Computer-aided drug discovery and development

pyGROMODS: a Python package for the generation of input files for molecular dynamic simulation with GROMACS

The pyGROMODS, an easy-to-use cross-platform python-based package, with a graphical user interface, for the generation of molecular dynamic (MD) input files and running MD simulation (MDS) of proteins, peptides, and protein-ligand complex using GROMACS, is here presented. Four routes, with underlining Python scripts, are implemented in pyGROMODS for the generation of MD input files. They are 'RLmulti' for processing multi-ligand protein complex, 'RLmany' for processing multiple ligands against a single protein target, 'RLsingle' for processing multiple pairs of proteins and ligands, and 'PPmore' for processing peptides or proteins without ligands or non-standard residues. In addition, using the package, the generated input files or appropriate input files from other sources can be uploaded to run MDS with GROMACS. The pyGROMODS is implemented with a unique ability to search the host machine systems for the installation of the required software, update and/or install required Python packages, allow the user to pre-define working directory, and generate unique workflow organization with well-defined folders and files in a well-organized manner. The pyGROMODS, which is released under the MIT License, is freely available for download via the GitHub (https://github.com/Dankem/pyGROMODS) and Zenodo (https://doi.org/10.5281/zenodo.7912747) repositories. The precompiled executables can also be downloaded from Zenodo (https://doi.org/10.5281/zenodo.8087090), and a video tutorial can be downloaded from https://youtu.be/I4OKc6uVx1M.Communicated by Ramaswamy H. Sarma.

Inhibitors Targeting the F-BOX Proteins

F-box proteins are involved in multiple cellular processes through ubiquitylation and consequent degradation of targeted substrates. Any significant mutation in F-box protein-mediated proteolysis can cause human malformations. The various cellular processes F-box proteins involved include cell proliferation, apoptosis, invasion, angiogenesis, and metastasis. To target F-box proteins and their associated signaling pathways for cancer treatment, researchers have developed thousands of F-box inhibitors. The most advanced inhibitor of FBW7, NVD-BK M120, is a powerful P13 kinase inhibitor that has been proven to bring about apoptosis in cancerous human lung cells by disrupting levels of the protein known as MCL1. Moreover, F-box Inhibitors have demonstrated their efficacy for treating certain cancers through targeting particular mutated proteins. This paper explores the key studies on how F-box proteins act and their contribution to malignancy development, which fabricates an in-depth perception of inhibitors targeting the F-box proteins and their signaling pathways that eventually isolate the most promising approach to anti-cancer treatments.

Deep Learning with Geometry-Enhanced Molecular Representation for Augmentation of Large-Scale Docking-Based Virtual Screening

Structure-based virtual screening has been a crucial tool in drug discovery for decades. However, as the chemical space expands, the existing structure-based virtual screening techniques based on molecular docking and scoring struggle to handle billion-entry ultralarge libraries due to the high computational cost. To address this challenge, people have resorted to machine learning techniques to enhance structure-based virtual screening for efficiently exploring the vast chemical space. In those cases, compounds are usually treated as sequential strings or two-dimensional topology graphs, limiting their ability to incorporate three-dimensional structural information for downstream tasks. We herein propose a novel deep learning protocol, GEM-Screen, which utilizes the geometry-enhanced molecular representation of the compounds docking to a specific target and is trained on docking scores of a small fraction of a library through an active learning strategy to approximate the docking outcome for yet nontraining entries. This protocol is applied to virtual screening campaigns against the AmpC and D4 targets, demonstrating that GEM-Screen enriches more than 90% of the hit scaffolds for AmpC in the top 4% of model predictions and more than 80% of the hit scaffolds for D4 in the same top-ranking size of library. GEM-Screen can be used in conjunction with traditional docking programs for docking of only the top-ranked compounds to avoid the exhaustive docking of the whole library, thus allowing for discovering top-scoring compounds from billion-entry libraries in a rapid yet accurate fashion.

Atomistic simulations suggest dietary flavonoids from Beta vulgaris (beet) as promising inhibitors of human angiotensin-converting enzyme and 2-alpha-adrenergic receptors in hypertension

Motivation

Beta vulgaris (beet) is extensively reported for its antihypertensive activity. However, the mechanismunderpinning its antihypertensive activity is not well understood. In this study, we evaluated the in silico interactionsof 70 compounds derived from beta vulgaris against the active sites of angiotensin-converting enzyme (ACE) and alpha-adrenergic receptor (AR).

