@article { ISI:000360934400003, title = {ChemScreener: a distributed computing tool for scaffold based virtual screening}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {18}, number = {6}, year = {2015}, month = {JAN}, pages = {544-561}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

In this work we present ChemScreener, a Java-based application to perform virtual library generation combined with virtual screening in a platform-independent distributed computing environment. ChemScreener comprises a scaffold identifier, a distinct scaffold extractor, an interactive virtual library generator as well as a virtual screening module for subsequently selecting putative bioactive molecules. The virtual libraries are annotated with chemophore-, pharmacophore- and toxicophore-based information for compound prioritization. The hits selected can then be further processed using QSAR, docking and other in silico approaches which can all be interfaced within the ChemScreener framework. As a sample application, in this work scaffold selectivity, diversity, connectivity and promiscuity towards six important therapeutic classes have been studied. In order to illustrate the computational power of the application, 55 scaffolds extracted from 161 anti-psychotic compounds were enumerated to produce a virtual library comprising 118 million compounds (17 GB) and annotated with chemophore, pharmacophore and toxicophore based features in a single step which would be non-trivial to perform with many standard software tools today on libraries of this size.

}, keywords = {Scaffold extraction, therapeutic category, virtual library generation, virtual screening}, issn = {1386-2073}, doi = {10.2174/1386207318666150703112242}, author = {Karthikeyan, Muthukumarasamy and Pandit, Deepak and Vyas, Renu} } @article { ISI:000360934400007, title = {Design and development of cheminfocloud: an integrated cloud enabled platform for virtual screening}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {18}, number = {6}, year = {2015}, month = {JAN}, pages = {604-619}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

The power of cloud computing and distributed computing has been harnessed to handle vast and heterogeneous data required to be processed in any virtual screening protocol. A cloud computing platorm ChemInfoCloud was built and integrated with several chemoinformatics and bioinformatics tools. The robust engine performs the core chemoinformatics tasks of lead generation, lead optimisation and property prediction in a fast and efficient manner. It has also been provided with some of the bioinformatics functionalities including sequence alignment, active site pose prediction and protein ligand docking. Text mining, NMR chemical shift (1H, 13C) prediction and reaction fingerprint generation modules for efficient lead discovery are also implemented in this platform. We have developed an integrated problem solving cloud environment for virtual screening studies that also provides workflow management, better usability and interaction with end users using container based virtualization, OpenVz.

}, keywords = {Chemoinformatics, cloud computing, machine learning, Molecular docking, OpenVz, sequence alignment, spectra prediction, text mining, virtual screening}, issn = {1386-2073}, doi = {10.2174/1386207318666150703113656}, author = {Karthikeyan, Muthukumarasamy and Pandit, Deepak and Bhavasar, Arvind and Vyas, Renu} } @article { ISI:000360934400006, title = {MegaMiner: a tool for lead identification through text mining using chemoinformatics tools and cloud computing environment}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {18}, number = {6}, year = {2015}, month = {JAN}, pages = {591-603}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

Virtual screening is an indispensable tool to cope with the massive amount of data being tossed by the high throughput omics technologies. With the objective of enhancing the automation capability of virtual screening process a robust portal termed MegaMiner has been built using the cloud computing platform wherein the user submits a text query and directly accesses the proposed lead molecules along with their drug-like, lead-like and docking scores. Textual chemical structural data representation is fraught with ambiguity in the absence of a global identifier. We have used a combination of statistical models, chemical dictionary and regular expression for building a disease specific dictionary. To demonstrate the effectiveness of this approach, a case study on malaria has been carried out in the present work. MegaMiner offered superior results compared to other text mining search engines, as established by F score analysis. A single query term {\textquoteleft}malaria{\textquoteright} in the portlet led to retrieval of related PubMed records, protein classes, drug classes and 8000 scaffolds which were internally processed and filtered to suggest new molecules as potential anti-malarials. The results obtained were validated by docking the virtual molecules into relevant protein targets. It is hoped that MegaMiner will serve as an indispensable tool for not only identifying hidden relationships between various biological and chemical entities but also for building better corpus and ontologies.

