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QSAR, Molecular Docking, MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads

Rahul D. Jawarkar, Praveen Sharma, Neetesh Jain, Ajaykumar Gandhi, Nobendu Mukerjee, Aamal A. Al-Mutairi, Magdi E. A. Zaki, Sami A. Al-Hussain, Abdul Samad, Vijay H. Masand, Arabinda Ghosh, Ravindra L. Bakal
2022 Molecules  
In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds.  ...  As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase  ...  Acknowledgments: The authors are thankful to Paola Gramatica and her team for providing QSA-RINS-v2.2.4 and developers of TINKER, ChemSketch 12 Freeware (ACD labs), and PyDescriptor for providing the free  ... 
doi:10.3390/molecules27154951 pmid:35956900 pmcid:PMC9370430 fatcat:6l6ubomspvgyfgmokdhdgdcp4e

Proteome‐Scale Drug‐Target Interaction Predictions: Approaches and Applications

Stephen Scott MacKinnon, S. A. Madani Tonekaboni, Andreas Windemuth
2021 Current Protocols  
and provide guidelines for the design and implementation of new drug-target interaction prediction models.  ...  We will discuss the validation approaches used, and propose a set of key criteria that should be applied to evaluate their validity.  ...  models.  ... 
doi:10.1002/cpz1.302 pmid:34794211 fatcat:e6fnkrgf7bgvxia6mxz3kq2lpu

Cross-Reactivity Virtual Profiling of the Human Kinome by X-ReactKIN: A Chemical Systems Biology Approach

Michal Brylinski, Jeffrey Skolnick
2010 Molecular Pharmaceutics  
To maximize the coverage of the kinome, X-React KIN relies solely on the predicted target structures and employs state-of-the-art modeling techniques.  ...  In this paper, we describe X-React KIN , a new machine learning approach that extends the modeling and virtual screening of individual protein kinases to a system level in order to construct a crossreactivity  ...  This work was supported in part by Grants GM-48835 and GM-37408 of the Division of General Medical Sciences of the National Institutes of Health.  ... 
doi:10.1021/mp1002976 pmid:20958088 pmcid:PMC2997910 fatcat:3mnpocfakjflnody3nuwlk63bq

The 18th European Symposium on Quantitative Structure–Activity Relationships

Anna Tsantili-Kakoulidou, Dimitris K Agrafiotis
2011 Expert Opinion on Drug Discovery  
effective binding in the kinase.  ...  Wigley for the contribution to this work with the x-ray structure of the protein ligand to the GR1222222.  ...  This data set was divided into 50 training set compounds used to build QSAR models and 8 test set compounds to evaluate the predictive capability of each model.  ... 
doi:10.1517/17460441.2011.560604 pmid:22646021 fatcat:tb4bhvtnpzahxm4xba7iw4afuy

Multi-Target in Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases

Amit Kumar Halder, M. Natália D. S. Cordeiro
2021 Biomolecules  
The virtual hits identified in this process were further filtered by using a similarity search, in silico prediction of drug-likeness, and ADME profiles as well as synthetic accessibility tools.  ...  The inhibitors of two isoforms of mitogen-activated protein kinase-interacting kinases (i.e., MNK-1 and MNK-2) are implicated in the treatment of a number of diseases including cancer.  ...  Acknowledgments: Thanks are due to Pravin Ambure of ProtoQSAR (Spain) for providing the GA-LDA_v1.0 tool used to set up the GA-LDA models.  ... 
doi:10.3390/biom11111670 pmid:34827668 pmcid:PMC8615736 fatcat:gq7l3ufm2va4lf4scffsfq2qdy

