Virtual Screening Tool Research Articles

One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome

BackgroundMolecular fingerprints are essential cheminformatics tools for virtual screening and mapping chemical space. Among the different types of fingerprints, substructure fingerprints perform best for small molecules such as drugs, while atom-pair fingerprints are preferable for large molecules such as peptides. However, no available fingerprint achieves good performance on both classes of molecules.ResultsHere we set out to design a new fingerprint suitable for both small and large molecules by combining substructure and atom-pair concepts. Our quest resulted in a new fingerprint called MinHashed atom-pair fingerprint up to a diameter of four bonds (MAP4). In this fingerprint the circular substructures with radii of r = 1 and r = 2 bonds around each atom in an atom-pair are written as two pairs of SMILES, each pair being combined with the topological distance separating the two central atoms. These so-called atom-pair molecular shingles are hashed, and the resulting set of hashes is MinHashed to form the MAP4 fingerprint. MAP4 significantly outperforms all other fingerprints on an extended benchmark that combines the Riniker and Landrum small molecule benchmark with a peptide benchmark recovering BLAST analogs from either scrambled or point mutation analogs. MAP4 furthermore produces well-organized chemical space tree-maps (TMAPs) for databases as diverse as DrugBank, ChEMBL, SwissProt and the Human Metabolome Database (HMBD), and differentiates between all metabolites in HMBD, over 70% of which are indistinguishable from their nearest neighbor using substructure fingerprints.ConclusionMAP4 is a new molecular fingerprint suitable for drugs, biomolecules, and the metabolome and can be adopted as a universal fingerprint to describe and search chemical space. The source code is available at https://github.com/reymond-group/map4 and interactive MAP4 similarity search tools and TMAPs for various databases are accessible at http://map-search.gdb.tools/ and http://tm.gdb.tools/map4/.

Beware of the generic machine learning-based scoring functions in structure-based virtual screening.

Machine learning-based scoring functions (MLSFs) have attracted extensive attention recently and are expected to be potential rescoring tools for structure-based virtual screening (SBVS). However, a major concern nowadays is whether MLSFs trained for generic uses rather than a given target can consistently be applicable for VS. In this study, a systematic assessment was carried out to re-evaluate the effectiveness of 14 reported MLSFs in VS. Overall, most of these MLSFs could hardly achieve satisfactory results for any dataset, and they could even not outperform the baseline of classical SFs such as Glide SP. An exception was observed for RFscore-VS trained on the Directory of Useful Decoys-Enhanced dataset, which showed its superiority for most targets. However, in most cases, it clearly illustrated rather limited performance on the targets that were dissimilar to the proteins in the corresponding training sets. We also used the top three docking poses rather than the top one for rescoring and retrained the models with the updated versions of the training set, but only minor improvements were observed. Taken together, generic MLSFs may have poor generalization capabilities to be applicable for the real VS campaigns. Therefore, it should be quite cautious to use this type of methods for VS.

Standalone cooperation-free OKN-based low vision contrast sensitivity estimation in VR - a pilot study.

In low vision patients, the assessment of contrast sensitivity is an essential tool to determine the stage of visual impairment. However, traditional contrast sensitivity tests rely on verbal feedback, and the expertise of the examiner. In the current study, a fast, OKN-based virtual diagnosis tool was developed estimating contrast sensitivity automatically without active cooperation of the patient as well as the practitioner within 3.5 minutes. In a HTC Vive headset with an SMI-eye tracker, a virtual rotating drum was implemented, and an algorithm was developed, evaluating the occurrence of an OKN. The tool was evaluated in healthy subjects as well as under low vision simulation for two spatial frequencies and four contrasts. It was then compared to two contrast sensitivity estimates based on manual report on the orientation of static gratings as well as the movement direction of translating gratings. An algorithm was developed, which matched ground truth ratings of occurrence of OKN with an accuracy of 88 %. Furthermore, known differences in contrast sensitivity between healthy and low vision conditions as well as a decrease in contrast sensitivity for lower spatial frequencies was successfully reproduced in the developed tool. The developed OKN-based sensitivity test represents a reliable proof of concept for technology readiness of virtual reality-based screening tools of visual function in practice, specifically in patients with difficulties to report perception verbally, or under conditions, where no experienced examiner is present.

EasyVS: a user-friendly web-based tool for molecule library selection and structure-based virtual screening.

