Relevant bibliographies by topics / Autodock

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Autodock'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Autodock"

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Tanisa, Asti Anna, and Rezi Riadhi. "VIRTUAL SCREENING OF BETA-SECRETASE 1 (BACE1) INHIBITORS IN THE INDONESIAN HERBAL DATABASE AS USING AUTODOCK AND AUTODOCK VINA." Asian Journal of Pharmaceutical and Clinical Research 10, no. 17 (October 1, 2017): 148. http://dx.doi.org/10.22159/ajpcr.2017.v10s5.23119.

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Objective: Alzheimer’s is a neurodegenerative disease caused by the accumulation of senile plaque in the brain that affects neuronal system leading to a less sensitive cellular response from neurons. Previous research has found that beta-secretase 1 (BACE1) plays an important role in the senile plaque formation, become a target in Alzheimer’s medication.Methods: In this study, virtual screening of BACE1 inhibitors on the Indonesian Herbal Database was done using AutoDock and AutoDock Vina. The screening was validated using the directory of useful decoys: Enhanced database. Parameters for validation process of AutoDock and AutoDock Vina are enrichment factor (EF), receiver operating characteristics, and area under the curve (AUC).Results: The dimensions of grid boxes were 30×30×30 (AutoDock) and 11.25×11.25×11.25 (AutoDock Vina). The EF 1% and AUC values obtained from the AutoDock are 7.74 and 0.73, respectively, and in the AutoDock Vina are 4.6 and 0.77, respectively. Based on the virtual screening results, the top six compounds obtained using AutoDock (binding energy ranging from −7.84 kcal/mol to −8.79 kcal/mol) include: Azadiradione, cylindrin, lanosterol, sapogenin, simiarenol, and taraxerol. The top seven compounds (binding energy ranging from −8.8 kcal/mol to −9.4 kcal/mol) obtained using AutoDeck Vina include: Bryophyllin A, diosgenin, azadiradione, sojagol, beta-amyrin, epifriedelinol, and jasmolactone C.Conclusions: Only azadiradione was obtained from the virtual screening conducted using both types of software; it interacts with the active region in BACE1 at residue Trp 76 (AutoDock result) and Thr 232 (AutoDock Vina result).
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Arcon, Juan Pablo, Carlos P. Modenutti, Demian Avendaño, Elias D. Lopez, Lucas A. Defelipe, Francesca Alessandra Ambrosio, Adrian G. Turjanski, Stefano Forli, and Marcelo A. Marti. "AutoDock Bias: improving binding mode prediction and virtual screening using known protein–ligand interactions." Bioinformatics 35, no. 19 (March 2, 2019): 3836–38. http://dx.doi.org/10.1093/bioinformatics/btz152.

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Abstract Summary The performance of docking calculations can be improved by tuning parameters for the system of interest, e.g. biasing the results towards the formation of relevant protein–ligand interactions, such as known ligand pharmacophore or interaction sites derived from cosolvent molecular dynamics. AutoDock Bias is a straightforward and easy to use script-based method that allows the introduction of different types of user-defined biases for fine-tuning AutoDock4 docking calculations. Availability and implementation AutoDock Bias is distributed with MGLTools (since version 1.5.7), and freely available on the web at http://ccsb.scripps.edu/mgltools/ or http://autodockbias.wordpress.com. Supplementary information Supplementary data are available at Bioinformatics online.
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Nguyen, Nguyen Thanh, Trung Hai Nguyen, T. Ngoc Han Pham, Nguyen Truong Huy, Mai Van Bay, Minh Quan Pham, Pham Cam Nam, Van V. Vu, and Son Tung Ngo. "Autodock Vina Adopts More Accurate Binding Poses but Autodock4 Forms Better Binding Affinity." Journal of Chemical Information and Modeling 60, no. 1 (December 30, 2019): 204–11. http://dx.doi.org/10.1021/acs.jcim.9b00778.

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Gaillard, Thomas. "Evaluation of AutoDock and AutoDock Vina on the CASF-2013 Benchmark." Journal of Chemical Information and Modeling 58, no. 8 (July 10, 2018): 1697–706. http://dx.doi.org/10.1021/acs.jcim.8b00312.

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Tanchuk, Vsevolod, Volodymyr Tanin, Andriy Vovk, and Gennady Poda. "A New Scoring Function for Molecular Docking Based on AutoDock and AutoDock Vina." Current Drug Discovery Technologies 12, no. 3 (September 16, 2015): 170–78. http://dx.doi.org/10.2174/1570163812666150825110208.

