Journal articles: 'Computational docking'

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Xing, Bo. "Computational Intelligence in Cross Docking." International Journal of Software Innovation 2, no. 1 (January 2014): 1–8. http://dx.doi.org/10.4018/ijsi.2014010101.

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Cross docking is a practice in logistics with the main operations of goods flow directly from receiving to the shipping docks without stopping or being put away into storage. It is a simple concept to talk about, but a challenging one to implement. So far, many different approaches have been followed in order to improve the efficiency of a cross docking system. However, as the complexity increases, the use of computational intelligence (CI) in those problems is becoming a unique tool of imperative value. In this paper, different CI methods, such as Tabu search, simulated annealing, genetic algorithm, and fuzzy logic. The key issues in implementing the proposed approaches are discussed, and finally the open questions are highlighted.

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Lengauer, Thomas, and Matthias Rarey. "Computational methods for biomolecular docking." Current Opinion in Structural Biology 6, no. 3 (June 1996): 402–6. http://dx.doi.org/10.1016/s0959-440x(96)80061-3.

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Khamis, Mohamed A., Walid Gomaa, and Walaa F. Ahmed. "Machine learning in computational docking." Artificial Intelligence in Medicine 63, no. 3 (March 2015): 135–52. http://dx.doi.org/10.1016/j.artmed.2015.02.002.

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Lee, Kyoungrim, and Joo-Woon Lee. "Computational Approaches to Protein-Protein Docking." Current Proteomics 5, no. 1 (April 1, 2008): 10–19. http://dx.doi.org/10.2174/157016408783955083.

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Hecht, David, and Gary Fogel. "Computational Intelligence Methods for Docking Scores." Current Computer Aided-Drug Design 5, no. 1 (March 1, 2009): 56–68. http://dx.doi.org/10.2174/157340909787580863.

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Al-hussaniy, Hany Akeel. "The development of molecular docking and molecular dynamics and their application in the field of chemistry and computer simulation." Journal of medical pharmaceutical and allied sciences 12, no. 1 (January 31, 2023): 5552–62. http://dx.doi.org/10.55522/jmpas.v12i1.4137.

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With the rapid development of modern life science, computational Molecular docking has gradually become one of the core disciplines and methods of modern life science research. Computational docking studies the relationship between the structure and pharmacodynamics of biological macromolecules and the interaction between biological macromolecules and ligands. It promotes the development of protein engineering, protein design, and computer-aided drug design with powerful and various docking software in predicting the three-dimensional structure and dynamic characteristics of proteins from protein sequences. Nowadays, this computing power can be provided by the GPU through the use of a general-purpose computing model on GPUs. This article presents two approaches to parallelizing the descriptive algorithms on the GPU to solve the molecular docking problem and then evaluating them in terms of the computation time achieved. The proposed approaches are effective in accelerating molecular docking on GPUs compared to a single-core or multicore CPU. Besides introducing parallelization approaches, we propose a new descriptive algorithm based on the bee swarm algorithm to solve the molecular docking problem as an alternative to traditional descriptive algorithms such as the genetic algorithm.

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Wang, Kai, Nan Lyu, Hongjuan Diao, Shujuan Jin, Tao Zeng, Yaoqi Zhou, and Ruibo Wu. "GM-DockZn: a geometry matching-based docking algorithm for zinc proteins." Bioinformatics 36, no. 13 (May 5, 2020): 4004–11. http://dx.doi.org/10.1093/bioinformatics/btaa292.

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Abstract Motivation Molecular docking is a widely used technique for large-scale virtual screening of the interactions between small-molecule ligands and their target proteins. However, docking methods often perform poorly for metalloproteins due to additional complexity from the three-way interactions among amino-acid residues, metal ions and ligands. This is a significant problem because zinc proteins alone comprise about 10% of all available protein structures in the protein databank. Here, we developed GM-DockZn that is dedicated for ligand docking to zinc proteins. Unlike the existing docking methods developed specifically for zinc proteins, GM-DockZn samples ligand conformations directly using a geometric grid around the ideal zinc-coordination positions of seven discovered coordination motifs, which were found from the survey of known zinc proteins complexed with a single ligand. Results GM-DockZn has the best performance in sampling near-native poses with correct coordination atoms and numbers within the top 50 and top 10 predictions when compared to several state-of-the-art techniques. This is true not only for a non-redundant dataset of zinc proteins but also for a homolog set of different ligand and zinc-coordination systems for the same zinc proteins. Similar superior performance of GM-DockZn for near-native-pose sampling was also observed for docking to apo-structures and cross-docking between different ligand complex structures of the same protein. The highest success rate for sampling nearest near-native poses within top 5 and top 1 was achieved by combining GM-DockZn for conformational sampling with GOLD for ranking. The proposed geometry-based sampling technique will be useful for ligand docking to other metalloproteins. Availability and implementation GM-DockZn is freely available at www.qmclab.com/ for academic users. Supplementary information Supplementary data are available at Bioinformatics online.

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Butt, Sania Safdar, Yasmin Badshah, Maria Shabbir, and Mehak Rafiq. "Molecular Docking Using Chimera and Autodock Vina Software for Nonbioinformaticians." JMIR Bioinformatics and Biotechnology 1, no. 1 (June 19, 2020): e14232. http://dx.doi.org/10.2196/14232.