Results

Structure-based virtual screening against angiotensin-converting enzyme revealed that, Cochliophilin A (-9.0 Kcal/mol), Miraxanthin (-8.3 Kcal/mol), and quercimeritrin (-9.7 Kcal/mol) had lower docking scores than the reference lisinopril (-7.9 Kcal/mol). These compounds exhibited dual binding tendency as they also ranked top compounds upon screening against adrenergic receptor. The thermodynamic parameters computed from the resulting trajectories obtained from the 100 ns full atomistic molecular dynamics simulation revealed structural stability and conformational flexibility of the ligand-receptor complexes as indicated by the RMSD, RMSF, RoG, SASA, and H-bond calculations. The molecular mechanics with generalized Born and surface area solvation binding energy calculations revealed that the proteins exhibit considerable binding energy with the phytochemicals in a dynamic environment. Furthermore, the hit compounds possess good physicochemical properties and drug-likeness. Overall, cochliophilin and quercimeritrin are promising dual-target directed flavonoids from Beta vulgaris; and are suggested for further experimental and preclinical evaluation.

Availability and implementation

All data was provided in the manuscript.

Advances in receptor chromatography for drug discovery and drug-receptor interaction studies

Receptor chromatography involves high-throughput separation and accurate drug screening based on specific drug-receptor recognition and affinity, which has been widely used to screen active compounds in complex samples. This review summarizes the immobilization methods for receptors from three aspects: random covalent immobilization methods, site-specific covalent immobilization methods and dual-target receptor chromatography. Meanwhile, it focuses on its applications from three angles: screening active compounds in natural products, in natural-product-derived DNA-encoded compound libraries and drug-receptor interactions. This review provides new insights for the design and application of receptor chromatography, high-throughput and accurate drug screening, drug-receptor interactions and more. Teaser: This review summarizes the immobilization methods of receptors and the application of receptor chromatography, which will provide new insights for the design and application of receptor chromatography, rapid drug screening, drug-receptor interactions and more.

In Silico Study and Excito-Repellent Activity of Vitex negundo L. Essential Oil against Anopheles gambiae

(1) Background: Essential oil from Vitex negundo is known to have repellent and insecticidal properties toward the Anopheles gambiae and this is linked to its monoterpene and sesquiterpene content. In this work, an effort is made to delineate the constitution of V. negundo essential oil (VNEO) and their interaction with odorant-binding proteins (OBPs) of A. gambiae and hence access its repellent efficiency as cost-effective and safer malaria vector control alternatives. (2) Methods: Anopheles species authentication was performed by genomic DNA analysis and was subjected to behavioral analysis. GC-MS profiling was used to identify individual components of VNEO. Anopheles OBPs were obtained from the RCSB protein data bank and used for docking studies. Determination of ligand efficiency metrics and QSAR studies were performed using Hyper Chem Professional 8.0.3, and molecular dynamics simulations were performed using the Desmond module. (3) Results: GC-MS analysis of VNEO showed 28 compounds (monoterpenes, 80.16%; sesquiterpenes, 7.63%; and unknown constituents, 10.88%). The ligand efficiency metrics of all four ligands against the OBP 7 were within acceptable ranges. β-selinene (−12.2 kcal/mol), β-caryophellene (−9.5 kcal/mol), sulcatone (−10.9 kcal/mol), and α-ylangene (−9.3 kcal/mol) showed the strongest binding affinities for the target proteins. The most stable hydrophobic interactions were observed between β-selinene (Phe111 and Phe120), Sulcatone (Phe54 and Phe120), and α-ylangene (Phe111), while only sulcatone (Tyr49) presented H-bond interactions in the simulated environment. (4) Conclusions: Sulcatone and β-caryophyllene presented the best log p values, 6.45 and 5.20, respectively. These lead phytocompounds can be used in their purest as repellent supplement or as a natural anti-mosquito agent in product formulations.