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Tang, Shidi, Ruiqi Chen, Mengru Lin, Qingde Lin, Yanxiang Zhu, Ji Ding, Haifeng Hu, Ming Ling, and Jiansheng Wu. "Accelerating AutoDock Vina with GPUs." Molecules 27, no. 9 (May 9, 2022): 3041. http://dx.doi.org/10.3390/molecules27093041.

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AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all the docking tools. Modern drug discovery is facing a common scenario of large virtual screening of drug hits from huge compound databases. Due to the seriality characteristic of the AutoDock Vina algorithm, there is no successful report on its parallel acceleration with GPUs. Current acceleration of AutoDock Vina typically relies on the stack of computing power as well as the allocation of resource and tasks, such as the VirtualFlow platform. The vast resource expenditure and the high access threshold of users will greatly limit the popularity of AutoDock Vina and the flexibility of its usage in modern drug discovery. In this work, we proposed a new method, Vina-GPU, for accelerating AutoDock Vina with GPUs, which is greatly needed for reducing the investment for large virtual screens and also for wider application in large-scale virtual screening on personal computers, station servers or cloud computing, etc. Our proposed method is based on a modified Monte Carlo using simulating annealing AI algorithm. It greatly raises the number of initial random conformations and reduces the search depth of each thread. Moreover, a classic optimizer named BFGS is adopted to optimize the ligand conformations during the docking progress, before a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmark tests show that Vina-GPU reaches an average of 21-fold and a maximum of 50-fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential for pushing the popularization of AutoDock Vina in large virtual screens.
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Goodsell, David S., Michel F. Sanner, Arthur J. Olson, and Stefano Forli. "The AutoDock suite at 30." Protein Science 30, no. 1 (September 12, 2020): 31–43. http://dx.doi.org/10.1002/pro.3934.

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Salamah, Nabilah Nurtika, Widya Dwi Aryati, and Arry Yanuar. "Virtual Screening of Indonesian Herbal Database as Adenosine A2A Antagonist using AutoDock and AutoDock Vina." Pharmacognosy Journal 11, no. 6 (October 15, 2019): 1219–24. http://dx.doi.org/10.5530/pj.2019.11.189.

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Ivonie, Ulfa, Arry Yanuar, and Firdayani . "VIRTUAL SCREENING OF INDONESIAN HERBAL DATABASE FOR CP ALLOSTERIC MODULATOR OF HEPATITIS B VIRUS." International Journal of Applied Pharmaceutics 10, no. 1 (December 20, 2018): 190. http://dx.doi.org/10.22159/ijap.2018.v10s1.42.

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Objective: This study performed a virtual screening of the Indonesian Herbal Database for the core protein allosteric modulator of the hepatitis Bvirus (HBV) using AutoDock and AutoDock Vina software, to discover novel safe drugs for patients.Methods: The method was validated using the parameters enrichment factor (EF), receiver operating characteristics, and area under the curve (AUC).The grid box size used in virtual screening with AutoDock was 55 × 55 × 55 with EF10% of 0.7652 and AUC of 0.6709, whereas that used in virtualscreening with AutoDock Vina was 20.625 × 20.625 × 20.625 with EF5% of 0.5075 and AUC of 0.7832.Results: The top 10 compounds from virtual screening with AutoDock at G levels −11.74–−10.31 kcal/mol were yuehchukene, lansionic acid, stigmast-4-en-3-one, myrtillin, sanggenol O, lanosterol, erycrista-gallin, alpha-spinasterol, cyanidin 3-arabinoside, and cathasterone and with AutoDock Vinaat G levels −12.1 to −10.7 kcal/mol were sanggenol O, cucumerin A, yuehchukene, palmarumycin CP1, dehydrocycloguanandin, myrtillin, liriodenine,myricetin 3-alpha-L-arabinopyranoside, myricetin 3-galactoside, and cassameridine.Conclusion: Three compounds were in top list of both virtual screening methods against Cp allosteric modulator of HBV are myrtillin, sanggenol O,and yuehchukene have a prospect to be investigated futher for anti HBV.
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Tanchuk, Vsevolod Yu, Volodymyr O. Tanin, Andriy I. Vovk, and Gennady Poda. "A New, Improved Hybrid Scoring Function for Molecular Docking and Scoring Based on AutoDock and AutoDock Vina." Chemical Biology & Drug Design 87, no. 4 (December 29, 2015): 618–25. http://dx.doi.org/10.1111/cbdd.12697.