EasyVS is a web-based platform built to simplify molecule library selection and virtual screening. With an intuitive interface, the tool allows users to go from selecting a protein target with a known structure and tailoring a purchasable molecule library to performing and visualizing docking in a few clicks. Our system also allows users to filter screening libraries based on molecule properties, cluster molecules by similarity and personalize docking parameters. EasyVS is freely available as an easy-to-use web interface at http://biosig.unimelb.edu.au/easyvs. douglas.pires@unimelb.edu.au or david.ascher@unimelb.edu.au. Supplementary data are available at Bioinformatics online.

Β-SITOSTEROL: ISOLATION FROM MUNTINGIA CALABURA LINN BARK EXTRACT, STRUCTURAL ELUCIDATION AND MOLECULAR DOCKING STUDIES AS POTENTIAL INHIBITOR OF SARS-CoV-2 Mpro (COVID-19)

Objecive: A novel human coronavirus (HCoV), labelled as SARS-CoV-2 (COVID-19), causing pneumonia is spreading around the world. At present, there are no specific treatments for COVID-19. β-sitosterol is well known for its multiple biological actions. The aim of this research is to isolate and study binding affinity of β-sitosterol for SARS-CoV-2 (COVID-19) main protease (Mpro).
 Methods: Extraction and Column chromatography was performed to isolate the β-sitosterol from n-hexane extract of Muntingia calabura bark followed by thin layer chromatography (TLC), HPTLC, FTIR and UV-Visible Spectroscopy. The molecular docking studies were performed on SARS-CoV-2 Mpro to determine the binding affinity of the β-sitosterol by using PyRx Virtual Screening Tool.
 Results: In present study, preliminary phytochemical screening showed presence of carbohydrate, steroid, terpenoid and flavonoid compounds. Total 115 fractions were collected from column chromatography by using benzene as solvent by isocratic elution technique. HPTLC Fingerprinting analysis showed the presence of β-sitosterol under 366 nm. FTIR characterization was performed of the same fraction which clearly gives the absorption peaks which resembles to β-sitosterol structure.
 Conclusion: The overall study concludes this method can be considered as a standard method for isolation of β-sitosterol from Muntingia calabura bark. Favipiravir have less binding affinity i.e. -5.7 kcal/mol than β-sitosterol which has -6.9 kcal/mol. The no. of hydrogen bonds formed by the Favipiravir is much more i.e. 04 than β-sitosterol which formed only 01 hydrogen bond with SARS-CoV-2 Mpro.

Identification of new anti-nCoV drug chemical compounds from Indian spices exploiting SARS-CoV-2 main protease as target

The 2019-novel coronavirus (nCoV) has caused a global health crisis by causing coronavirus disease-19 (COVID-19) pandemic in the human population. The unavailability of specific vaccines and anti-viral drug for nCoV, science demands sincere efforts in the field of drug design and discovery for COVID-19. The novel coronavirus main protease (SARS-CoV-2 Mpro) play a crucial role during the disease propagation, and hence SARS-CoV-2 Mpro represents as a drug target for the drug discovery. Herein, we have applied bioinformatics approach for screening of chemical compounds from Indian spices as potent inhibitors of SARS-CoV-2 main protease (PDBID: 6Y84). The structure files of Indian spices chemical compounds were taken from PubChem database or Zinc database and screened by molecular docking, by using AutoDock-4.2, MGLTools-1.5.6, Raccoon virtual screening tools. Top 04 hits based on their highest binding affinity were analyzed. Carnosol exhibited highest binding affinity -8.2 Kcal/mol and strong and stable interactions with the amino acid residues present on the active site of SARS-CoV-2 Mpro. Arjunglucoside-I (-7.88 Kcal/mol) and Rosmanol (-7.99 Kcal/mol) also showed a strong and stable binding affinity with favourable ADME properties. These compounds on MD simulations for 50 ns shows strong hydrogen-bonding interactions with the protein active site and remains stable inside the active site. Our virtual screening results suggest that these small chemical molecules can be used as potential inhibitors against SARS-CoV-2 Mpro and may have an anti-viral effect on nCoV. However, further validation and investigation of these inhibitors against SARS-CoV-2 main protease are needed to claim their candidacy for clinical trials.Communicated by Ramaswamy H. Sarma

In silico screening and molecular docking of bioactive agents towards human coronavirus receptor