}, keywords = {Chemoinformatics, cloud computing, malaria, text mining, virtual screening}, issn = {1386-2073}, doi = {10.2174/1386207318666150703113525}, author = {Karthikeyan, Muthukumarasamy and Pandit, Yogesh and Pandit, Deepak and Vyas, Renu} } @article { ISI:000360934400004, title = {Prediction of bioactive compounds using computed NMR chemical shifts}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {18}, number = {6}, year = {2015}, month = {JAN}, pages = {562-576}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

NMR based chemical shifts are an important diagnostic parameter for structure elucidation as they capture rich information related to conformational, electronic and stereochemical arrangement of functional groups in a molecule which is responsible for its activity towards any biological target. The present work discusses the importance of computing NMR chemical shifts from molecular structures. The NMR chemical shift data (experimental or computed) was used to generate fingerprints in binary formats for mapping molecular fragments (as descriptors) and correlating with the bioactivity classes. For this study, chemical shift data derived binary fingerprints were computed for 149 classes and 4800 bioactive molecules. The sensitivity and selectivity of fingerprints in discriminating molecules belonging to different therapeutic categories was assessed using a LibSVM based classifier. An accuracy of 82\% for proton and 94\% for carbon NMR fingerprints were obtained for anti-psoriatic and anti-psychotic molecules demonstrating the effectiveness of this approach for virtual screening.

}, keywords = {Chemical shift, fingerprints, NMR, similarity searching, virtual screening}, issn = {1386-2073}, doi = {10.2174/1386207318666150703113312}, author = {Karthikeyan, Muthukumarasamy and Rajamohanan, Pattuparambil Ramanpillai and Vyas, Renu} } @article { ISI:000360934400005, title = {Protein ligand complex guided approach for virtual screening}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {18}, number = {6}, year = {2015}, month = {JAN}, pages = {577-590}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

The target ligand association data is a rich source of information which is not exploited enough for drug design efforts in virtual screening. A java based open-source toolkit for Protein Ligand Network Extraction (J-ProLiNE) focused on protein-ligand complex analysis with several features integrated in a distributed computing network has been developed. Sequence alignment and similarity search components have been automated to yield local, global alignment scores along with similarity and distance scores. 10000 proteins with co-crystallized ligands from pdb and MOAD databases were extracted and analyzed for revealing relationships between targets, ligands and scaffolds. Through this analysis, we could generate a protein ligand network to identify the promiscuous and selective scaffolds for multiple classes of proteins targets. Using J-ProLiNE we created a 507 x 507 matrix of protein targets and native ligands belonging to six enzyme classes and analyzed the results to elucidate the protein-protein, protein-ligand and ligand-ligand interactions. In yet another application of the J-ProLiNE software, we were able to process kinase related information stored in US patents to construct disease-gene-ligand-scaffold networks. It is hoped that the studies presented here will enable target ligand knowledge based virtual screening for inhibitor design.

}, keywords = {Complexes, ligand, protein, scaffolds, sequences, similarity score, virtual screening}, issn = {1386-2073}, doi = {10.2174/1386207318666150703112620}, author = {Karthikeyan, Muthukumarasamy and Pandit, Deepak and Vyas, Renu} } @article { ISI:000360934800003, title = {Role of chemical reactivity and transition state modeling for virtual screening}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {18}, number = {7}, year = {2015}, month = {JAN}, pages = {638-657}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

Every drug discovery research program involves synthesis of a novel and potential drug molecule utilizing atom efficient, economical and environment friendly synthetic strategies. The current work focuses on the role of the reactivity based fingerprints of compounds as filters for virtual screening using a tool ChemScore. A reactant-like (RLS) and a product-like (PLS) score can be predicted for a given compound using the binary fingerprints derived from the numerous known organic reactions which capture the molecule-molecule interactions in the form of addition, substitution, rearrangement, elimination and isomerization reactions. The reaction fingerprints were applied to large databases in biology and chemistry, namely ChEMBL, KEGG, HMDB, DSSTox, and the Drug Bank database. A large network of 1113 synthetic reactions was constructed to visualize and ascertain the reactant product mappings in the chemical reaction space. The cumulative reaction fingerprints were computed for 4000 molecules belonging to 29 therapeutic classes of compounds, and these were found capable of discriminating between the cognition disorder related and anti-allergy compounds with reasonable accuracy of 75\% and AUC 0.8. In this study, the transition state based fingerprints were also developed and used effectively for virtual screening in drug related databases. The methodology presented here provides an efficient handle for the rapid scoring of molecular libraries for virtual screening.