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Dissertations / Theses on the topic "Autodock"

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Wang, Qi. "Protein-ligand Docking Application and Comparison using Discovery Studio and AutoDock." Thesis, North Dakota State University, 2017. https://hdl.handle.net/10365/28365.

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Protein-ligand docking is a structure-based computational method, which is used to predict the small molecule binding modes and binding affinities with protein receptors. The goals of this study are to compare the docking performances of different software and apply the docking method to predict how protein fatty acid desaturase 1 (FADS1) interact with ligands. Two docking software, Discovery Studio and AutoDock, are used for docking comparison of 195 protein-ligand complexes from PDBind dataset. AutoDock performs a little bit better than Discovery Studio on the docking percentage, which is the percent of the docked complexes out of 195. On the other hand, Discovery Studio has a higher accuracy (successfully docked complexes, within 5 RMSD of the native complex structures) than AutoDock. The interaction between FADS1 and Sesamin shows a similar pattern comparing to the interaction between a homolog of FADS1 and a ligand shown in a PDB structure (PDB id 1EUE).
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Nadas, Janos Istvan. "Computational Structure Activity Relationship Studies on the CD1d/Glycolipid/TCR Complex using AMBER and AUTODOCK." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1251145085.

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Jesus, Éverton Mendonça de. "Adaptação e avaliação de triagem virtual em arquiteturas paralelas híbridas." Instituto de Matemática. Departamento de Ciência da Computação, 2016. http://repositorio.ufba.br/ri/handle/ri/22716.

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A Triagem Virtual é uma metodologia computacional de busca de novos fármacos que verifica a interação entre moléculas (ligantes) e alvos macromoleculares. Este trabalho Objetivou a adaptação de uma ferramenta de Triagem Virtual para arquiteturas paralelas com GPUs e multicore e avaliação dos seus resultados, buscando com isso aumentar o desempenho da triagem, reduzindo seu tempo de execução e, consequentemente, permitindo a escalabilidade do número de moléculas envolvidas no processo. A ferramenta escolhida Para este propósito foi o Autodock devido a sua ampla adoção dentre os pesquisadores de novos fármacos que utilizam a Triagem Virtual. Três implementações foram criadas abordando diferentes técnicas de paralelismo. A primeira foi uma versão multicore onde foi utilizado OpenMP, a segunda foi uma implementação em GPUs utilizando CUDA e porém, foi criada uma implementação híbrida utilizando a versão multicore e a versão para GPUs em conjunto. Em todas as abordagens foram alcançados bons resultados em relação ao tempo de execução total, porém a versão híbrida foi a que obteve os melhores resultados. A versão multicore alcançou speedups, ou ganhos de desempenho, da ordem de 10 vezes. A versão para GPUs alcançou speedups da ordem de 28 vezes e a híbrida de 85 vezes. Com estes resultados foi possível determinar que o uso de plataformas de execução paralelas podem, efetivamente, melhorar o desempenho Triagem Virtual.
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DI, DOMIZIO ALESSANDRO. "Development of methodologies for molecular docking and their applications." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2009. http://hdl.handle.net/10281/7460.

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Virtual High Throughput Screening (vHTS) has an increasingly important role in lowering both costs and time in drug discovery. The present work deals with the development (C++ programming language) of a new molecular docking software (semi-flexible model) to be used for vHTS. It would improve some of the main aspects of this type of softwares in current use, with particular reference to the program AutoDock: a particular importance is given to the calculation of the ligand conformational energy variation in passing from the unbound to the bound state, which represents a crucial term in the docking energy definition. Two real cases to which the new software modules were applied are then presented: the first belonging to drug discovery, concerning the study of new Ras oncogenic protein inhibitors; the second belonging to virtual protein engineering (VPE), concerning the design of a modified enzyme to be used in the manufacturing of semisynthetic antibiotics.
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Ghiasi, Zahra. "Development of a Computational Mechanism to Generate Molecules with Drug-likeCharacteristics." Ohio University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou162861276157897.

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Österberg, Fredrik. "Exploring Ligand Binding in HIV-1 Protease and K+ Channels Using Computational Methods." Doctoral thesis, Uppsala universitet, Strukturell molekylärbiologi, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6167.