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In the field of drug discovery, many methods of molecular modeling have been employed to study complex biological and chemical systems. Experimental strategies are integrated with computational approaches for the identification, characterization, and development of novel drugs and compounds. In modern drug designing, molecular docking is an approach that explores the confirmation of a ligand within the binding site of a macromolecule. To date, many software and tools for docking have been employed. AutoDock Vina (in UCSF [University of California, San Francisco] Chimera) is one of the computationally fastest and most accurate software employed in docking. In this paper, a sequential demonstration of molecular docking of the ligand fisetin with the target protein Akt has been provided, using AutoDock Vina in UCSF Chimera 1.12. The first step involves target protein ID retrieval from the protein database, the second step involves visualization of the protein structure in UCSF Chimera, the third step involves preparation of the target protein for docking, the fourth step involves preparation of the ligand for docking, the fifth step involves docking of the ligand and the target protein as Mol.2 files in Chimera by using AutoDock Vina, and the final step involves interpretation and analysis of the docking results. By following the guidelines and steps outlined in this paper, researchers with no previous background in bioinformatics research can perform computational docking in an easier and more user-friendly manner.

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Taufer, M., R. Armen, Jianhan Chen, P. Teller, and C. Brooks. "Computational multiscale modeling in protein--ligand docking." IEEE Engineering in Medicine and Biology Magazine 28, no. 2 (March 2009): 58–69. http://dx.doi.org/10.1109/memb.2009.931789.

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Baskaran, C., and M. Ramachandran. "Computational molecular docking studies on anticancer drugs." Asian Pacific Journal of Tropical Disease 2 (January 2012): S734—S738. http://dx.doi.org/10.1016/s2222-1808(12)60254-0.

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Goodsell, D. S. "Computational Docking of Biomolecular Complexes with AutoDock." Cold Spring Harbor Protocols 2009, no. 5 (May 1, 2009): pdb.prot5200. http://dx.doi.org/10.1101/pdb.prot5200.

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Lanez, Elhafnaoui, Lazhar Bechki, and Touhami Lanez. "Computational Molecular Docking, Voltammetric and Spectroscopic DNA Interaction Studies of 9N-(Ferrocenylmethyl)adenine." Chemistry & Chemical Technology 13, no. 1 (March 5, 2019): 11–17. http://dx.doi.org/10.23939/chcht13.01.011.

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Al-Madhagi, H. A., and M. G. Saleh. "Computational investigation of honeybee venom proteins as potential Omicron SARS-CoV-2 inhibitors." Ukrainian Biochemical Journal 94, no. 6 (February 23, 2023): 3–10. http://dx.doi.org/10.15407/ubj94.06.003.

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Because of the catastrophic consequences of COVID-19 on the world population, there should be novel interventions to handle ongoing infections and daily death cases. The aim of the current study is to examine the effectiveness of HBV (Honeybee venom) proteins on spike protein RBD by in silico tools. The sequences of 5 HBV proteins were used for homology modeling by Phyre 2. The generated protein models were employed for protein-protein docking against Omicron Spike glycoprotein receptor binding domain (RBD) (PDB ID# 7T9L) through HDock and ClusPro platforms followed by prediction of binding affinity using PRODIGY web portal and PDBsum for revealing interaction details. It was found that all of the examined HBV proteins exhibited strong docking scores and binding affinity profiles toward RBD. The findings of the present study indicate the possible HBV as preventive as well as treatment options against Omicron SARS-CoV-2. Keywords: COVID-19, docking, Honeybee venom, RBD, SARS-COV-2

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Cakici, Serdar, Selcuk Sumengen, Ugur Sezerman, and Selim Balcisoy. "DockPro: A VR-Based Tool for Protein-Protein Docking Problem." International Journal of Virtual Reality 8, no. 2 (January 1, 2009): 19–23. http://dx.doi.org/10.20870/ijvr.2009.8.2.2720.

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Proteins are large molecules that are vital for all living organisms and they are essential components of many industrial products. The process of binding a protein to another is called protein-protein docking. Many automated algorithms have been proposed to find docking configurations that might yield promising protein-protein complexes. However, these automated methods are likely to come up with false positives and have high computational costs. Consequently, Virtual Reality has been used to take advantage of user's experience on the problem; and proposed applications can be further improved. Haptic devices have been used for molecular docking problems; but they are inappropriate for protein-protein docking due to their workspace limitations. Instead of haptic rendering of forces, we provide a novel visual feedback for simulating physicochemical forces of proteins. We propose an interactive 3D application, DockPro, which enables domain experts to come up with dockings of protein-protein couples by using magnetic trackers and gloves in front of a large display.

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Jakhar, Ritu, Mehak Dangi, Alka Khichi, and Anil Kumar Chhillar. "Relevance of Molecular Docking Studies in Drug Designing." Current Bioinformatics 15, no. 4 (June 11, 2020): 270–78. http://dx.doi.org/10.2174/1574893615666191219094216.

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Molecular Docking is used to positioning the computer-generated 3D structure of small ligands into a receptor structure in a variety of orientations, conformations and positions. This method is useful in drug discovery and medicinal chemistry providing insights into molecular recognition. Docking has become an integral part of Computer-Aided Drug Design and Discovery (CADDD). Traditional docking methods suffer from limitations of semi-flexible or static treatment of targets and ligand. Over the last decade, advances in the field of computational, proteomics and genomics have also led to the development of different docking methods which incorporate protein-ligand flexibility and their different binding conformations. Receptor flexibility accounts for more accurate binding pose predictions and a more rational depiction of protein binding interactions with the ligand. Protein flexibility has been included by generating protein ensembles or by dynamic docking methods. Dynamic docking considers solvation, entropic effects and also fully explores the drug-receptor binding and recognition from both energetic and mechanistic point of view. Though in the fast-paced drug discovery program, dynamic docking is computationally expensive but is being progressively used for screening of large compound libraries to identify the potential drugs. In this review, a quick introduction is presented to the available docking methods and their application and limitations in drug discovery.