}, keywords = {fingerprints, intermediates, metabolic pathways, product-like score, reactant-like score, reaction, screening, synthesis, virtual screening}, issn = {1386-2073}, doi = {10.2174/1386207318666150703113135}, author = {Karthikeyan, Muthukumarasamy and Vyas, Renu and Tambe, Sanjeev S. and Radhamohan, Deepthi and Kulkarni, Bhaskar D.} } @article { ISI:000360934400002, title = {Role of open source tools and resources in virtual screening for drug discovery}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {18}, number = {6}, year = {2015}, month = {JUL}, pages = {528-543}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

Advancement in chemoinformatics research in parallel with availability of high performance computing platform has made handling of large scale multi-dimensional scientific data for high throughput drug discovery easier. In this study we have explored publicly available molecular databases with the help of open-source based integrated in-house molecular informatics tools for virtual screening. The virtual screening literature for past decade has been extensively investigated and thoroughly analyzed to reveal interesting patterns with respect to the drug, target, scaffold and disease space. The review also focuses on the integrated chemoinformatics tools that are capable of harvesting chemical data from textual literature information and transform them into truly computable chemical structures, identification of unique fragments and scaffolds from a class of compounds, automatic generation of focused virtual libraries, computation of molecular descriptors for structure-activity relationship studies, application of conventional filters used in lead discovery along with in-house developed exhaustive PTC (Pharmacophore, Toxicophores and Chemophores) filters and machine learning tools for the design of potential disease specific inhibitors. A case study on kinase inhibitors is provided as an example.

}, keywords = {Chemoinformatics, kinases, open source, scaffold, virtual screening}, issn = {1386-2073}, doi = {10.2174/1386207318666150703111911}, author = {Karthikeyan, Muthukumarasamy and Vyas, Renu} } @article { ISI:000380565100006, title = {Virtual screening techniques to probe the antimalarial activity of some traditionally used phytochemicals}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {19}, number = {7}, year = {2016}, month = {JUL}, pages = {572-591}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

Malaria parasites show resistance to most of the antimalarial drugs and hence developing antimalarials which can act on multitargets rather than a single target will be a promising strategy of drug design. Here we report a new approach by which virtual screening of 292 unique phytochemicals present in 72 traditionally important herbs is used for finding out inhibitors of plasmepsin-2 and falcipain-2 for antimalarial activity against P. falciparum. Initial screenings of the selected molecules by Random Forest algorithm model of Weka using the bioassay datasets AID 504850 and AID 2302 screened 120 out of the total 292 phytochemicals to be active against the targets. Toxtree scan cautioned 21 compounds to be either carcinogenic or mutagenic and were thus removed for further analysis. Out of the remaining 99 compounds, only 46 compounds offered drug-likeness as per the {\textquoteleft}rule of five{\textquoteright} criteria. Out of ten antimalarial drug targets, only two target proteins such as 3BPF and 3PNR of falcipain-2 and 1PFZ and 2BJU of plasmepsin-2 are selected as targets. The potential binding of the selected 46 compounds to the active sites of these four targets was analyzed using MOE software. The docked conformations and the interactions with the binding pocket residues of the target proteins were understood by {\textquoteleft}Ligplot{\textquoteright} analysis. It has been found that 8 compounds are dual inhibitors of falcipain-2 and plasmepsin-2, with the best binding energies. Compound 117 (6aR, 12aS)-12a-Hydroxy-9-methoxy-2,3-dimethylenedioxy-8-prenylrotenone (Usaratenoid C) present in the plant Millettia usaramensis showed maximum molecular docking score.