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Understanding protein-ligand interactions is highly important in drug development. In the present work the objective is to comprehend the link between structure and function using molecular modelling. Specifically, this thesis has been focused on implementation of receptor flexibility in molecular docking and studying structure-activity relationships of potassium ion channels and their blockers. In ligand docking simulations protein motion and heterogeneity of structural waters are approximated using an ensemble of protein structures. Four methods of combining multiple target structures within a single grid-based lookup table of interaction energies are tested. Two weighted average methods permit consistent and accurate ligand docking using a single grid representation of the target protein structures. Quaternary ammonium ions (QAIs) are well known K+ channel blockers. Conformations around C–N bonds at the quaternary centre in tetraalkylammonium ions in water solution are investigated using quantum mechanical methods. Relative solvation free energies of QAIs are further estimated from molecular dynamics simulations. The torsion barrier for a two-step interconversion between the conformations D2d and S4 is calculated to be 9.5 kcal mol–1. Furthermore D2d is found to be more stable than the S4 conformation which is in agreement with experimental studies. External QAI binding to the K+ channel KcsA is also studied. Computer simulations and relative binding free energies of the KcsA complexes with QAIs are calculated. This is done with the molecular dynamics free energy perturbation approach together with automated ligand docking. In agreement with experiment, the Et4N+ blocker in D2d symmetry has better binding than the other QAIs. Binding of blockers to the human cardiac hERG potassium channel is studied using a combination of homology modelling, automated docking and molecular dynamics simulations. The calculations reproduce the relative binding affinities of a set of drug derivatives very well and indicate that both polar interactions near the intracellular opening of the selectivity filter as well as hydrophobic complementarity in the region around F656 are important for blocker binding. Hence, the derived model of hERG should be useful for further interpretations of structure-activity relationships.
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Chen, Sih-Yu. "Computational studies of biomolecules." Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/11064.

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In modern drug discovery, lead discovery is a term used to describe the overall process from hit discovery to lead optimisation, with the goal being to identify drug candidates. This can be greatly facilitated by the use of computer-aided (or in silico) techniques, which can reduce experimentation costs along the drug discovery pipeline. The range of relevant techniques include: molecular modelling to obtain structural information, molecular dynamics (which will be covered in Chapter 2), activity or property prediction by means of quantitative structure activity/property models (QSAR/QSPR), where machine learning techniques are introduced (to be covered in Chapter 1) and quantum chemistry, used to explain chemical structure, properties and reactivity. This thesis is divided into five parts. Chapter 1 starts with an outline of the early stages of drug discovery; introducing the use of virtual screening for hit and lead identification. Such approaches may roughly be divided into structure-based (docking, by far the most often referred to) and ligand-based, leading to a set of promising compounds for further evaluation. Then, the use of machine learning techniques, the issue of which will be frequently encountered, followed by a brief review of the "no free lunch" theorem, that describes how no learning algorithm can perform optimally on all problems. This implies that validation of predictive accuracy in multiple models is required for optimal model selection. As the dimensionality of the feature space increases, the issue referred to as "the curse of dimensionality" becomes a challenge. In closing, the last sections focus on supervised classification Random Forests. Computer-based analyses are an integral part of drug discovery. Chapter 2 begins with discussions of molecular docking; including strategies incorporating protein flexibility at global and local levels, then a specific focus on an automated docking program – AutoDock, which uses a Lamarckian genetic algorithm and empirical binding free energy function. In the second part of the chapter, a brief introduction of molecular dynamics will be given. Chapter 3 describes how we constructed a dataset of known binding sites with co-crystallised ligands, used to extract features characterising the structural and chemical properties of the binding pocket. A machine learning algorithm was adopted to create a three-way predictive model, capable of assigning each case to one of the classes (regular, orthosteric and allosteric) for in silico selection of allosteric sites, and by a feature selection algorithm (Gini) to rationalize the selection of important descriptors, most influential in classifying the binding pockets. In Chapter 4, we made use of structure-based virtual screening, and we focused on docking a fluorescent sensor to a non-canonical DNA quadruplex structure. The preferred binding poses, binding site, and the interactions are scored, followed by application of an ONIOM model to re-score the binding poses of some DNA-ligand complexes, focusing on only the best pose (with the lowest binding energy) from AutoDock. The use of a pre-generated conformational ensemble using MD to account for the receptors' flexibility followed by docking methods are termed “relaxed complex” schemes. Chapter 5 concerns the BLUF domain photocycle. We will be focused on conformational preference of some critical residues in the flavin binding site after a charge redistribution has been introduced. This work provides another activation model to address controversial features of the BLUF domain.
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Дядечко, Алла Миколаївна, Алла Николаевна Дядечко, Alla Mykolaivna Diadechko, and O. Shulima. "3D graphics: autodesk maya." Thesis, Видавництво СумДУ, 2010. http://essuir.sumdu.edu.ua/handle/123456789/17883.

