjacent to the tumor to identify aging-related transcripts associated with survival outcome, and applied it to 12 cancer types. As a result, five aging-related genes (DUSP22, MAPK14, MAPKAPK3, STAT1, and VCP) in normal tissues were found to be significantly associated with a worse survival outcome in patients with renal cell carcinoma (RCC). This computational approach was investigated using nontumorigenic immune cells purified from young and aged mice. Aged immune cells showed upregulated expression of all five aging-related genes and promoted RCC invasion compared to young immune cells. Further studies revealed DUSP22 as a regulator and druggable target of metastasis. DUSP22 gene knockdown reduced RCC invasion and the small molecule inhibitor BML-260 prevented RCC dissemination in a tumor/immune cell xenograft model. Overall, these results demonstrate that deciphering the relationship between aging-related gene expression in normal tissues and cancer patient survival can provide new prognostic markers, regulators of tumorigenesis and novel targets for drug development.

QSAR analysis of 3-pyrimidin-4-yl-oxazolidin-2-one derivatives isocitrate dehydrogenase inhibitors using Topomer CoMFA and HQSAR methods

A series of mIDH1 inhibitors derived from 3-pyrimidine-4-oxazolidin-2-ketone derivatives were studied by QSAR model to explore the key factors that inhibit mIDH1 activity. The generated model was cross-verified and non-cross-verified by Topomer CoMFA and HQSAR methods; the independent test set was verified by PLS method; the Topomer search technology was used for virtual screening and molecular design; and the Surflex-Dock method and ADMET technology were used for molecular docking, pharmacology and toxicity prediction of the designed drug molecules. The Topomer CoMFA and HQSAR cross-validation coefficients q² are 0.783 and 0.784, respectively, and the non-cross-validation coefficients r² are 0.978 and 0.934, respectively. Ten new drug molecules have been designed using Topomer search technology. The results of molecular docking and ADMET show that the newly designed drug molecules are effective. The docking situation, pharmacology and toxicity prediction results are good. The model can be used to predict the bioactivity of the same type of new compounds and their derivatives. The prediction results of molecular design, molecular docking and ADMET can provide some ideas for the design and development of novel mIDH1 inhibitor anticancer drugs, and provide certain theoretical basis of the experimental verification of new compounds in the future. Newly designed molecules after docking with corresponding proteins in the PDB library, it can explore the targets of drug molecules acting with large proteins and the related force, which is very helpful for the design of new drugs and the mechanism of drug action.

Recent progress in natural product-based inhibitor screening with enzymatic fluorescent probes

Drug discovery is a complex process in which many challenges need to be overcome, from the discovery of a drug candidate to ensuring the efficacy and safety of the candidate in humans. Modern analytical methods allow tens of thousands of drug candidates to be screened for their inhibition of specific enzymes or receptors. In recent years, fluorescent probes have been used for the detection and diagnosis of human pathogens as well as high-throughput screening. This review focuses on recent progress in organic small-molecule based enzyme-activated fluorescent probes for screening of inhibitors from natural products. The contents include the construction of fluorescent probes, working mechanism and the process of inhibitor screening. The progress suggests that fluorescent probes are a vital and rapidly growing technology for inhibitor screening of enzymes, in particular, inhibitor screening in situ.

Docking and molecular dynamics predicted B-DNA and dihydropyrimidinone selenoesters interactions elucidating antiproliferative effects on breast adenocarcinoma cells

Dihydropyrimidinones have demonstrated different biological activities including anticancer properties. Cytotoxic potential and antiproliferative potential of new dihydropyrimidinone-derived selenoesters (Se-DHPM) compounds were assessed in vitro against the breast adenocarcinoma cells (MCF-7). Among the eight Se-DHPM compounds tested just 49A and 49F were the most cytotoxic for MCF-7 and the most selective for the non-tumor strain (McCoy) and reduced cell viability in a time- and concentration-dependent manner. Compounds 49A and 49F increased the rate of cell death due to apoptosis and necrosis comparatively to the control, however only the 49F showed antiproliferative potential, reducing the number of colonies formed. In the molecular assay 49A interacts with CT-DNA and caused hyperchromism while 49F caused a hypochromic effect. The intercalation test revealed that the two compounds caused destabilization in the CT-DNA molecule. This effect was evidenced by the loss of fluorescence when the compounds competed and caused the displacement of propidium iodide. Simulations (docking and molecular dynamics) using B-DNA brought a greater understanding of ligand-B-DNA interactions. Furthermore, they predicted that the compounds act as minor groove ligands that are stabilized through hydrogen bonds and hydrophobic interactions. However, the form of interaction foreseen for 49A was more energetically favorable and had more stable hydrogen bonds during the simulation time. Despite some violations foreseen in the ADMET for 49F, the set of other results point to this Se-DHPM as a promising leader compound with anti-tumor potential for breast cancer.Communicated by Ramaswamy H. Sarma.