Toward more realistic drug-target interaction predictions

T. Pahikkala, A. Airola, S. Pietila, S. Shakyawar, A. Szwajda, J. Tang, T. Aittokallio
2014 Briefings in Bioinformatics  
complexity of the prediction task in the practical applications, as well as novel benchmarking data sets that capture the continuous nature of the drug^target interactions for kinase inhibitors.  ...  Using quantitative drug^target bioactivity assays for kinase inhibitors, as well as a popular benchmarking data set of binary drug^target interactions for enzyme, ion channel, nuclear receptor and G protein-coupled  ...  ACKNOWLEDGEMENTS The authors thank the authors of the studies by Yamanishi et al. [13] , Davis et al. [27] and Metz et al. [28] for making their data publicly available.  ... 
doi:10.1093/bib/bbu010 pmid:24723570 pmcid:PMC4364066 fatcat:hltwd4oqubedhczxohktzxqm7i

Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects

Isidro Cortés-Ciriano, Qurrat Ul Ain, Vigneshwari Subramanian, Eelke B. Lenselink, Oscar Méndez-Lucio, Adriaan P. IJzerman, Gerd Wohlfahrt, Peteris Prusis, Thérèse E. Malliavin, Gerard J. P. van Westen, Andreas Bender
2015 MedChemComm  
Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously.  ...  dence for the predictions; and (iv) considering the experimental uncertainty in the modelling.  ...  metrics proposed by Golbraikh and Tropsha 76 can be used (similar to QSAR) to validate models using observed and predicted values on the test set.  ... 
doi:10.1039/c4md00216d fatcat:kvf2cuib6fbjta4dferweweugm

Predicting Drug-Induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

Yen Low, Takeki Uehara, Yohsuke Minowa, Hiroshi Yamada, Yasuo Ohno, Tetsuro Urushidani, Alexander Sedykh, Eugene Muratov, Viktor Kuz'min, Denis Fourches, Hao Zhu, Ivan Rusyn (+1 others)
2011 Chemical Research in Toxicology  
The data within each modeling set were further divided into multiple pairs of training and test sets for internal validation.  ...  In other words, pairs of compounds with similar gene expression profiles were more likely to have the same hepatotoxicity than pairs of chemically similar compounds.  ... 
doi:10.1021/tx200148a pmid:21699217 pmcid:PMC4281093 fatcat:w5g6kdquwbb7xft5en3pgcyu5i

Which Compound to Select in Lead Optimization? Prospectively Validated Proteochemometric Models Guide Preclinical Development

Gerard J. P. van Westen, Jörg K. Wegner, Peggy Geluykens, Leen Kwanten, Inge Vereycken, Anik Peeters, Adriaan P. IJzerman, Herman W. T. van Vlijmen, Andreas Bender, Mark Wainberg
2011 PLoS ONE  
We validated our model by experimentally confirming model predictions for 317 untested compound -mutant pairs, with a prediction error comparable with assay variability (RMSE 0.62).  ...  Hence, our models allow the evaluation of compound performance on untested sequences and the selection of the most promising leads for further preclinical research.  ...  Acknowledgments GvW would like to thank Tibotec BVBA for generously providing the data set. Author Contributions Conceived and designed the experiments: GJPvW JKW API HWTvV AB.  ... 
doi:10.1371/journal.pone.0027518 pmid:22132107 pmcid:PMC3223189 fatcat:hf56j24ud5eefbaguimf7hid5m

QSAR development and profiling of 72,524 REACH substances for PXR activation and CYP3A4 induction

S.A. Rosenberg, M. Xia, R. Huang, N.G. Nikolov, E.B. Wedebye, M. Dybdahl
2017 Computational Toxicology  
Statistical analyses of the experimental drug dataset and the QSAR-predicted set of REACH substances were performed to identify similarities and differences in frequencies of overlapping positive results  ...  These predictions can, for example, be used for priority setting and in weight-of-evidence assessments of chemicals.  ...  Acknowledgements We would like to thank the Danish 3R Center and the Danish Environmental Protection Agency for supporting the project.  ... 
doi:10.1016/j.comtox.2017.01.001 fatcat:bj5emluw6vafdhesvnv62inufy

Cheminformatics aspects of high throughput screening: from robots to models: symposium summary