Screening and identification of phytochemical drug molecules against mutant BRCA1 receptor of breast cancer using computational approaches

The American Cancer Society claims that breast cancer is the second most significant cause of cancer-related death, with over one million women diagnosed each year. Breast cancer linked to the BRCA1 gene has a significant risk of mortality and recurrence and is susceptible to alteration or over-expression, which can lead to hereditary breast cancer. Given the shortage of effective and possibly curative treatments for breast cancer, the present study combined molecular and computational analysis to find prospective phytochemical substances that can suppress the mutant gene (BRCA1) that causes the disease. Virtual screening and Molecular docking approaches are utilized to find probable phytochemicals from the ZINC database. The 3D structure of mutant BRCA1 protein with the id 3PXB was extracted from the NCBI-PDB. Top 10 phytochemical compounds shortlisted based on molecular docking score between - 11.6 and - 13.0. Following the ADMET properties, only three (ZINC000085490903 = - 12.50, ZINC000085490832 = - 12.44, and ZINC000070454071 = - 11.681) of the 10 selected compounds have drug-like properties. The molecular dynamic simulation study of the top three potential phytochemicals showed stabilized RMSD and RMSF values as compared to the APO form of the BRCA1 receptor. Further, trajectory analysis revealed that approximately similar radius of gyration score tends to the compactness of complex structure, and principal component and cross-correlation analysis suggest that the residues move in a strong correlation. Thermostability of the target complex (B-factor) provides information on the stable energy minimized structure. The findings suggest that the top three ligands show potential as breast cancer inhibitors.

Computational Approach to Combat COVID-19 Infection: Emerging Tool for Accelerating Drug Research

BACKGROUND

Drug discovery and development process is an expensive, complex, time-consuming and risky. There are different techniques involved in the drug development process which include random screening, computational approach, molecular manipulation and serendipitous research. Among these methods, the computational approach is considered as an efficient strategy to accelerate and economize the drug discovery process.

OBJECTIVE

This approach is mainly applied in various phases of drug discovery process including target identification, target validation, lead identification and lead optimization. Due to increase in the availability of information regarding various biological targets of different disease states, computational approaches such as molecular docking, de novo design, molecular similarity calculation, virtual screening, pharmacophore-based modeling and pharmacophore mapping have been applied extensively.

METHODS

Various drug molecules can be designed by applying computational tools to explore the drug candidates for treatment of Coronavirus infection. The world health organization has announced the novel corona virus disease as COVID-19 and declared it as pandemic globally on 11 February 2020. So, it is thought of interest to scientific community to apply computational methods to design and optimize the pharmacological properties of various clinically available and FDA approved drugs such as remdesivir, ribavirin, favipiravir, oseltamivir, ritonavir, arbidol, chloroquine, hydroxychloroquine, carfilzomib, baraticinib, prulifloxacin, etc for effective treatment of COVID-19 infection.

RESULTS

Further, various survey reports suggest that the extensive studies are carried out by various research communities to find out the safety and efficacy profile of these drug candidates.

CONCLUSION

This review is focused on the study of various aspects of these drugs related to their target sites on virus, binding interactions, physicochemical properties etc.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

In silico identification and study of potential anti-mosquito juvenile hormone binding protein (MJHBP) compounds as candidates for dengue virus - Vector insecticides

Dengue has become a huge global health burden. It is currently recognized as the most rapidly spreading mosquito-borne viral disease. Yet, there are currently no licensed vaccines or specific therapeutics to manage the virus, thus, scaling up vector control approaches is important in controlling this viral spread. This study aimed to identify and study in silico, potential anti-mosquito compounds targeting Juvenile hormone (JH) mediated pathways via the Mosquito Juvenile Hormone Binding Protein (MJHBP). The study was implemented using series of computational methods. The query compounds included pyrethroids and those derived from ZINC and ANPDB databases using a simple pharmacophore model in Molecular Operating Environment (MOE). Molecular docking of selected compounds' library was implemented in MOE. The resultant high-score compounds were further validated by molecular dynamics simulation via Maestro 12.3 module and the respective Prime/Molecular Mechanics Generalized Born Surface Area (Prime/MM-GBSA) binding energies computed. The study identified compounds-pyrethroids, natural and synthetic - with high docking energy scores (ranging from 10.91-12.34 kcal/mol). On further analysis of the high-ranking (in terms of docking scores) compounds using MD simulation, the compounds - Ekeberin D4, Maesanin, Silafluofen and ZINC16919139- revealed very low binding energies (-122.99, -72.91 -104.50 and,-74.94 kcal/mol respectively), fairly stable complex and interesting interaction with JH-binding site amino acid residues on MJHBP. Further studies can explore these compounds in vitro/in vivo in the search for more efficient mosquito vector control.