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Graff, David E., and Connor W. Coley. "pyscreener: A Python Wrapper for Computational Docking Software." Journal of Open Source Software 7, no. 71 (March 16, 2022): 3950. http://dx.doi.org/10.21105/joss.03950.

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Oda, Akifumi, Noriyuki Yamaotsu, Shuichi Hirono, Yurie Watanabe, Shuichi Fukuyoshi, and Ohgi Takahashi. "Effects of initial settings on computational protein–ligand docking accuracies for several docking programs." Molecular Simulation 41, no. 10-12 (May 27, 2014): 1027–34. http://dx.doi.org/10.1080/08927022.2014.917300.

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SAKK, ERIC. "ON THE COMPUTATION OF MOLECULAR SURFACE CORRELATIONS FOR PROTEIN DOCKING USING FOURIER TECHNIQUES." Journal of Bioinformatics and Computational Biology 05, no. 04 (August 2007): 915–35. http://dx.doi.org/10.1142/s0219720007002916.

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The computation of surface correlations using a variety of molecular models has been applied to the unbound protein docking problem. Because of the computational complexity involved in examining all possible molecular orientations, the fast Fourier transform (FFT) (a fast numerical implementation of the discrete Fourier transform (DFT)) is generally applied to minimize the number of calculations. This approach is rooted in the convolution theorem which allows one to inverse transform the product of two DFTs in order to perform the correlation calculation. However, such a DFT calculation results in a cyclic or "circular" correlation which, in general, does not lead to the same result as the linear correlation desired for the docking problem. In this work, we provide computational bounds for constructing molecular models used in the molecular surface correlation problem. The derived bounds are then shown to be consistent with various intuitive guidelines previously reported in the protein docking literature. Finally, these bounds are applied to different molecular models in order to investigate their effect on the correlation calculation.

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Choi, Jieun, and Juyong Lee. "V-Dock: Fast Generation of Novel Drug-like Molecules Using Machine-Learning-Based Docking Score and Molecular Optimization." International Journal of Molecular Sciences 22, no. 21 (October 27, 2021): 11635. http://dx.doi.org/10.3390/ijms222111635.

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We propose a computational workflow to design novel drug-like molecules by combining the global optimization of molecular properties and protein-ligand docking with machine learning. However, most existing methods depend heavily on experimental data, and many targets do not have sufficient data to train reliable activity prediction models. To overcome this limitation, protein-ligand docking calculations must be performed using the limited data available. Such docking calculations during molecular generation require considerable computational time, preventing extensive exploration of the chemical space. To address this problem, we trained a machine-learning-based model that predicted the docking energy using SMILES to accelerate the molecular generation process. Docking scores could be accurately predicted using only a SMILES string. We combined this docking score prediction model with the global molecular property optimization approach, MolFinder, to find novel molecules exhibiting the desired properties with high values of predicted docking scores. We named this design approach V-dock. Using V-dock, we efficiently generated many novel molecules with high docking scores for a target protein, a similarity to the reference molecule, and desirable drug-like and bespoke properties, such as QED. The predicted docking scores of the generated molecules were verified by correlating them with the actual docking scores.

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Monteiro, Alex France M., Jéssika De O. Viana, Anuraj Nayarisseri, Ernestine N. Zondegoumba, Francisco Jaime B. Mendonça Junior, Marcus Tullius Scotti, and Luciana Scotti. "Computational Studies Applied to Flavonoids against Alzheimer’s and Parkinson’s Diseases." Oxidative Medicine and Cellular Longevity 2018 (December 30, 2018): 1–21. http://dx.doi.org/10.1155/2018/7912765.

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Neurodegenerative diseases, such as Parkinson’s and Alzheimer’s, are understood as occurring through genetic, cellular, and multifactor pathophysiological mechanisms. Several natural products such as flavonoids have been reported in the literature for having the capacity to cross the blood-brain barrier and slow the progression of such diseases. The present article reports on in silico enzymatic target studies and natural products as inhibitors for the treatment of Parkinson’s and Alzheimer’s diseases. In this study we evaluated 39 flavonoids using prediction of molecular properties and in silico docking studies, while comparing against 7 standard reference compounds: 4 for Parkinson’s and 3 for Alzheimer’s. Osiris analysis revealed that most of the flavonoids presented no toxicity and good absorption parameters. The Parkinson’s docking results using selected flavonoids as compared to the standards with four proteins revealed similar binding energies, indicating that the compounds 8-prenylnaringenin, europinidin, epicatechin gallate, homoeriodictyol, capensinidin, and rosinidin are potential leads with the necessary pharmacological and structural properties to be drug candidates. The Alzheimer’s docking results suggested that seven of the 39 flavonoids studied, being those with the best molecular docking results, presenting no toxicity risks, and having good absorption rates (8-prenylnaringenin, europinidin, epicatechin gallate, homoeriodictyol, aspalathin, butin, and norartocarpetin) for the targets analyzed, are the flavonoids which possess the most adequate pharmacological profiles.

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Salih, Twana Mohsin. "A Comparative Study for the Accuracy of Three Molecular Docking Programs Using HIV-1 Protease Inhibitors as a Model." Iraqi Journal of Pharmaceutical Sciences ( P-ISSN 1683 - 3597 E-ISSN 2521 - 3512) 31, no. 2 (December 24, 2022): 160–68. http://dx.doi.org/10.31351/vol31iss2pp160-168.