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Tiwari, Rohit. "COMPUTATIONAL AND SYNTHETIC STUDIES ON ANTIMETABOLITES FOR ANTICANCER-, ANTIVIRAL-,AND ANTIBIOTIC DRUG DISCOVERY." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1267819591.

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Ludwigsson, Jonas. "Creating realistic hair in Autodesk Maya." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-14411.

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This thesis work focuses on how to create realistic looking hair using only the vanilla version of Autodesk Maya. It describes two approaches, the widely used polygon-stripe based technique and the Maya built-in nHair. It also evaluates these two approaches in terms of ease of implementation, production speed and quality of final results. The conclusion is that nHair has the potential to produce realistic looking hair but contains various bugs and is not optimized at the current stage, while the polygon-stripe based approach is robust and flexible but the realism of rendering results is heavily dependent upon the skill level of artists.
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Books on the topic "Autodock"

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Patel, Rajesh, and Vishal Mevada. Beginner`s Guide to Protein- Ligand Docking Using Autodock Vina: HOW to DOCK in 3 EASY STEPS USING AUTODOCK VINA Concise Guide to Docking. Independently Published, 2017.

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Dan, Sonnen David Vesset. Autodesk. IDC Research, 2005.

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Hurkman, Alexis Van. Autodesk Smoke Essentials: Autodesk Official Press. Wiley & Sons, Incorporated, John, 2013.

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Aubin, Paul F. Mastering Autodesk Revit Building (Autodesk Revit). Autodesk Press, 2006.

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Hurkman, Alexis Van. Autodesk Smoke Essentials: Autodesk Official Press. Sybex, 2014.

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Hurkman, Alexis Van. Autodesk Smoke Essentials: Autodesk Official Press. Wiley & Sons, Incorporated, John, 2014.

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Hurkman, Alexis Van. Autodesk Smoke Essentials: Autodesk Official Press. Wiley & Sons, Incorporated, John, 2014.

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Hurkman, Alexis Van. Autodesk Smoke Essentials: Autodesk Official Press. Wiley & Sons, Incorporated, John, 2014.

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Hurkman, Alexis Van. Autodesk Smoke Essentials: Autodesk Official Press. Wiley & Sons, Incorporated, John, 2013.

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Waguespack, Curtis. Mastering Autodesk Inventor 2014 and Autodesk Inventor LT 2014: Autodesk Official Press. Wiley & Sons, Incorporated, John, 2013.

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Book chapters on the topic "Autodock"

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Hill, Anthony D., and Peter J. Reilly. "Scoring Functions for AutoDock." In Methods in Molecular Biology, 467–74. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2343-4_27.

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Hart, William E., Chris Rosin, Richard K. Belew, and Garrett M. Morris. "Improved Evolutionary Hybrids for Flexible Ligand Docking in AutoDock." In Nonconvex Optimization and Its Applications, 209–29. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3218-4_12.

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Varela-Salinas, Génesis, Carlos Armando García-Pérez, Rafael Peláez, and Adolfo J. Rodríguez. "Visual Clustering Approach for Docking Results from Vina and AutoDock." In Lecture Notes in Computer Science, 342–53. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59650-1_29.

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Liu, Zhuoran, Changsheng Zhang, Qidong Zhao, Bin Zhang, and Wenjuan Sun. "Comparative Study of Evolutionary Algorithms for Protein-Ligand Docking Problem on the AutoDock." In Simulation Tools and Techniques, 598–607. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32216-8_58.

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K, Rohini, and Shanthi V. "Receptor Cavity-Based Approach Combined with Autodock Protocol for the Screening of Antiviral Compounds from Streptomyces sp." In Methods in Actinobacteriology, 307–18. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1728-1_39.