Y. Jane Tseng, Eric Martin, Cristian G. Bologa, Anang A. Shelat
2013 Journal of Computer-Aided Molecular Design  
Specifically, this article also covers the remarks and discussions in the open panel discussion of the symposium and summarizes the following talks on "Accurate Kinase virtual screening: biochemical, cellular  ...  The "Cheminformatics aspects of high throughput screening (HTS): from robots to models" symposium was part of the computers in chemistry technical program at the American Chemical Society National Meeting  ...  of results, and preparing the slides for the molecular matching pairs part of the presentation.  ... 
doi:10.1007/s10822-013-9646-6 pmid:23636795 pmcid:PMC4205101 fatcat:kxvlwscr2vadvmttflvzo7s52a

Computational Methods in Drug Discovery

G. Sliwoski, S. Kothiwale, J. Meiler, E. W. Lowe
2013 Pharmacological Reviews  
Authorship Contributions Wrote or contributed to the writing of the manuscript: Sliwoski, Kothiwale, Meiler, Lowe.  ...  Raw similarity scores between all pairs of ligand sets are calculated as the sum of all Tanimoto coefficients between the sets greater than 0.57.  ...  Models are numerically assessed and ranked by estimating similarity between a model and corresponding experimental structure.  ... 
doi:10.1124/pr.112.007336 pmid:24381236 pmcid:PMC3880464 fatcat:4dzrdkspkjecnombnchznma2ny

A comparative QSAR analysis and molecular docking studies of quinazoline derivatives as tyrosine kinase (EGFR) inhibitors: A rational approach to anticancer drug design

Malleshappa N. Noolvi, Harun M. Patel
2013 Journal of Saudi Chemical Society  
The contribution plot of steric and electrostatic field interactions generated by 3D-QSAR shows interesting results in terms of internal and external predictability.  ...  Hence the model proposed in this work can be employed to design new derivatives of quinazoline with specific tyrosine kinase (EGFR) inhibitory activity.  ...  Acknowledgements The authors would like to thank the Director General, Department of Science and Technology, New Delhi for funding the project (Grant No. SR/FT/LS-0083/2008), Chairman, Captain  ... 
doi:10.1016/j.jscs.2011.04.017 fatcat:sn6nwg57unfrlnwavlu23cj62q

Multi-task learning with a natural metric for quantitative structure activity relationship learning

Noureddin Sadawi, Ivan Olier, Joaquin Vanschoren, Jan N. van Rijn, Jeremy Besnard, Richard Bickerton, Crina Grosan, Larisa Soldatova, Ross D. King
2019 Journal of Cheminformatics  
These results indicate that QSAR learning can be performed effectively, even if little data is available for specific drug targets, by leveraging what is known about similar drug targets.  ...  The goal of quantitative structure activity relationship (QSAR) learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound  ...  Acknowledgements This research was funded by the Engineering and Physical Sciences Research Council (EPSRC) grant EP/K030469/1.  ... 
doi:10.1186/s13321-019-0392-1 pmid:33430958 fatcat:lxt5jk6eozaj3crkvvpvqgycxq

A combined 2-D and 3-D QSAR modeling, molecular docking study, design, and pharmacokinetic profiling of some arylimidamide-azole hybrids as superior L. donovani inhibitors

Fabian Audu Ugbe, Gideon Adamu Shallangwa, Adamu Uzairu, Ibrahim Abdulkadir
2022 Bulletin of the National Research Centre  
Pyridoxal kinase (PdxK) receptor (PDB: 6K91) was the target protein of interest in this study.  ...  least square (UVEPLS) were employed for building the 3-D QSAR model.  ...  Harrison Quantum Chemistry Research Group, Ahmadu Bello University Zaria, for providing all software and suitable environment utilized for this study.  ... 
doi:10.1186/s42269-022-00874-1 doaj:4b990674f619464faa28bf5f519eae6a fatcat:blxncaxpobc5revmgen2vddo3y
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