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Flexible molecular docking is a computational method of structure-based drug design to evaluate binding interactions between receptor and ligand and identify the ligand conformation within the receptor pocket. Currently, various molecular docking programs are extensively applied; therefore, realizing accuracy and performance of the various docking programs could have a significant value. In this comparative study, the performance and accuracy of three widely used non-commercial docking software (AutoDock Vina, 1-Click Docking, and UCSF DOCK) was evaluated through investigations of the predicted binding affinity and binding conformation of the same set of small molecules (HIV-1 protease inhibitors) and a protein target HIV-1 protease enzyme. The tested sets are composed of eight receptor-ligand complexes with high resolution crystal structures downloaded from Protein Data Bank website. Molecular dockings were applied between approved HIV-1 protease inhibitors and the HIV-1 protease using AutoDock Vina, 1-Click Docking, and DOCK6. Then, docking poses of the top-ranked solution was realized using UCSF Chimera. Furthermore, Pearson correlation coefficient (r) and coefficient of determination (r2) between the experimental results and the top scored docking results of each program were calculated using Graphpad prism V9.2. After comparing saquinavir top scored binding poses of each docking program with the crystal structure, various conformational changes were observed. Moreover, according to the relative comparison between the top ranked calculated ?Gbinding values against the experimental results, r2 value of AutoDock Vina, 1-Click Docking, and DOCK6 were 0.65, 0.41, and 0.005, respectively. The outcome of this study shows that the top scored binding free energy could not produce the best pose prediction. In addition, AutoDock Vina results have the highest correlation with the experimental results.

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Apostolakist, J., and A. Caflisch. "Computational Ligand Design." Combinatorial Chemistry & High Throughput Screening 2, no. 2 (April 1999): 91–104. http://dx.doi.org/10.2174/1386207302666220203193501.

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Abstract: A variety of computational tools that are used to assist drug design are reviewed. Particular emphasis is given to the limitations and merits of different methodologies. Recently, a number of general methods have been proposed for clustering compounds in classes of drug like and non-drug-like molecules. The usefulness of this classification for drug design is discussed. The estimation of (relative) binding affinities is from a theoretical point of view the most challenging part of ligand design. We review three methods for the estimation of binding energies. Firstly, quantitative structure-activity relationships (QSAR) are presented. These have gained significantly from recent developments of experimental techniques for combinatorial synthesis and high-throughput screening as well as the use of powerful computational procedures like genetic algorithms and neural networks for the derivation of models. Secondly, empirical energy functions are shown to lead to more general models than standard QSAR, since they are fitted to a variety of complexes. They have been used recently with considerable success. Thirdly, we briefly outline free energy calculations based on molecular dynamics simulations, the method with the most sound theoretical foundation. Recent developments are reestablishing the interest in this approach. In the last part of this review structure-based ligand design programs are described. These are closely related to docking, with the difference that in design, unlike in most docking procedures, ligands are built on a fragment-by-fragment basis. Finally, a short description of our approach to computational combinatorial ligand design is given.

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Batool, Majda, Affifa Tajammal, Firdous Farhat, Francis Verpoort, Zafar Khattak, Mehr-un-Nisa, Muhammad Shahid, et al. "Molecular Docking, Computational, and Antithrombotic Studies of Novel 1,3,4-Oxadiazole Derivatives." International Journal of Molecular Sciences 19, no. 11 (November 15, 2018): 3606. http://dx.doi.org/10.3390/ijms19113606.

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A new series of 1,3,4-oxadiazoles derivatives was synthesized, characterized, and evaluated for their in vitro and in vivo anti-thrombotic activity. Compounds (3a–3i) exhibited significant clot lysis with respect to reference drug streptokinase (30,000 IU), and enhanced clotting time (CT) values (130–342 s) than heparin (110 s). High affinity towards 1NFY with greater docking score was observed for the compounds (3a, 3i, 3e, 3d, and 3h) than the control ligand RPR200095. In addition, impressive inhibitory potential against factor Xa (F-Xa) was observed with higher docking scores (5612–6270) with Atomic Contact Energy (ACE) values (−189.68 to −352.28 kcal/mol) than the control ligand RPR200095 (Docking score 5192; ACE −197.81 kcal/mol). In vitro, in vivo, and in silico results proposed that these newly synthesized compounds might be used as anticoagulant agents.

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Ouma, Stephen, Richard Kagia, and Faith Kamakia. "Determination of pharmacological activity of bioactives in Allium sativum using computational analysis." F1000Research 12 (February 9, 2023): 151. http://dx.doi.org/10.12688/f1000research.130105.1.

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Introduction: Use of natural products for management of diseases has increased widely due to the belief that natural products are less toxic than conventional medicines. Natural products have been utilised for management of chronic diseases such as diabetes and cancers. Respiratory infections have also been managed using natural products. Allium sativum is one of the natural products that has been utilised in the management of SARS-CoV infections, diabetes and cancer. Methods: This study was aimed at screening bioactive agents in Allium sativum using computational analysis. The targets of the bioactive agents were predicted using SwissTargetPrediction tools. Molecular docking followed, where the docking energies of the bioactive agents to the targets were generated. The bioactive agents were analysed for pharmacokinetics properties using SwissADME as well as toxicity profiles using the ProTox II webserver. The docking scores, toxicities and pharmacokinetics profiles of the bioactive agents in Allium sativum were compared with those of reference compounds. Results: All the bioactives showed lower docking scores than the reference compounds. The bioactives, however, showed some activity on specific receptors such as carbonic anhydrases, cyclooxygenase and ghrelin. All the bioactives showed high gastrointestinal tract absorption and none violated Lipinski’s rule of five. Diallyl trisulphide was predicted to be most lethal, with an LD50 of 100mg/kg, while was the safest, with 8000mg/kg. Conclusions: In conclusion, bioactives showed lower docking scores than the reference compounds, therefore overall pharmacological activity could be attributed to synergy between the bioactives for a particular receptor.

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Tivon, Barr, Ronen Gabizon, Bente A. Somsen, Peter J. Cossar, Christian Ottmann, and Nir London. "Covalent flexible peptide docking in Rosetta." Chemical Science 12, no. 32 (2021): 10836–47. http://dx.doi.org/10.1039/d1sc02322e.