Artificial Intelligence and the Future of Diagnostic and Therapeutic Radiopharmaceutical Development:: In Silico Smart Molecular Design

Novel diagnostic and therapeutic radiopharmaceuticals are increasingly becoming a central part of personalized medicine. Continued innovation in the development of new radiopharmaceuticals is key to sustained growth and advancement of precision medicine. Artificial intelligence has been used in multiple fields of medicine to develop and validate better tools for patient diagnosis and therapy, including in radiopharmaceutical design. In this review, we first discuss common in silico approaches and focus on their usefulness and challenges in radiopharmaceutical development. Next, we discuss the practical applications of in silico modeling in design of radiopharmaceuticals in various diseases.

Heat Shock Proteins as Targets for Novel Antimalarial Drug Discovery

Plasmodium falciparum, the parasitic agent that is responsible for a severe and dangerous form of human malaria, has a history of long years of cohabitation with human beings with attendant negative consequences. While there have been some gains in the fight against malaria through the application of various control measures and the use of chemotherapeutic agents, and despite the global decline in malaria cases and associated deaths, the continual search for new and effective therapeutic agents is key to achieving sustainable development goals. An important parasite survival strategy, which is also of serious concern to the scientific community, is the rate at which the parasites continually develop resistance to drugs. Among the key players in the parasite's ability to develop resistance, maintain cellular integrity, and survives within an unusual environment of the red blood cells are the molecular chaperones of the heat shock proteins (HSP) family. HSPs constitute a novel avenue for antimalarial drug discovery and by exploring their ubiquitous nature and multifunctional activities, they may be suitable targets for the discovery of multi-targets antimalarial drugs, needed to fight incessant drug resistance. In this chapter, features of selected families of plasmodial HSPs that can be exploited in drug discovery are presented. Also, known applications of HSPs in small molecule screening, their potential usefulness in high throughput drug screening, as well as possible challenges are highlighted.

Emerging Need of Today: Significant Utilization of Various Databases and Softwares in Drug Design and Development

In drug discovery, in silico methods have become a very important part of the process. These approaches impact the entire development process by discovering and identifying new target proteins as well as designing potential ligands with a significant reduction of time and cost. Furthermore, in silico approaches are also preferred because of reduction in the experimental use of animals as; in vivo testing for safer drug design and repositioning of known drugs. Novel software-based discovery and development such as direct/indirect drug design, molecular modelling, docking, screening, drug-receptor interaction, and molecular simulation studies are very important tools for the predictions of ligand-target interaction pattern, pharmacodynamics as well as pharmacokinetic properties of ligands. On the other part, the computational approaches can be numerous, requiring interdisciplinary studies and the application of advanced computer technology to design effective and commercially feasible drugs. This review mainly focuses on the various databases and software used in drug design and development to speed up the process.

Field-Template, QSAR, Ensemble Molecular Docking, and 3D-RISM Solvation Studies Expose Potential of FDA-Approved Marine Drugs as SARS-CoVID-2 Main Protease Inhibitors

Currently, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has infected people among all countries and is a pandemic as declared by the World Health Organization (WHO). SARS-CoVID-2 main protease is one of the therapeutic drug targets that has been shown to reduce virus replication, and its high-resolution 3D structures in complex with inhibitors have been solved. Previously, we had demonstrated the potential of natural compounds such as serine protease inhibitors eventually leading us to hypothesize that FDA-approved marine drugs have the potential to inhibit the biological activity of SARS-CoV-2 main protease. Initially, field-template and structure–activity atlas models were constructed to understand and explain the molecular features responsible for SARS-CoVID-2 main protease inhibitors, which revealed that Eribulin Mesylate, Plitidepsin, and Trabectedin possess similar characteristics related to SARS-CoVID-2 main protease inhibitors. Later, protein–ligand interactions are studied using ensemble molecular-docking simulations that revealed that marine drugs bind at the active site of the main protease. The three-dimensional reference interaction site model (3D-RISM) studies show that marine drugs displace water molecules at the active site, and interactions observed are favorable. These computational studies eventually paved an interest in further in vitro studies. Finally, these findings are new and indeed provide insights into the role of FDA-approved marine drugs, which are already in clinical use for cancer treatment as a potential alternative to prevent and treat infected people with SARS-CoV-2.