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Sendler, Ulrich. "Autodesk." In Xpert.press, 135–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-87898-8_17.

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Bitencourt-Ferreira, Gabriela, Val Oliveira Pintro, and Walter Filgueira de Azevedo. "Docking with AutoDock4." In Methods in Molecular Biology, 125–48. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9752-7_9.

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El-Hachem, Nehme, Benjamin Haibe-Kains, Athar Khalil, Firas H. Kobeissy, and Georges Nemer. "AutoDock and AutoDockTools for Protein-Ligand Docking: Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1(BACE1) as a Case Study." In Methods in Molecular Biology, 391–403. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6952-4_20.

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Kishore, T. "Getting Started with Autodesk Inventor 2018." In Learn Autodesk Inventor 2018 Basics, 1–14. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3225-5_1.

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Miroshnychenko, Kateryna V., and Anna V. Shestopalova. "Molecular Docking of Biologically Active Substances to Double Helical Nucleic Acids." In Advances in Medical Technologies and Clinical Practice, 127–57. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0362-0.ch005.

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Molecular docking of ligands to DNA-targets is of great importance for the design of new anticancer drugs. Unfortunately, most docking programs were developed for protein-ligand docking which raises a question about their applicability for the DNA-ligand docking. In this study, the popular docking programs AutoDock Vina, AutoDock4 and AutoDock3 were compared for a test set of 50 DNA-ligand complexes taken from the Nucleic Acid Database. It was shown that the version 3.05 of the AutoDock program was the most successful in reproducing the structures of intercalation and minor-groove complexes. The program AutoDock4 was able to re-dock to within 2 Å RMSD most of the intercalation complexes of the test set, but showed poor performance for minor groove binders. While Vina, on the contrary, failed to construct six intercalation complexes of the test set, but showed satisfactory results for DNA-ligand minor-groove complexes when small search space was used.
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Conference papers on the topic "Autodock"

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Kannan, Sarnath, and Raghavendra Ganji. "Porting Autodock to CUDA." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586277.

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Rakhshani, Hojjat, Lhassane Idoumghar, Julien Lepagnot, Mathieu Brevilliers, and Edward Keedwell. "Automatic hyperparameter selection in Autodock." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621172.

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Hisle, Mark S., Maxwell S. Meier, and David M. Toth. "Accelerating AutoDock Vina with Containerization." In PEARC '18: Practice and Experience in Advanced Research Computing. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3219104.3219154.

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Solis-Vasquez, Leonardo, Diogo Santos-Martins, Andreas Koch, and Stefano Forli. "Evaluating the Energy Efficiency of OpenCL-accelerated AutoDock Molecular Docking." In 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE, 2020. http://dx.doi.org/10.1109/pdp50117.2020.00031.

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Lang Yu, Zhongzhi Luan, Xiangzheng Sun, Zhe Wang, and Hailong Yang. "VinaSC: Scalable Autodock Vina with fine-grained scheduling on heterogeneous platform." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822624.

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Damayanti, Sophi, Andhika Bintang Mahardhika, Slamet Ibrahim, Wei Lim Chong, Vannajan Sanghiran Lee, and Daryono Hadi Tjahjono. "O-desmethylquinine as a cyclooxygenase-2 (COX-2) inhibitors using AutoDock Vina." In 3RD INTERNATIONAL CONFERENCE ON FUNDAMENTAL AND APPLIED SCIENCES (ICFAS 2014): Innovative Research in Applied Sciences for a Sustainable Future. AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4898452.

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Babu, M. Naresh, R. Bhramaramba, and Allam Appa Rao. "Structure based drug design studies on urokinase plasminogen activator inhibitors using AutoDock." In the Second International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2393216.2393301.

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Srinivasu, Nulaka, K. V. V. Satyanarayana, and N. Gopala Krishna Murthy. "Structure based drug design studies on CDK2 amino pyrazole inhibitors using Autodock Tools." In 2014 Conference on IT in Business, Industry and Government (CSIBIG). IEEE, 2014. http://dx.doi.org/10.1109/csibig.2014.7056965.

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Setiadi, Yanuar, Maula Eka Sriyani, Muhamad Basit Febrian, and Badra Sanditya Rattyananda. "Computational simulations of radiolabeled compounds on toxoplasma gondii TS-DHFR using Autodock 4." In INTERNATIONAL CONFERENCE ON SCIENCE AND APPLIED SCIENCE (ICSAS) 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0072409.