Coronavirus infection has turned into pandemic despite of efforts of efforts of countries like America, Italy, China, France etc. Currently India is also outraged by the virulent effect of coronavirus. Although World Health Organisation initially claimed to have all controls over the virus, till date infection has coasted several lives worldwide. Currently we do not have enough time for carrying out traditional approaches of drug discovery. Computer aided drug designing approaches are the best solution. The present study is completely dedicated to in silico approaches like virtual screening, molecular docking and molecular property calculation. The library of 15 bioactive molecules was built and virtual screening was carried towards the crystalline structure of human coronavirus(6nzk) which was downloaded from protein database. Pyrx virtual screening tool was used and results revealed that F14 showed best binding affinity. The best screened molecule was further allowed to dock with the target using Autodock vina software. The results of docking of F14 with target using autodock vina revealed the binding affinity of -10.6. The interaction study with discovery studio visualize revealed the molecular interaction with serine, histidine, asparagine, leucine, tyrosine, lysine, isoleucine, threonine. The molecular properties of F14 were also calculated. The study helped us understand that 3-amino-2-phenylquinazolin-4(3H)-one molecule can act as a potent parent structure for treating pandemic of coronavirus. This might be an essential tool for the medicinal chemist for designing novel molecule for treatment of coronavirus infection.

A Tool to Predict Affinity and Specificity of GAG Binding to Proteins

Glycosaminoglycans (GAGs) are unusual biopolymers that interact with a large number of proteins that reside intracellularly, extracellularly or on cell membranes. These include growth factors, chemokines/cytokines, serine proteases, serpins, metalloproteases, histone acyltransferases, integrins and other cell membrane receptors. Antithrombin is the only serpin believed to bind GAGs with high specificity. However, our computational algorithm is beginning to challenge this prejudicial assumption. In fact, our computational tool shows that GAG display selectivity from both – chemical as well as biological – perspectives. Alternatively, distinct group of GAG sequences selectively recognize a target protein from the horde of sequences (chemical specificity) and distinct binding sites on a protein uniquely recognize a group of GAG sequences (biological specificity). This understanding has come about from the computational tool we have developed to study GAG – protein interactions. The use of this novel combinatorial virtual library screening (CVLS) tool to predict affinity and specificity of GAG sequences for target proteins will be presented. In principle, the tool could be applied to any protein – GAG system of interest to identify the precise GAG sequence that offers high selectivity and high affinity.Support or Funding InformationU01 CA241951; K12 HL141954; R25 HL128639; R01 HL090586

Комплексный подход к разработке и созданию компьютерной программы виртуального скрининга антиоксидантной активности

Патогенез многих распространенных заболеваний человека (в том числе патологии сердечнососудистой системы, дыхательных путей, злокачественные новообразования) имеет достаточно четко очерченную свободнорадикальную фазу. Поэтому важной задачей современной фармакологии является поиск и создание высокоэффективных антиоксидантов. Для предварительной оценки антиоксидантной активности (АОА) разрабатываются информационные технологии и компьютерные программы виртуального скрининга. Поэтому разработка подходов к созданию компьютерных программ прогноза АОА, а именно скавенджеров NO, является перспективным и актуальным направлением современной фундаментальной медицины. Цель исследования – на основании комплексного математико-теоретического подхода и результатов фармакологических исследований в ряду производных ксантина разработать высокоселективную программу виртуального скрининга АОА. В качестве объекта исследования было отобрано 122 соединения, производные ксантинил7-ацетатных и ксантинил-8-тиоацетатных кислот и 3-R-гидроксиметилксантинов. Для этих соединений были проведены квантово-химические расчеты энергетических дескрипторов молекулярных орбиталей, а также проведено исследование антиоксидантной активности in vitro по ингибированию NO в системе фотоиндуцированного окисления нитропруссида натрия. Проведен расчет корреляционной зависимости между квантово-химическими параметрами соединений и АОА по ингибированию NO. Показано использование электронно-топологического подхода, который позволяет производить поиск молекулярных фрагментов-признаков активности. Были рассчитаны квантово-химические параметры исследуемых молекул с помощью метода CNDO/2. Рассчитаны показатели энергии предельных орбиталей (НВМО, ВЗМО), показатели абсолютной жесткости молекулы, абсолютной электрической негативности молекулы. На основании этих данных рассчитаны показатели реактивного индекса. Кроме того, рассчитана зависимость антиоксидантного действия (по ингибированию NO) веществ от их квантово-химических дескрипторов и определена функция их зависимости. Определена корреляционная зависимость АОА от квантово-химических показателей. Для создания программного обеспечения с целью скрининга потенциальных скавенджеров NO были выбраны квантово-химические дескрипторы. При этом, исходя из химической природы и реакционной способности активных форм азота,особое внимание следует уделить энергиям высшей занятой и низшей вакантной молекулярных орбиталей, а также индексу реактивности Фукуи. Разработанная на основе комплексного подхода (совокупности теоретических концепций, математических методов и правил, и реализующих их алгоритмов) новая компьютерная программа прогноза является высокоэффективным инструментом виртуального скрининга скавенджеров NO в ряду вновь синтезированных структурно-сходных азагетекроциклов, что доказано вычислительным тестированием и подтверждено экспериментальными исследованиями. Так, применение новой компьютерной программы для прогноза АОА позволило выявить у вновь синтезированного соединения – гидразида 8-бензиламинотеофиллинил-7-уксусной кислоты (С-3) свойства скавенджера NO, что подтверждено в опытах in vitro.