}, keywords = {ADME, malaria, Molecular docking, molecular operating environment, Plasmodium falciparum, virtual screening, weka}, issn = {1386-2073}, doi = {10.2174/1386207319666160420141200}, author = {Shibi, Indira G. and Aswathy, Lilly and Jisha, Radhakrishnan S. and Masand, Vijay H. and Gajbhiye, Jayant M.} } @article {47013, title = {Design of novel drug-like molecules using informatics rich secondary metabolites analysis of Indian medicinal and aromatic plants}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {23}, year = {2020}, month = {JAN}, pages = {1113-1131}, type = {Article}, abstract = {

Background: Several medicinal plants are being used in Indian medicine systems from ancient times. However, in most cases, the specific molecules or the active ingredients responsible for the medicinal or therapeutic properties are not yet known. Objective: This study aimed to report a computational protocol as well as a tool for generating novel potential drug candidates from the bioactive molecules of Indian medicinal and aromatic plants through the chemoinformatics approach. Methods: We built a database of the Indian medicinal and aromatic plants coupled with associated information (plant families, plant parts used for the medicinal purpose, structural information, therapeutic properties, etc.) We also developed a Java-based chemoinformatics open-source tool called DoMINE (Database of Medicinally Important Natural products from plantaE) for the generation of virtual library and screening of novel molecules from known medicinal plant molecules. We employed chemoinformatics approaches to in-silico screened metabolites from 104 Indian medicinal and aromatic plants and designed novel drug-like bioactive molecules. For this purpose, 1665 ring containing molecules were identified by text mining of literature related to the medicinal plant species, which were later used to extract 209 molecular scaffolds. Different scaffolds were further used to build a focused virtual library. Virtual screening was performed with cluster analysis to predict drug-like and lead-like molecules from these plant molecules in the context of drug discovery. The predicted drug-like and lead-like molecules were evaluated using chemoinformatics approaches and statistical parameters, and only the most significant molecules were proposed as the candidate molecules to develop new drugs. Results and Conclusion: The supra network of molecules and scaffolds identifies the relationship between the plant molecules and drugs. Cluster analysis of virtual library molecules showed that novel molecules had more pharmacophoric properties than toxicophoric and chemophoric properties. We also developed the DoMINE toolkit for the advancement of natural product-based drug discovery through chemoinformatics approaches. This study will be useful in developing new drug molecules from the known medicinal plant molecules. Hence, this work will encourage experimental organic chemists to synthesize these molecules based on the predicted values. These synthesized molecules need to be subjected to biological screening to identify potential molecules for drug discovery research.

}, keywords = {Drugs, medicinal plants, Metabolites, scaffolds, text mining, virtual libraries, virtual screening}, issn = {1386-2073}, doi = {10.2174/1386207323666200606211342}, author = {Karade, Divya and Vijayasarathi, Durairaj and Kadoo, Narendra and Vyas, Renu and Ingle, P. K. and Karthikeyan, Muthukumarasamy} } @article {47392, title = {Machine learning classifiers aid virtual screening for efficient design of mini-protein therapeutics}, journal = {Bioorganic \& Medicinal Chemistry Letters}, volume = {38}, year = {2021}, month = {APR }, pages = {127852}, type = {Article}, abstract = {

De novo design of mini-proteins (4-12 kDa) has recently been shown to produce new candidates for protein therapeutics. They are temperature stable molecules that bind to the drug target with high affinity for inhibiting its interactions. The development of mini-protein binders requires laboratory screening of tens of thousands of molecules for effective target binding. In this study we trained machine learning classifiers which can distinguish, with 90\% accuracy and 80\% precision, mini-protein binders from non-binding molecules designed for a particular target; this significantly reduces the number of mini protein candidates for experimental screening. Further, on the basis of our results we propose a multi-stage protocol where a small dataset (few hundred experimentally verified target-specific mini-proteins) can be used to train classifiers for improving the efficiency of mini-protein design for any specific target.

}, keywords = {Drug design, machine learning, Mini-proteins, Protein therapeutics, virtual screening}, issn = {0960-894X}, doi = {10.1016/j.bmcl.2021.127852}, author = {Gaur, Neeraj K. and Goyal, Venuka Durani and Kulkarni, Kiran and Makde, Ravindra D.} } @article {46985, title = {Pharmacophore modeling of pretomanid (PA-824) derivatives for antitubercular potency against replicating and non-replicating Mycobacterium tuberculosis}, journal = {Journal of Biomolecular Structure \& Dynamics}, volume = {39}, year = {2021}, month = {FEB}, pages = {889-900}, type = {Article}, abstract = {