A Toxicity Prediction Tool for Potential Agonist/Antagonist Activities in Molecular Initiating Events Based on Chemical Structures

Because the health effects of many compounds are unknown, regulatory toxicology must often rely on the development of quantitative structure-activity relationship (QSAR) models to efficiently discover molecular initiating events (MIEs) in the adverse-outcome pathway (AOP) framework. However, the QSAR models used in numerous toxicity prediction studies are publicly unavailable, and thus, they are challenging to use in practical applications. Approaches that simultaneously identify the various toxic responses induced by a compound are also scarce. The present study develops Toxicity Predictor, a web application tool that comprehensively identifies potential MIEs. Using various chemicals in the Toxicology in the 21st Century (Tox21) 10K library, we identified potential endocrine-disrupting chemicals (EDCs) using a machine-learning approach. Based on the optimized three-dimensional (3D) molecular structures and XGBoost algorithm, we established molecular descriptors for QSAR models. Their predictive performances and applicability domain were evaluated and applied to Toxicity Predictor. The prediction performance of the constructed models matched that of the top model in the Tox21 Data Challenge 2014. These advanced prediction results for MIEs are freely available on the Internet.

Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening

The versatility of similarity searching and quantitative structure-activity relationships to model the activity of compound sets within given bioactivity ranges (i.e., interpolation) is well established. However, their relative performance in the common scenario in early stage drug discovery where lots of inactive data but no active data points are available (i.e., extrapolation from the low-activity to the high-activity range) has not been thoroughly examined yet. To this aim, we have designed an iterative virtual screening strategy which was evaluated on 25 diverse bioactivity data sets from ChEMBL. We benchmark the efficiency of random forest (RF), multiple linear regression, ridge regression, similarity searching, and random selection of compounds to identify a highly active molecule in the test set among a large number of low-potency compounds. We use the number of iterations required to find this active molecule to evaluate the performance of each experimental setup. We show that linear and ridge regression often outperform RF and similarity searching, reducing the number of iterations to find an active compound by a factor of 2 or more. Even simple regression methods seem better able to extrapolate to high-bioactivity ranges than RF, which only provides output values in the range covered by the training set. In addition, examination of the scaffold diversity in the data sets used shows that in some cases similarity searching and RF require two times as many iterations as random selection depending on the chemical space covered in the initial training data. Lastly, we show using bioactivity data for COX-1 and COX-2 that our framework can be extended to multitarget drug discovery, where compounds are selected by concomitantly considering their activity against multiple targets. Overall, this study provides an approach for iterative screening where only inactive data are present in early stages of drug discovery in order to discover highly potent compounds and the best experimental set up in which to do so.

In silico cancer research towards 3R

BACKGROUND

Improving our understanding of cancer and other complex diseases requires integrating diverse data sets and algorithms. Intertwining in vivo and in vitro data and in silico models are paramount to overcome intrinsic difficulties given by data complexity. Importantly, this approach also helps to uncover underlying molecular mechanisms. Over the years, research has introduced multiple biochemical and computational methods to study the disease, many of which require animal experiments. However, modeling systems and the comparison of cellular processes in both eukaryotes and prokaryotes help to understand specific aspects of uncontrolled cell growth, eventually leading to improved planning of future experiments. According to the principles for humane techniques milestones in alternative animal testing involve in vitro methods such as cell-based models and microfluidic chips, as well as clinical tests of microdosing and imaging. Up-to-date, the range of alternative methods has expanded towards computational approaches, based on the use of information from past in vitro and in vivo experiments. In fact, in silico techniques are often underrated but can be vital to understanding fundamental processes in cancer. They can rival accuracy of biological assays, and they can provide essential focus and direction to reduce experimental cost.

MAIN BODY

We give an overview on in vivo, in vitro and in silico methods used in cancer research. Common models as cell-lines, xenografts, or genetically modified rodents reflect relevant pathological processes to a different degree, but can not replicate the full spectrum of human disease. There is an increasing importance of computational biology, advancing from the task of assisting biological analysis with network biology approaches as the basis for understanding a cell's functional organization up to model building for predictive systems.

CONCLUSION

Underlining and extending the in silico approach with respect to the 3Rs for replacement, reduction and refinement will lead cancer research towards efficient and effective precision medicine. Therefore, we suggest refined translational models and testing methods based on integrative analyses and the incorporation of computational biology within cancer research.

The advancement of multidimensional QSAR for novel drug discovery - where are we headed?