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

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

Advanced in Silico Methods for the Development of Anti- Leishmaniasis and Anti-Trypanosomiasis Agents.

Leishmaniasis and trypanosomiasis occur primarily in undeveloped countries and account for millions of deaths and disability-adjusted life years. Limited therapeutic options, high toxicity of chemotherapeutic drugs and the emergence of drug resistance associated with these diseases demand urgent development of novel therapeutic agents for the treatment of these dreadful diseases. In the last decades, different in silico methods have been successfully implemented for supporting the lengthy and expensive drug discovery process. In the current review, we discuss recent advances pertaining to in silico analyses towards lead identification, lead modification and target identification of antileishmaniasis and anti-trypanosomiasis agents. We describe recent applications of some important in silico approaches, such as 2D-QSAR, 3D-QSAR, pharmacophore mapping, molecular docking, and so forth, with the aim of understanding the utility of these techniques for the design of novel therapeutic anti-parasitic agents. This review focuses on: (a) advanced computational drug design options; (b) diverse methodologies - e.g.: use of machine learning tools, software solutions, and web-platforms; (c) recent applications and advances in the last five years; (d) experimental validations of in silico predictions; (e) virtual screening tools; and (f) rationale or justification for the selection of these in silico methods.

Virtual screening as a tool to discover new \u03b2-haematin inhibitors with activity against malaria parasites

Malaria remains a major public health problem. With the loss of antimalarials to resistance, the malaria burden will likely continue for decades. New antimalarial scaffolds are crucial to avoid cross-resistance. Here, we present the first structure based virtual screening using the β-haematin crystal as a target for new inhibitor scaffolds by applying a docking method. The ZINC15 database was searched for compounds with high binding affinity with the surface of the β-haematin crystal using the PyRx Virtual Screening Tool. Top-ranked compounds predicted to interact with β-haematin were submitted to a second screen applying in silico toxicity and drug-likeness predictions using Osiris DataWarrior. Fifteen compounds were purchased for experimental testing. An NP-40 mediated β-haematin inhibition assay and parasite growth inhibition activity assay were performed. The benzoxazole moiety was found to be a promising scaffold for further development, showing intraparasitic haemozoin inhibition using a cellular haem fractionation assay causing a decrease in haemozoin in a dose dependent manner with a corresponding increase in exchangeable haem. A β-haematin inhibition hit rate of 73% was found, a large enrichment over random screening, demonstrating that virtual screening can be a useful and cost-effective approach in the search for new haemozoin inhibiting antimalarials.

In Silico, Ex Vivo and In Vivo Studies of Roflumilast as a Potential Antidiarrheal and Antispasmodic agent: Inhibition of the PDE-4 Enzyme and Voltage-gated Ca++ ion Channels.

The aim of the present study was to evaluate the possible gut inhibitory role of the phosphodiesterase (PDE) inhibitor roflumilast. Increasing doses of roflumilast were tested against castor oil-induced diarrhea in mice, whereas the pharmacodynamics of the same effect was determined in isolated rabbit jejunum tissues. For in silico analysis, the identified PDE protein was docked with roflumilast and papaverine using the Autodock vina program from the PyRx virtual screening tool. Roflumilast protected against diarrhea significantly at 0.5 and 1.5 mg/kg doses, with 40% and 80% protection. Ex vivo findings from jejunum tissues show that roflumilast possesses an antispasmodic effect by inhibiting spontaneous contractions in a concentration-dependent manner. Roflumilast reversed carbachol (CCh, 1 µM)-mediated and potassium (K+, 80 mM)-mediated contractile responses with comparable efficacies but different potencies. The observed potency against K+ was significantly higher in comparison to CCh, similar to verapamil. Experiments were extended to further confirm the inhibitory effect on Ca++ channels. Interestingly, roflumilast deflected Ca++ concentration–response curves (CRCs) to the right with suppression of the maximum peak at both tested doses (0.001-0.003 mg/mL), similar to verapamil. The PDE-inhibitory effect was authenticated when pre-incubation of jejunum tissues with roflumilast (0.03-0.1 mg/mL) produced a leftward deflection of isoprenaline-mediated inhibitory CRCs and increased the tissue level of cAMP, similar to papaverine. This idea was further strengthened by molecular docking studies, where roflumilast exhibited a better binding affinity (-9.4 kcal/mol) with the PDE protein than the standard papaverine (-8.3 kcal/mol). In conclusion, inhibition of Ca++ channels and the PDE-4 enzyme explains the pharmacodynamics of the gut inhibitory effect of roflumilast.