Virtual Screening Based Discovery of PTP1B Inhibitors and Their Biological Evaluations

Background :

The discovery of novel antidiabetics for the treatment of type 2 diabetes mellitus (T2DM) is an important task nowadays because the current treatment approaches have certain limitations. The reported studies showed that the protein tyrosine phosphatase 1B (PTP1B) is a valuable target, can be used to develop significant antidiabetic molecules.

Objective:

In the present investigation, computational methods and biological evaluation studies have been applied to develop novel PTP1B inhibitors with good enzyme binding affinity and activity.

Methods:

Virtual screening (docking) analysis of SPECS database compounds on PTP1B enzyme was performed using Schrodinger software. In vitro and in vivo biological evaluations had been conducted with the identified hits.

Results:

The results revealed that the molecules identified through these studies have shown significant interactions with the active site residues of the PTP1B enzyme. The compounds S1 and S2 provided significant binding interactions with the residues (Arg221 and Gln262) and have shown considerable in vitro PTP1B inhibitory activity and in vivo antidiabetic activity. The compounds S1 and S2 possessed 35.44±0.12% and 33.68±0.08% inhibitory activities, respectively.

Conclusion:

These identified hits will be used as a template for design and development of novel PTP1B inhibitors with a compatible pharmacokinetic profile.

TREX1 as a Novel Immunotherapeutic Target

Mutations in the TREX1 3' → 5' exonuclease are associated with a spectrum of autoimmune disease phenotypes in humans and mice. Failure to degrade DNA activates the cGAS-STING DNA-sensing pathway signaling a type-I interferon (IFN) response that ultimately drives immune system activation. TREX1 and the cGAS-STING DNA-sensing pathway have also been implicated in the tumor microenvironment, where TREX1 is proposed to degrade tumor-derived DNA that would otherwise activate cGAS-STING. If tumor-derived DNA were not degraded, the cGAS-STING pathway would be activated to promote IFN-dependent antitumor immunity. Thus, we hypothesize TREX1 exonuclease inhibition as a novel immunotherapeutic strategy. We present data demonstrating antitumor immunity in the TREX1 D18N mouse model and discuss theory surrounding the best strategy for TREX1 inhibition. Potential complications of TREX1 inhibition as a therapeutic strategy are also discussed.

Application and assessment of deep learning for the generation of potential NMDA receptor antagonists

Uncompetitive antagonists of the N-methyl d-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists in silico is therefore desirable for both new medication development and preempting and identifying new designer drugs. Recently, generative deep learning models have been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds. In this study, we assess the application of a generative model to the NMDAR to achieve two primary objectives: (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages conferred by applying such generative artificial intelligence models to drug design and the current limitations of the approach. We apply, and provide source code for, a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds. We present twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve, though synthesis and experimental validation of these compounds are still required.

Application of In Silico and HTS Approaches to Identify Nuclear Import Inhibitors for Venezuelan Equine Encephalitis Virus Capsid Protein: A Case Study

The development of new drugs is costly and time-consuming, with estimates of over $US1 billion and 15 years for a product to reach the market. As understanding of the molecular basis of disease improves, various approaches have been used to target specific molecular interactions in the search for effective drugs. These include high-throughput screening (HTS) for novel drug identification and computer-aided drug design (CADD) to assess the properties of putative drugs before experimental work begins. We have applied conventional HTS and CADD approaches to the problem of identifying antiviral compounds to limit infection by Venezuelan equine encephalitis virus (VEEV). Nuclear targeting of the VEEV capsid (CP) protein through interaction with the host nuclear import machinery has been shown to be essential for viral pathogenicity, with viruses incapable of this interaction being greatly attenuated. Our previous conventional HTS and in silico structure-based drug design (SBDD) screens were successful in identifying novel inhibitors of CP interaction with the host nuclear import machinery, thus providing a unique opportunity to assess the relative value of the two screening approaches directly. This focused review compares and contrasts the two screening approaches, together with the properties of the inhibitors identified, as a case study for parallel use of the two approaches to identify antivirals. The utility of SBDD screens, especially when used in parallel with traditional HTS, in identifying agents of interest to target the host-pathogen interface is highlighted.