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We developed Rosetta CovPepDock, a computational pipeline for covalent peptide docking. We showed it is highly accurate in retrospective benchmarks, and applied it prospectively to design potent and selective covalent binders of 14-3-3σ.

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Marinho, Márcia M., Francisco Wagner Q. Almeida-Neto, Emanuelle M. Marinho, Leonardo P. da Silva, Ramon R. P. P. B. Menezes, Ricardo P. dos Santos, Emmanuel S. Marinho, Pedro de Lima-Neto, and Alice M. C. Martins. "Quantum computational investigations and molecular docking studies on amentoflavone." Heliyon 7, no. 1 (January 2021): e06079. http://dx.doi.org/10.1016/j.heliyon.2021.e06079.

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Gioia, Dario, Martina Bertazzo, Maurizio Recanatini, Matteo Masetti, and Andrea Cavalli. "Dynamic Docking: A Paradigm Shift in Computational Drug Discovery." Molecules 22, no. 11 (November 22, 2017): 2029. http://dx.doi.org/10.3390/molecules22112029.

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Tajammal, Affifa, Aysha Siddiqa, Ahmad Irfan, Muhammad Azam, Huma Hafeez, Munawar Ali Munawar, and Muhammad Asim Raza Basra. "Antioxidant, molecular docking and computational investigation of new flavonoids." Journal of Molecular Structure 1254 (April 2022): 132189. http://dx.doi.org/10.1016/j.molstruc.2021.132189.

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May, Andreas, Florian Sieker, and Martin Zacharias. "How to Efficiently Include Receptor Flexibility During Computational Docking." Current Computer Aided-Drug Design 4, no. 2 (June 1, 2008): 143–53. http://dx.doi.org/10.2174/157340908784533265.

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Esquivel-Rodriguez, Juan, and Daisuke Kihara. "Computational Methods for Multiple Protein Docking for Asymmetric Complexes." Biophysical Journal 102, no. 3 (January 2012): 260a. http://dx.doi.org/10.1016/j.bpj.2011.11.1431.

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Burton, Richard M. "Computational Laboratories for Organization Science: Questions, Validity and Docking." Computational & Mathematical Organization Theory 9, no. 2 (July 2003): 91–108. http://dx.doi.org/10.1023/b:cmot.0000022750.46976.3c.

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Roberts, Victoria A., Michael E. Pique, Lynn F. Ten Eyck, and Sheng Li. "Predicting protein-DNA interactions by full search computational docking." Proteins: Structure, Function, and Bioinformatics 81, no. 12 (October 18, 2013): 2106–18. http://dx.doi.org/10.1002/prot.24395.

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Jain, Ajay N. "Bias, reporting, and sharing: computational evaluations of docking methods." Journal of Computer-Aided Molecular Design 22, no. 3-4 (December 13, 2007): 201–12. http://dx.doi.org/10.1007/s10822-007-9151-x.

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Yuriev, Elizabeth, Mark Agostino, and Paul A. Ramsland. "Challenges and advances in computational docking: 2009 in review." Journal of Molecular Recognition 24, no. 2 (October 23, 2010): 149–64. http://dx.doi.org/10.1002/jmr.1077.

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CRISTINO, MARIA DA GLÓRIA G., CARLA CAROLINA F. DE MENESES, MALÚCIA MARQUES SOEIRO, JOÃO ELIAS V. FERREIRA, ANTONIO FLORÊNCIO DE FIGUEIREDO, JARDEL PINTO BARBOSA, RUTH C. O. DE ALMEIDA, JOSÉ C. PINHEIRO, and ANDRÉIA DE LOURDES R. PINHEIRO. "COMPUTATIONAL MODELING OF ANTIMALARIAL 10-SUBSTITUTED DEOXOARTEMISININS." Journal of Theoretical and Computational Chemistry 11, no. 02 (April 2012): 241–63. http://dx.doi.org/10.1142/s0219633612500162.

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Nineteen 10-substitued deoxoartemisinin derivatives and artemisinin with activity against D-6 strains of malarial falciparum designated as Sierra Leone are studied. We use molecular electrostatic potential maps in an attempt to identify key structural features of the artemisinins that are necessary for their activities and molecular docking to investigate the interaction with the molecular receptor (heme). Chemometric modeling: Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), K-Nearest Neighbor (KNN), Soft Independent Modeling of Class Analogy (SIMCA) and Stepwise Discriminant Analysis (SDA) are employed to reduce dimensionality and investigate which subset of descriptors are responsible for the classification between more active (MA) and less active (LA) artemisinins. The PCA, HCA, KNN, SIMCA and SDA studies showed that the descriptors LUMO (Lowest Unoccupied Molecular Orbital) energy, DFeO1 (Distance between the O 1 atom from ligand and iron atom from heme), X1A (Average Connectivity Index Chi-1) and Mor15u (Molecular Representation of Structure Based on Electron Diffraction) code of signal 15, unweighted, are responsible for separating the artemisinins according to their degree of antimalarial activity. The prediction study was done with a new set of eight artemisinins by using the chemometric methods and five of them were predicted as active against D-6 strains of falciparum malaria. In order to verify if the key structural features that are necessary for their antimalarial activities were investigated for the interaction with the heme, we also carried out calculations of the molecular electrostatic potential (MEP) and molecular docking. MEP maps and molecular docking were analyzed for more active compounds of the prediction set.

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Raval, Keval, and Tejas Ganatra. "Basics, types and applications of molecular docking: A review." IP International Journal of Comprehensive and Advanced Pharmacology 7, no. 1 (March 15, 2022): 12–16. http://dx.doi.org/10.18231/j.ijcaap.2022.003.