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"In silico investigation to select the most fitting mTOR inhibitors using AutoDock Vina Oral Granule." In 2nd Hawler Pharmaceutical Sciences Conference. Hawler Medical University, 2020. http://dx.doi.org/10.15218/hpsc.02.11.

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Reports on the topic "Autodock"

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CALS TEST NETWORK WRIGHT-PATTERSON AFB OH. Technical IGES Transfer Using: AUTODESK, Incorporated's Data, MIL-D-28000A (IGES). Quick Short Test Report. Fort Belvoir, VA: Defense Technical Information Center, May 1993. http://dx.doi.org/10.21236/ada313003.

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Nguyen, Ba Nghiep, and Jin Wang. PNNL Technical Support to The Implementation of EMTA and EMTA-NLA Models in Autodesk? Moldflow? Packages. Office of Scientific and Technical Information (OSTI), December 2012. http://dx.doi.org/10.2172/1060674.

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AUTODESK INC SAN RAFAEL CA. The US Army Corps of Engineers Roadmap for Life-Cycle Building Information Modeling (BIM). Supplement 1- BIM Implementation Guide for Military Construction (MILCON) Projects Using the Autodesk Platform. Fort Belvoir, VA: Defense Technical Information Center, November 2012. http://dx.doi.org/10.21236/ada576142.

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Modlo, Yevhenii O., Serhiy O. Semerikov, Pavlo P. Nechypurenko, Stanislav L. Bondarevskyi, Olena M. Bondarevska, and Stanislav T. Tolmachev. The use of mobile Internet devices in the formation of ICT component of bachelors in electromechanics competency in modeling of technical objects. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3264.

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Computer simulation of technical objects and processes is one of the components of the system of professional training of a modern electromechanics engineer. It has been established that despite the fact that mobile Internet devices (MID) are actively used by electrical engineers, the methods of using them in the process of bachelor in electromechanics training is considered only in some domestic scientific studies. The article highlights the components of the methods of using MID in the formation of the ICT component of the competence of the bachelor in electromechanics in modeling of technical objects, providing for students to acquire basic knowledge in the field of Computer Science and modern ICT and skills to use programming systems, math packages, subroutine libraries, and the like. For processing tabular data, it is proposed to use various freely distributed tools that do not significantly differ in functionality, such as Google Sheets, Microsoft Excel, for processing text data – QuickEdit Text Editor, Google Docs, Microsoft Word. For 3D-modeling and viewing the design and technological documentation, the proposed comprehensive use of Autodesk tools in the training process.
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Furman, Burford, Laxmi Ramasubramanian, Shannon McDonald, Ron Swenson, Jack Fogelquist, Yu Chiao, Alex Pape, and Mario Cruz. Solar-Powered Automated Transportation: Feasibility and Visualization. Mineta Transportation Institute, December 2021. http://dx.doi.org/10.31979/mti.2021.1948.

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A solar-powered automated transportation network (ATN) connecting the North and South campuses of San José State University with three passenger stations was designed, visualized, and analyzed in terms of its energy usage, carbon offset, and cost. The study’s methodology included the use of tools and software such as ArcGIS, SketchUp, Infraworks, Sketchup, Rhinoceros, and Autodesk 3DS Max. ATN vehicle energy usage was estimated using data from the university’s Park & Ride shuttle bus operation and by modeling with SUMOPy, the advanced simulation suite for the micro-traffic simulator SUMO. The energy study showed that an extensive solar photovoltaic (PV) canopy over the guideway and stations is sufficient for the network to run 24/7 in better-than-zero net-metered conditions—even if ridership were to increase 15% above that predicted from SJSU Park & Ride shuttle data. The resulting energy system has a PV-rated output of 6.2 MW, a battery system capacity of 9.8 MWh, and an estimated cost of $11.4 million USD. The solar ATN also produces 98% lower CO2 and PM2.5 emissions compared to the Park & Ride shuttle bus. A team of experts including urban planners, architects, and engineers designed and visualized the conceptual prototype, including a comprehensive video explaining the need for solar ATN and what a typical rider would experience while utilizing the system. This research demonstrates both benefits and challenges for solar-powered ATN, as well as its functionality within the urban built environment to serve diverse San José neighborhoods.
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Autodesk Buzzsaw®. Purdue University, 2012. http://dx.doi.org/10.5703/1288284315827.

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