Molecular simulation studies to reveal the binding mechanisms of shikonin derivatives inhibiting VEGFR-2 kinase

Traditional vascular endothelial growth factor receptor 2 (VEGFR-2) inhibitors can manage angiogenesis; however, severe toxicity and resistance limit their long-term applications in clinical therapy. Shikonin (SHK) and its derivatives could be promising to inhibit the VEGFR-2 mediated angiogenesis, as they are reported to bind in the catalytic kinase domain with low affinity. However, the detailed molecular insights and binding dynamics of these natural inhibitors are unknown, which is crucial for potential SHK based lead design. Therefore, the present study employed molecular modeling and simulations techniques to get insight into the binding behaviors of SHK and its two derivates, β-hydroxyisovalerylshikonin (β-HIVS) and acetylshikonin (ACS). Here the intermolecular interactions between protein and ligands were studied by induced fit docking approach, which were further evaluated by treating QM/MM (quantum mechanics/molecular mechanics) and molecular dynamics (MD) simulation. The result showed that the naphthazarin ring of the SHK derivates is vital for strong binding to the catalytic domain; however, the binding stability can be modulated by the side chain modification. Because of having electrostatic potential, this ring makes essential interactions with the DFG (Asp1046 and Phe1047) motif and also allows interacting with the allosteric binding site. Taken together, the studies will advance our knowledge and scope for the development of new selective VEGFR-2 inhibitors based on SHK and its analogs.

Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation

BACKGROUND

The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases have overwhelmed health and public health services. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have extended their major role in tracking disease patterns, and in identifying possible treatments.

OBJECTIVE

This study aims to identify potential COVID-19 protease inhibitors through shape-based Machine Learning assisted by Molecular Docking and Molecular Dynamics simulations.

METHODS

31 Repurposed compounds have been selected targeting the main coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from the 3D shape to the pharmacophoric features of their seed compound. Ligand-Receptor Docking was performed with Optimized Potential for Liquid Simulations (OPLS) algorithms to identify highaffinity compounds from the list of selected candidates for 6LU7, which were subjected to Molecular Dynamic Simulations followed by ADMET studies and other analyses.

RESULTS

Shape-based Machine learning reported remdesivir, valrubicin, aprepitant, and fulvestrant as the best therapeutic agents with the highest affinity for the target protein. Among the best shape-based compounds, a novel compound identified was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorv-EMBS'. Further, toxicity analysis showed nCorv-EMBS to be suitable for further consideration as the main protease inhibitor in COVID-19.

CONCLUSION

Effective ACE-II, GAK, AAK1, and protease 3C blockers can serve as a novel therapeutic approach to block the binding and attachment of the main COVID-19 protease (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 receptors in the lung. The novel compound nCorv- EMBS herein proposed stands as a promising inhibitor to be evaluated further for COVID-19 treatment.

Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction

Natural products comprise a rich reservoir for innovative drug leads and are a constant source of bioactive compounds. To find pharmacological targets for new or already known natural products using modern computer-aided methods is a current endeavor in drug discovery. Nature's treasures, however, could be used more effectively. Yet, reliable pipelines for the large-scale target prediction of natural products are still rare. We developed an in silico workflow consisting of four independent, stand-alone target prediction tools and evaluated its performance on dihydrochalcones (DHCs)-a well-known class of natural products. Thereby, we revealed four previously unreported protein targets for DHCs, namely 5-lipoxygenase, cyclooxygenase-1, 17β-hydroxysteroid dehydrogenase 3, and aldo-keto reductase 1C3. Moreover, we provide a thorough strategy on how to perform computational target predictions and guidance on using the respective tools.

Identify thrombin inhibitor with novel skeleton based on virtual screening study

Virtual screening refers to the screening of active compounds based on a small-molecule database. This procedure can rapidly select active compounds with pharmaceutical properties from millions of molecules, thus considerably reducing the number of experimental screening compounds and cost of drug development and shortening the research cycle. In this paper, a pharmacophore screening method was used for virtual screening to determine new scaffold compounds with potential anticoagulant activities. The pharmacophore model (Model_01-20) was constructed in SYBYL-X 2.0 based on dabigatran derivatives (D1-D9) with micromolar to nanomolar activities and tested by decoy test method. Model_01 was selected to screen more than 1600 million compounds in the Zinc 12.0 database. Furtherly, molecular docking analysis and ADME prediction were conducted on more than 100,000 screened compounds. Finally, two compounds (Z-19 and Z-29) were selected for anticoagulant activity test in vitro, Compound Z-29 with tryptophan aurone structure was found possess anticoagulant effect and its IC₅₀ = 22.9 ± 6.88 μM. ADME prediction results show that compound Z-29 features a high intestinal absorption rate, which is valuable for further in-depth research. The research results of this paper can be used for further structural modification and optimisation to guide the design and provide new ideas and methods for the discovery of new thrombin inhibitors.Communicated by Ramaswamy H. Sarma.