INTRODUCTION

The Multidimensional quantitative structure-activity relationship (multidimensional-QSAR) method is one of the most popular computational methods employed to predict interesting biochemical properties of existing or hypothetical molecules. With continuous progress, the QSAR method has made remarkable success in various fields, such as medicinal chemistry, material science and predictive toxicology. Areas covered: In this review, the authors cover the basic elements of multidimensional -QSAR including model construction, validation and application. It includes and emphasizes the very recent developments of multidimensional -QSAR such as: HQSAR, G-QSAR, MIA-QSAR, multi-target QSAR. The advantages and disadvantages of each method are also discussed and typical examples of their application are detailed. Expert opinion: Although there are defects in multidimensional-QSAR modeling, it is still of enormous help to chemists, biologists and other researchers in various fields. In the authors' opinion, the latest more precise and feasible QSAR models should be further developed by integrating new descriptors, algorithms and other relevant computational techniques. Apart from being applied in traditional fields (e.g. lead optimization and predictive risk assessment), QSAR should be used more widely as a routine method in other emerging research fields including the modeling of nanoparticles(NPs), mixture toxicity and peptides.

Identification of molecular descriptors for design of novel Isoalloxazine derivatives as potential Acetylcholinesterase inhibitors against Alzheimer's disease

In Alzheimer's disease (AD), the level of Acetylcholine (ACh) neurotransmitter is reduced. Since Acetylcholinesterase (AChE) cleaves ACh, inhibitors of AChE are very much sought after for AD treatment. The side effects of current inhibitors necessitate development of newer AChE inhibitors. Isoalloxazine derivatives have proved to be promising (AChE) inhibitors. However, their structure-activity relationship studies have not been reported till date. In the present work, various quantitative structure-activity relationship (QSAR) building methods such as multiple linear regression (MLR), partial least squares ,and principal component regression were employed to derive 3D-QSAR models using steric and electrostatic field descriptors. Statistically significant model was obtained using MLR coupled with stepwise selection method having r² = .9405, cross validated r² (q²) = .6683, and a high predictability (pred_r² = .6206 and standard error, pred_r²se = .2491). Steric and electrostatic contribution plot revealed three electrostatic fields E_496, E_386 and E_577 and one steric field S_60 contributing towards biological activity. A ligand-based 3D-pharmacophore model was generated consisting of eight pharmacophore features. Isoalloxazine derivatives were docked against human AChE, which revealed critical residues implicated in hydrogen bonds as well as hydrophobic interactions. The binding modes of docked complexes (AChE_IA1 and AChE_IA14) were validated by molecular dynamics simulation which showed their stable trajectories in terms of root mean square deviation and molecular mechanics/Poisson-Boltzmann surface area binding free energy analysis revealed key residues contributing significantly to overall binding energy. The present study may be useful in the design of more potent Isoalloxazine derivatives as AChE inhibitors.

The oxadiazole antibacterials

The oxadiazoles are a class of antibacterials discovered by in silico docking and scoring of compounds against the X-ray structure of a penicillin-binding protein. These antibacterials exhibit activity against Gram-positive bacteria, including against methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE). They show in vivo efficacy in murine models of peritonitis/sepsis and neutropenic thigh MRSA infection. They are bactericidal and orally bioavailable. The oxadiazoles show promise in treatment of MRSA infection.

A quantitative structure-activity relationship (QSAR) study of some diaryl urea derivatives of B-RAF inhibitors

In the current study, both ligand-based molecular docking and receptor-based quantitative structure activity relationships (QSAR) modeling were performed on 35 diaryl urea derivative inhibitors of ^V600EB-RAF. In this QSAR study, a linear (multiple linear regressions) and a nonlinear (partial least squares least squares support vector machine (PLS-LS-SVM)) were used and compared. The predictive quality of the QSAR models was tested for an external set of 31 compounds, randomly chosen out of 35 compounds. The results revealed the more predictive ability of PLS-LS-SVM in analysis of compounds with urea structure. The selected descriptors indicated that size, degree of branching, aromaticity, and polarizability affected the inhibition activity of these inhibitors. Furthermore, molecular docking was carried out to study the binding mode of the compounds. Docking analysis indicated some essential H-bonding and orientations of the molecules in the active site.