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From hit discovery through lead optimization and beyond, computational methods have become an essential part of many drugs development processes. There are typically several steps in the docking process, and each one provides a new level of complexity. Docking methods are used to place small molecules in the active region of the enzyme. In addition to these methods, scoring functions are used to estimate a compound's biological activity by looking at how it interacts with prospective targets. Molecular docking is considered to be the most widely utilized computational phenomenon in the field of computer-aided drug design (CADD). It is being utilized at the academic level as well as in pharmaceutical companies for the lead discovery process. Molecular docking is mainly associated with two terms: ligand and protein. Protein is the target site where ligand may bind to give specific activity. Molecular docking provides information on the ability of the ligand to bind with protein which is known as binding affinity. Applications of molecular docking in drug development have evolved significantly since it was first created to aid in the study of molecular recognition processes between small and large compounds. This review emphasizes the basic features of molecular docking along with the types, approaches and applications.

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Singh, Niraj Kumar, Somdutt Mujwar, and Debapriya Garabadu. "in silico Anti-Cholinestarase Activity of Flavonoids: A Computational Approach." Asian Journal of Chemistry 31, no. 12 (November 16, 2019): 2859–64. http://dx.doi.org/10.14233/ajchem.2019.22153.

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In the present study, a computational approach has been designed to evaluate the potential anti-cholinesterase activity of derivatives of flavonoids. Molecular docking studies is performed for the 9 flavonoids against the human acetylcholine (ACh) enzyme to evaluate their binding affinity for having anti-alzheimer activity. All the 9 flavonoid compounds exhibited strong binding affinity that promises potent inhibition of human acetylcholine enzyme. Potential binding affinity of all the flavonoids against human acetylcholine enzyme confirms their possible mechanism of action by using AutoDock based molecular docking simulation technique. Thus, these flavonoid compounds could be presumed to be potential anti-cholinesterase drugs.

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Teo, Ruijie D., Sijia S. Dong, Zeev Gross, Harry B. Gray, and William A. Goddard. "Computational predictions of corroles as a class of Hsp90 inhibitors." Molecular BioSystems 11, no. 11 (2015): 2907–14. http://dx.doi.org/10.1039/c5mb00352k.

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Catalani, Valeria, Michelle Botha, John Martin Corkery, Amira Guirguis, Alessandro Vento, Norbert Scherbaum, and Fabrizio Schifano. "The Psychonauts’ Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity." Pharmaceuticals 14, no. 8 (July 26, 2021): 720. http://dx.doi.org/10.3390/ph14080720.

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Designer benzodiazepines (DBZDs) represent a serious health concern and are increasingly reported in polydrug consumption-related fatalities. When new DBZDs are identified, very limited information is available on their pharmacodynamics. Here, computational models (i.e., quantitative structure-activity relationship/QSAR and Molecular Docking) were used to analyse DBZDs identified online by an automated web crawler (NPSfinder®) and to predict their possible activity/affinity on the gamma-aminobutyric acid A receptors (GABA-ARs). The computational software MOE was used to calculate 2D QSAR models, perform docking studies on crystallised GABA-A receptors (6HUO, 6HUP) and generate pharmacophore queries from the docking conformational results. 101 DBZDs were identified online by NPSfinder®. The validated QSAR model predicted high biological activity values for 41% of these DBDZs. These predictions were supported by the docking studies (good binding affinity) and the pharmacophore modelling confirmed the importance of the presence and location of hydrophobic and polar functions identified by QSAR. This study confirms once again the importance of web-based analysis in the assessment of drug scenarios (DBZDs), and how computational models could be used to acquire fast and reliable information on biological activity for index novel DBZDs, as preliminary data for further investigations.

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Jaiyeoba, Oluwaseyi, Azman Samsudin, and Suhaila Sulaiman. "Utilising the Computational Power of Blockchain Proof-ofWork in Computer-Aided Drug Design." International Journal of Emerging Technology and Advanced Engineering 12, no. 10 (October 1, 2022): 37–50. http://dx.doi.org/10.46338/ijetae1022_05.

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Proof-of-Work (PoW) algorithm is a popular blockchain algorithm employed in many blockchain applications such as Bitcoin. Cryptographic hashing is the foundation of the PoW algorithm and blockchain technology in general. Unfortunately, the use of hashing in PoW has led to huge computational requirements. Researchers and industrialists are aware of the immense energy consumed by the PoW algorithm in blockchain-based cryptocurrencies. For instance, Bitcoin currently consumes above 110 TWh of electricity annually. This vast amount of energy is used to calculate non-valuable cryptographic hashes, which eventually becomes a waste when the right nonce value is found. Bioinformatic researchers depend on molecular docking simulation, which is effective but requires heavy computing resources. This paper proposes a solution to the above issues by taking advantage of the wasted computing power harnessed by the PoW algorithm and subsequently channelling the computing resources towards molecular docking simulations for drug discovery. With the new proposed framework, molecular docking is introduced into PoW algorithm where energy from a PoW system like bitcoin can be used to help researchers crunch bioinformatics data for computer aided drug discovery. A target protein receptor and ligands are introduced at the beginning of a new mining period, rather than computing random computer bits to discover a nonce, mining nodes will perform docking simulations with the receptor and ligands to generate docked conformations for drug creation research. This research does not seek to reduce the computational energy of the PoW rather utilise this energy for valuable drug creation process. Keywords— Blockchain, Bitcoin, PoW, Ligand, Molecular docking, RMSD, Score

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Pandey, Anwesh, Rolly Yadav, Anamika Shukla, and Anil Kumar Yadav. "Unveiling the Antimicrobial Activities of Dicationic Carbazoles and Related Analogs Through Computational Docking." Advanced Science, Engineering and Medicine 12, no. 1 (January 1, 2020): 40–44. http://dx.doi.org/10.1166/asem.2020.2513.