Computing Poses of Ligands Bound to Proteins using MELD Accelerated Molecular Dynamics

Computing the poses of ligands bound to protein molecules is often done with DOCK, a widely used rapid virtual screening tool. In principle, more accurate predictions are possible by using a full atomistic forcefield, explicit solvent, and Replica Exchange Molecular Dynamics (REMD) to sample the conformational and solvation states. But it is computationally expensive. Here, we use the Bayesian MELD accelerator of REMD, by inputting DOCKs best 30 poses to speed up the sampling. Thus, we can make a comparative test of whether, and when, the full physics can improve upon an initial set of DOCK poses, to find the correct one. We find that in 20 ligand-protein pairs previously regarded as DOCK scoring failures, MELD rescues 15 of them. As a reference, of 10 additional pairs that were previous DOCK successes, MELD gets 6 of those right. MELD is able to sample efficiently different bound conformations and poses - we see occasional failures in selecting the right one which we attribute to force field and initial pose problems.

Functional analysis and screening small molecules to RpfF protein in Xanthomonas oryzae involved in rice bacterial blight disease

Xanthomonas oryzae pv. oryzae (Xoo) is an important rice pathogen. This is a vascular pathogen entering the plant via the hydathodes causing rice bacterial blight. It has been known that most regulation of pathogenicity factor F (RpfF) genes in Xanthomonas regulates virulence in response to the diffusible signal factor (DSF). The RpfF recognized as an attractive drug target in bacterial rice blight disease. In this study, we performed the gene-gene interaction of RpfF and pathway functional analysis. 3D structure of RpfF protein was predicted using a homology modelling tool Swiss-Model and refined by molecular dynamics (MD) simulation. The refined model protein was predicted structural assessment using various tools such as PROCHECK, ERRAT, and VERIFY-3D. We have collected 2 500 rifampicin analogues from Zinc Database by virtual screening. The screened compounds were docked into the active site of the RpfF protein using AutoDock Vina in PyRx Virtual Screening Tool. Furthermore, docking result and in silico ADMET analysis described that the compounds ZINC03056414, ZINC03205310, ZINC08673779, ZINC09100848, ZINC09729566, ZINC11415953, ZINC12810788, ZINC24989313, ZINC27441787 and ZINC32739565 have best binding energies and less toxicity than reference compound. This study revealed that the active site residues such as HIS-118, HIS-147, THR-148, ARG-179, ASP-207, ARG-240 and THR-244 are key roles in the pathogenicity. It could be beneficial in the design of small molecule therapeutics or the treatment of rice bacterial blight disease.

Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network.

Identifying drug-disease associations is helpful for not only predicting new drug indications and recognizing lead compounds, but also preventing, diagnosing, treating diseases. Traditional experimental methods are time consuming, laborious and expensive. Therefore, it is urgent to develop computational method for predicting potential drug-disease associations on a large scale. Herein, a novel method was proposed to identify drug-disease associations based on the deep learning technique. Molecular structure and clinical symptom information were used to characterize drugs and diseases. Then, a novel two-dimensional matrix was constructed and mapped to a gray-scale image for representing drug-disease association. Finally, deep convolution neural network was introduced to build model for identifying potential drug-disease associations. The performance of current method was evaluated based on the training set and test set, and accuracies of 89.90 and 86.51% were obtained. Prediction ability for recognizing new drug indications, lead compounds and true drug-disease associations was also investigated and verified by performing various experiments. Additionally, 3,620,516 potential drug-disease associations were identified and some of them were further validated through docking modeling. It is anticipated that the proposed method may be a powerful large scale virtual screening tool for drug research and development. The source code of MATLAB is freely available on request from the authors.