Pretomanid (PA-824) is the recently (2019) approved drug for the treatment of extensively drug-resistant (XDR) TB and the multidrug-resistant (MDR) TB by US FDA. The experimental data of antitubercular activity of 543 pretomanid derivatives (total 6 datasets) against replicating (active) and non-replicating (dormant) forms of Mycobacterium tuberculosis (strain H37Rv) are available in the literature. Such vast experimental data of pretomanid derivatives against both of these endpoints, and recent approval of pretomanid molecule as a drug encouraged us to utilize this existing experimental information for the development of the 3D-pharmacophore models. The developed model (Hypo-1, MABA) showed the three physicochemical features namely, the oxygen atom of nitro group (HBA_1), fused pyran ring of imidazopyran heterocycle (HYAl_2) and the 4-fluorophenyl moiety (HYAr_3) are crucial for the antitubercular activity against replicating M. tb. Subsequently, the pharmacophore model (Hypo-1, LORA) developed against the non-replicating form of M. tb also showed the contribution of three physicochemical features namely, the 4-tri-fluoromethyl group (HYAl_2) and both the phenyl groups (HYAr_3, HYAr_4) of biaryl moiety in increasing the antitubercular activity. Both the pharmacophoric classifier models showed the classification accuracies of 82.98 and 74.42\% for the training set compounds, and 63.91 and 61.60\% for the test set compounds respectively, for labelling the compounds into higher and lower active classes. Both the models were also found to be retaining the higher active compounds in top 1.00\% of the total number of compounds (decoys and actives), after performing the decoy set screening. Communicated by Ramaswamy H. Sarma

}, keywords = {LORA assay, MABA assay, Mycobacterium tuberculosis, non-replicating M, pharmacophore modeling, pretomanid derivatives, replicating M, tuberculosis, virtual screening}, issn = {0739-1102}, doi = {10.1080/07391102.2020.1719205, Early Access Date = JAN 2020}, author = {Aher, Rahul Balasaheb and Sarkar, Dhiman} } @article {47329, title = {Pharmacore maping based on docking, ADME/toxicity, virtual screening on 3,5-dimethyl-1,3,4-hexanetriol and dodecanoic acid derivates for anticancer inhibitors}, journal = {Journal of Biomolecular Structure \& Dynamics}, volume = {39}, year = {2021}, month = {JAN}, pages = {4490-4500}, type = {Article}, abstract = {

Plants produced natural generating products play a significant role in drug discovery of new bioactive compounds and these are used for advancement of innovative curative drugs for specific target health diseases. In this study Docking and ADME/T virtual screening method are apply for in drug discovery and can be divided into ligand- and target structure-based. The aim of this study was to analyze theDecalepis hamiltoniiisolated compounds by using the evaluation of molecular docking and virtual screening of anticancer drugs. MOE docking ADME/Toxicity and virtual screening approaches. A docking energy -12.97 kcal/mol; -9.93- kcal/mol on cancer responsible protein was targeted. Further, the compounds were filtered through the rule of five, ADME/Toxicity risk and synthetic accessibility. The active compound were then docked to recognize the possible target binding pocket to obtain a set of a ligand poses and to prioritize the predicted active compounds. The scrutinize compounds, as well as their metabolites were evaluated for different pharmacokinetics parameter such as ADME/Toxicity. Therefore, the result shows that a large number of compounds were found to be ADME/toxicity positive to be a positive drug molecule against cancer, selected compounds under study satisfies parameters for ADME and Toxicity properties. The present study demonstrate to identifying the novel structures which are having similar structural feature with like activity with respect to the compounds 3,5-Dimethyl-1,3,4-Hexanetriol and Dodecanoic acid that are shown best binding energy with the receptors 4igk and 4b3z respectively. This study may provide significant clues for discovery novel drug inhibitors for cancer properties.

}, keywords = {ADME, Decalepis hamiltonii, docking, drug discovery, Toxicity, virtual screening}, issn = {0739-1102}, doi = {10.1080/07391102.2020.1778533}, author = {Prakash, Palanisamy and Vijayasarathi, Durairaj and Selvam, Kuppusamy and Karthi, Sengodan and Manivasagaperumal, Rengarajan} }