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Computational docking is a globally used tool now-a-days in bioinformatics. All the drugs/ligands generate their effect only when they interact/bind with the target molecule, here DNA. The potential drugs/ligands can only be identified by the study of their relative binding energies and preferential binding modes. Due to availability of huge numbers of such drugs/ligands; the evaluation of their relative potency is a challenging task. In the present work, carbazoles and its derivatives were studied for their DNA binding abilities using computational molecular docking. All the docked ligands had planar structures which allowed them to adopt crescent shape and thus minor groove binding to DNA was preferred by most of them. Computational docking revealed that DNA binding energies of carbazoles and its analogs followed the same trend as their thermal melting values. Also the drugs/ligands preferred themselves to bind at AT-rich regions of the minor groove of the selected DNA sequences.

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Patel, Jimish R., Hirak V. Joshi, Ujashkumar A. Shah, and Jayvadan K. Patel. "A Review on Computational Software Tools for Drug Design and Discovery." Indo Global Journal of Pharmaceutical Sciences 12 (2022): 53–81. http://dx.doi.org/10.35652/igjps.2022.12006.

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In the current era of modern drug design & development via computer-aided drug design, the potential role of computational software tools is widely enlarged in use. Computer-based drug design is revolutionary in the new drug discovery process because these processes are fast, time, and cost-saving with more efficient pharmacological activity. Computer-Based drug design is mainly applied for the drug-design and gets many successes in new drug research. There is plenty of software available in drug design; however; still, many issues are rising during its use. To clarify these issues, an attempt has been provided here in this article about the information about worldwide used 189 computation tools along with citation of software tools, download links, computer operative system and application of tools for available software such as Molecular modeling, docking, proteins conformation, pharmacophore mapping, ADMET, Docking pose visualization, force field calculation, homology modeling, 3D structure generator, Computational Crystallography, protein Database, and calculation software. This vital information enlightens all the software right from old to a recent one. Review article important for choice and application of wide-reaching used Drug Design software.©2022iGlobal Research and PublishingFoundation. All rights reserved.

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Naqvi, Ahmad Abu Turab, Taj Mohammad, Gulam Mustafa Hasan, and Md Imtaiyaz Hassan. "Advancements in Docking and Molecular Dynamics Simulations Towards Ligand-receptor Interactions and Structure-function Relationships." Current Topics in Medicinal Chemistry 18, no. 20 (December 31, 2018): 1755–68. http://dx.doi.org/10.2174/1568026618666181025114157.

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Protein-ligand interaction is an imperative subject in structure-based drug design and protein function prediction process. Molecular docking is a computational method which predicts the binding of a ligand molecule to the particular receptor. It predicts the binding pose, strength and binding affinity of the molecules using various scoring functions. Molecular docking and molecular dynamics simulations are widely used in combination to predict the binding modes, binding affinities and stability of different protein-ligand systems. With advancements in algorithms and computational power, molecular dynamics simulation is now a fundamental tool to investigative bio-molecular assemblies at atomic level. These methods in association with experimental support have been of great value in modern drug discovery and development. Nowadays, it has become an increasingly significant method in drug discovery process. In this review, we focus on protein-ligand interactions using molecular docking, virtual screening and molecular dynamics simulations. Here, we cover an overview of the available methods for molecular docking and molecular dynamics simulations, and their advancement and applications in the area of modern drug discovery. The available docking software and their advancement including application examples of different approaches for drug discovery are also discussed. We have also introduced the physicochemical foundations of molecular docking and simulations, mainly from the perception of bio-molecular interactions.

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Khodade, Prashant, R. Prabhu, Nagasuma Chandra, Soumyendu Raha, and R. Govindarajan. "Parallel implementation ofAutoDock." Journal of Applied Crystallography 40, no. 3 (May 15, 2007): 598–99. http://dx.doi.org/10.1107/s0021889807011053.

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Computational docking of ligands to protein structures is a key step in structure-based drug design. Currently, the time required for each docking run is high and thus limits the use of docking in a high-throughput manner, warranting parallelization of docking algorithms.AutoDock, a widely used tool, has been chosen for parallelization. Near-linear increases in speed were observed with 96 processors, reducing the time required for docking ligands to HIV-protease from 81 min, as an example, on a single IBM Power-5 processor (1.65 GHz), to about 1 min on an IBM cluster, with 96 such processors. This implementation would make it feasible to perform virtual ligand screening usingAutoDock.

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AlTarabeen, Mousa, Qosay Al-Balas, Amgad Albohy, Werner Ernst Georg Müller, and Peter Proksch. "Marine-Based Candidates as Potential RSK1 Inhibitors: A Computational Study." Molecules 28, no. 1 (December 26, 2022): 202. http://dx.doi.org/10.3390/molecules28010202.

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Manzamines are chemically related compounds extracted from the methanolic extract of Acanthostrongylophora ingens species. Seven compounds were identified by our research group and are being characterized. As their biological target is unknown, this work is based on previous screening work performed by Mayer et al., who revealed that manzamine A could be an inhibitor of RSK1 kinase. Within this work, the RSK1 N-terminal kinase domain is exploited as a target for our work and the seven compounds are docked using Autodock Vina software. The results show that one of the most active compounds, Manzamine A N-oxide (5), with an IC50 = 3.1 μM, displayed the highest docking score. In addition, the compounds with docking scores lower than the co-crystalized ligand AMP-PCP (−7.5 and −8.0 kcal/mol) for ircinial E (1) and nakadomarin A (7) were found to be inferior in activity in the biological assay. The docking results successfully managed to predict the activities of four compounds, and their in silico results were in concordance with their biological data. The β-carboline ring showed noticeable receptor binding, which could explain its reported biological activities, while the lipophilic side of the compound was found to fit well inside the hydrophobic active site.