A potential target of Tanshinone IIA for acute promyelocytic leukemia revealed by inverse docking and drug repurposing

Tanshinone IIA is a pharmacologically active ingredient extracted from Danshen, a Chinese traditional medicine. Its molecular mechanisms are still unclear. The present study utilized computational approaches to uncover the potential targets of this compound. In this research, PharmMapper server was used as the inverse docking tool and the results were verified by Autodock vina in PyRx 0.8, and by DRAR-CPI, a server for drug repositioning via the chemical-protein interactome. Results showed that the retinoic acid receptor alpha (RARα), a target protein in acute promyelocytic leukemia (APL), was in the top rank, with a pharmacophore model matching well the molecular features of Tanshinone IIA. Moreover, molecular docking and drug repurposing results showed that the complex was also matched in terms of structure and chemical-protein interactions. These results indicated that RARα may be a potential target of Tanshinone IIA for APL. The study can provide useful information for further biological and biochemical research on natural compounds.

Insight into the structural features of pyrazolopyrimidine- and pyrazolopyridine-based B-Raf(V600E) kinase inhibitors by computational explorations

Presently, both ligand-based and receptor-based 3D-QSAR modelings were performed on 107 pyrazolopyrimidine- and pyrazolopyridine-based inhibitors of B-Raf(V600E) kinase. The optimal model is successful to predict the inhibitors' activity with Q(2) of 0.504, R(2) ncv of 0.960, and R(2) pred of 0.872. Besides, the 3D contour maps explain well the structural requirements of the interaction between the ligand and the receptor. Furthermore, molecular docking and MD were also carried out to study the binding mode. Our findings are the following: (i) Bulky substituents at position 3, 10 and ring D improve the inhibitory activity, but impair the activity at position 5, 11, and 19. (ii) Electropositive groups at position 10, 13 and 20 and electronegative groups at position 2 increase the biological activity. (iii) Hydrophobic substituents at ring C are beneficial to improve the biological activity, while hydrophilic substituents at position 11 and ring D are good for the activity. (4) This scaffold of inhibitors may bind to the B-Raf kinase with an 'L' conformation and belong to type III binding mode, which is fixed by hydrophobic interaction and hydrogen bonds with residues from hinge region and DFG motif. These results may be a guidance to develop new B-Raf(V600E) kinase inhibitors.

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Pharmacological inactivation of Skp2 SCF ubiquitin ligase restricts cancer stem cell traits and cancer progression

Skp2 E3 ligase is overexpressed in numerous human cancers and plays a critical role in cell-cycle progression, senescence, metabolism, cancer progression, and metastasis. In the present study, we identified a specific Skp2 inhibitor using high-throughput in silico screening of large and diverse chemical libraries. This Skp2 inhibitor selectively suppresses Skp2 E3 ligase activity, but not activity of other SCF complexes. It also phenocopies the effects observed upon genetic Skp2 deficiency, such as suppressing survival and Akt-mediated glycolysis and triggering p53-independent cellular senescence. Strikingly, we discovered a critical function of Skp2 in positively regulating cancer stem cell populations and self-renewal ability through genetic and pharmacological approaches. Notably, Skp2 inhibitor exhibits potent antitumor activities in multiple animal models and cooperates with chemotherapeutic agents to reduce cancer cell survival. Our study thus provides pharmacological evidence that Skp2 is a promising target for restricting cancer stem cell and cancer progression.

Patient-specific modelling in drug design, development and selection including its role in clinical decision-making

Genomics has made enormous progress in the twelve years since the publication of the first draft human genome sequence, but it has not yet been translated into the clinic. Despite spiralling development costs, the number of new drug registrations is not increasing. One reason for this lies in the genetic complexity of disease. Most diseases involve dysregulation in pathways that involve many genes, and many (including most cancers) are themselves genetically heterogeneous. Systems biology involves the multi-level simulation of physiology, cell biology and biochemistry using complex computational techniques. We show here using case studies in cancer and HIV how such computational models, and particularly models based on individual patient data, can be used for drug design and development, and in the selection of the appropriate treatment for a given patient in the face of resistance mutations. If these techniques are to be adopted in routine clinical practice, clinicians will need better training in modern approaches to the integrated analysis of large-scale heterogeneous data and multi-scale models, while developers will need to provide much more usable tools. Investment in computational infrastructure is needed so that results can be returned on clinically relevant timescales and data warehouses designed with data protection as well as accessibility in mind.

Computational drug discovery

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field.