QSPR model for Caco-2 cell permeability prediction using a combination of HQPSO and dual-RBF neural network.

The Caco-2 cell model is widely used to evaluate the in vitro human intestinal permeability of drugs due to its morphological and functional similarity to human enterocytes. Although it is safe and relatively economic, it is time-consuming. A rapid and accurate quantitative structure-property relationship (QSPR) model of Caco-2 permeability is helpful to improve the efficiency of oral drug development. The aim of our study is to explore the predictive ability of the QSPR model, to study its permeation mechanism, and to develop a potential permeability prediction model, for Caco-2 cells. In our study, a relatively large data set was collected and the abnormal data were eliminated using the Monte Carlo regression and hybrid quantum particle swarm optimization (HQPSO) algorithm. Then, the remaining 1827 compounds were used to establish QSPR models. To generate multiple chemically diverse training and test sets, we used a combination of principal component analysis (PCA) and self-organizing mapping (SOM) neural networks to split the modeling data set characterized by PaDEL-descriptors. After preliminary selection of descriptors by the mean decrease impurity (MDI) method, the HQPSO algorithm was used to select the key descriptors. Six different methods, namely, multivariate linear regression (MLR), support vector machine regression (SVR), xgboost, radial basis function (RBF) neural networks, dual-SVR and dual-RBF were employed to develop QSPR models. The best dual-RBF model was obtained finally with R2 = 0.91, and Rcv52 = 0.77, for the training set, and RT2 = 0.77, for the test set. A series of validation methods were used to assess the robustness and predictive ability of the dual-RBF model under OECD principles. A new application domain (AD) definition method based on the descriptor importance-weighted and distance-based (IWD) method was proposed, and the outliers were analyzed carefully. Combined with the importance of the descriptors used in the dual-RBF model, we concluded that the “H E-state” and hydrogen bonds are important factors affecting the permeability of drugs passing through the Caco-2 cell. Compared with the reported studies, our method exhibits certain advantages in data size, transparency of modeling process and prediction accuracy to some extent, and is a promising tool for virtual screening in the early stage of drug development.

A microstructures generation tool for virtual ply property screening of hybrid composites with high volume fractions of non-circular fibers – VIPER

Within the framework of computational micromechanics (CMM), a simulation toolset is being developed to predict the mechanical behavior of fiber-reinforced polymers from the measured properties and spatial distribution of the different phases and interfaces in the composite. Towards this end, a numerical methodology is proposed herein for the generation of 2D periodic microstructures with arbitrary fiber geometries. A major advantage of the approach presented in this work is the ability of generating high volume fractions of non-circular fibers very efficiently. The underlying algorithm is based on the minimization of fiber overlapping by dynamic translation and rotation of the fibers until intersections are eliminated. The randomness of the microstructures obtained is assessed by means of multiple spatial descriptors.

Ligand based virtual screening using SVM on GPU

In silico methods play an essential role in modern drug discovery methods. Virtual screening, an in silico method, is used to filter out the chemical space on which actual wet lab experiments are need to be conducted. Ligand based virtual screening is a computational strategy using which one can build a model of the target protein based on the knowledge of the ligands that bind successfully to the target. This model is then used to predict if the new molecule is likely to bind to the target. Support vector machine, a supervised learning algorithm used for classification, can be utilized for virtual screening the ligand data. When used for virtual screening purpose, SVM could produce interesting results. But since we have a huge ligand data, the time taken for training the SVM model is quite high compared to other learning algorithms. By parallelizing these algorithms on multi-core processors, one can easily expedite these discoveries. In this paper, a GPU based ligand based virtual screening tool (GpuSVMScreen) which uses SVM have been proposed and bench-marked. This data parallel virtual screening tool provides high throughput by running in short time. The proposed GpuSVMScreen can successfully screen large number of molecules (billions) also. The source code of this tool is available at http://ccc.nitc.ac.in/project/GPUSVMSCREEN.

Synthesis and Molecular Docking Studies of Coumarinyl Thiazole as Cell Division Protein Kinase 2 Inhibitor

A series of 2-alkylamino-4-(3-coumarinyl)thiazoles were synthesized, characterized and evaluated their anticancer activity through molecular docking studies. Cell division protein kinase 2 (PDB code: 1KE9) is selected as a target and the compounds which obeys Lipinski rule of five is selected as a ligand. Molecular docking study is carried out using AutoDock Vina in PyRx virtual screening tool. This study revealed that all the compounds are active against the molecular target and compounds 5a and 5c have the highest docking score.