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Kotthoff, Ian, Petras J. Kundrotas, and Ilya A. Vakser. "DOCKGROUND membrane protein-protein set." PLOS ONE 17, no. 5 (May 17, 2022): e0267531. http://dx.doi.org/10.1371/journal.pone.0267531.

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Membrane proteins are significantly underrepresented in Protein Data Bank despite their essential role in cellular mechanisms and the major progress in experimental protein structure determination. Thus, computational approaches are especially valuable in the case of membrane proteins and their assemblies. The main focus in developing structure prediction techniques has been on soluble proteins, in part due to much greater availability of the structural data. Currently, structure prediction of protein complexes (protein docking) is a well-developed field of study. However, the generic protein docking approaches are not optimal for the membrane proteins because of the differences in physicochemical environment and the spatial constraints imposed by the membranes. Thus, docking of the membrane proteins requires specialized computational methods. Development and benchmarking of the membrane protein docking approaches has to be based on high-quality sets of membrane protein complexes. In this study we present a new dataset of 456 non-redundant alpha helical binary interfaces. The set is significantly larger and more representative than the previously developed sets. In the future, it will become the basis for the development of docking and scoring benchmarks, similar to the ones for soluble proteins in the Dockground resource http://dockground.compbio.ku.edu.

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Ozheredov, S. P., O. M. Demchuk, P. A. Karpov, S. I. Spivak, and Ya B. Blume. "Identification of plant α-tubulin amino acids playing a key role in specific binding of nitroaniline compounds." Faktori eksperimental'noi evolucii organizmiv 24 (August 30, 2019): 333–37. http://dx.doi.org/10.7124/feeo.v24.1125.

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Aim. Computational prediction of amino acid residues critical for specific binding of nitro- and dinitroaniline compounds in plant α-tubulin. Methods. Protein structure modeling (I-Tasser, Grid-computing) and ligand library preparation, molecular docking (CCDC Gold), molecular dynamics (MD, Gromacs computing in Grid). Evaluation of the amino acid ensemble associated with ligand binding based on results of MD energy perturbations of protein-ligand complex. Results. The structural model of plant α-tubulin from Avena sativa was build. Also, the virtual library of 25 nitroaniline compounds was prepared. The docking of ligands into the interdimer contact of α-tubulin and MD simulations of the leading complexes reveal differences in ligands conformational energy during the exchange between free and binding states. The mean number of hydrogen bonds and dynamics of their formation in complex were compared. These computations allow us to select a.a. residues playing key role in specific interaction with nitro- and dinitroaniline compounds in plant α-tubulin. Conclusions. Computational prediction specify 28 a.a. residues playing the main role in binding of nitro- and dinitroaniline compounds with plant α-tubulin from Avena sativa: Arg2, Glu3, Ile4, Cys129, Thr130, Gly131, Leu132, Gln133, Gly134, Gly162, Lys163, Lys164, Ser165, Leu242, Arg243, Asp245, Gly246, Ala247, Ile248, Asn249, Val250, Asp251,Val252, Thr253, Glu254, Phe255, Thr257, Asn258. Keywords: plant, α-tubulin, nitroaniline compounds, molecular docking, molecular dynamics, ligand binding.

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Shimki, Afia Ibnath, Anisur Rahman, Md Helal Uddin Chowdhury, Md Nazim Uddin Chy, and Md Adnan. "In silico molecular docking study of phytochemicals obtained from Holigarna caustica (Dennst.) for cancer treatment." Journal of Phytomolecules and Pharmacology 1, no. 1 (2022): 47–55. http://dx.doi.org/10.56717/jpp.2022.v01i01.006.

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Holigarna caustica, commonly known in Bangladesh as "Katebel," is used in traditional medicine to treat tumors, malignancies, skin problems, obesity, inflammation, eye irritation, and arthritis. The goal of this work was to use computational models like molecular docking to find the bioactive phytochemicals in this plant that are responsible for the anticancer potential. The molecular docking experiment was carried out using Glide of Schrödinger Maestro (version 10.1). Our computational research revealed that a total of eighteen phytocompounds may be responsible for the plant's anticancer properties, which should be further studied in experimental models.

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De Paris, Renata, Christian V. Quevedo, Duncan D. Ruiz, Osmar Norberto de Souza, and Rodrigo C. Barros. "Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments." Computational Intelligence and Neuroscience 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/916240.

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Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for thek-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.

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Sharov, Artem V., Tatyana M. Burkhanova, Tugba Taskın Tok, Maria G. Babashkina, and Damir A. Safin. "Computational Analysis of Molnupiravir." International Journal of Molecular Sciences 23, no. 3 (January 28, 2022): 1508. http://dx.doi.org/10.3390/ijms23031508.

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In this work, we report in-depth computational studies of three plausible tautomeric forms, generated through the migration of two acidic protons of the N4-hydroxylcytosine fragment, of molnupiravir, which is emerging as an efficient drug to treat COVID-19. The DFT calculations were performed to verify the structure of these tautomers, as well as their electronic and optical properties. Molecular docking was applied to examine the influence of the structures of the keto-oxime, keto-hydroxylamine and hydroxyl-oxime tautomers on a series of the SARS-CoV-2 proteins. These tautomers exhibited the best affinity behavior (−9.90, −7.90, and −9.30 kcal/mol, respectively) towards RdRp-RTR and Nonstructural protein 3 (nsp3_range 207–379-MES).

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Journal articles on the topic 'Computational docking'

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