Design and Implementation of Parallel Lamarckian Genetic Algorithm for Automated Docking of Molecules

Wang, Li; Weng, Zi-jun; Liang, Yi; Wang, Yong; Zhang, Zheng; Di, Ruihua

doi:10.1109/hpcc.2008.50

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…The docking parameters used in this step were the Lamarckian genetic algorithm [48] with default docking parameter; binding site coordinates x = -9.732, y = 11.403, and z = 68.925; and grid box size 40 × 56 × 40. Autodock uses a Lamarckian genetic algorithm (LGA), which introduces a local search based on the traditional genetic algorithm, making it more efficient to determine the optimal docking [49]. With these parameters, we obtained the root-mean-square deviation (RMSD) value of the native ligand as < 2 Å [50] and then applied these parameters to other ligands.…”

Section: Molecular Dockingmentioning

confidence: 99%

Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches

Erlina

Paramita

Kusuma

et al. 2022

BMC Complement Med Ther

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Background The number of COVID-19 cases continues to grow in Indonesia. This phenomenon motivates researchers to find alternative drugs that function for prevention or treatment. Due to the rich biodiversity of Indonesian medicinal plants, one alternative is to examine the potential of herbal medicines to support COVID therapy. This study aims to identify potential compound candidates in Indonesian herbal using a machine learning and pharmacophore modeling approaches. Methods We used three classification methods that had different decision-making processes: support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF). For the pharmacophore modeling approach, we performed a structure-based analysis on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and repurposed SARS, MERS, and SARS-CoV-2 drugs identified from the literature as datasets in the ligand-based method. Lastly, we used molecular docking to analyze the interactions between the 3CLpro and 14 hit compounds from the Indonesian Herbal Database (HerbalDB), with lopinavir as a positive control. Results From the molecular docking analysis, we found six potential compounds that may act as the main proteases of the SARS-CoV-2 inhibitor: hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside. Conclusions Our layered virtual screening with machine learning and pharmacophore modeling approaches provided a more objective and optimal virtual screening and avoided subjective decision making of the results. Herbal compounds from the screening, i.e. hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside are potential antiviral candidates for SARS-CoV-2. Moringa oleifera and Psidium guajava that consist of those compounds, could be an alternative option as COVID-19 herbal preventions.

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Section: Molecular Dockingmentioning

confidence: 99%

Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches

Erlina

Paramita

Kusuma

et al. 2022

BMC Complement Med Ther

View full text Add to dashboard Cite

show abstract

“…In the molecular docking step, we used hit compounds candidates yielded by two approaches, machine learning and pharmacophore modelling, and used macromolecules of SARS-CoV-2 main protease (PDB ID: 6LU7 LGA), which introduces local search based on the traditional genetic algorithm, making it more efficient in figuring out the optimal docking [42]. With these parameters, we got the RMSD value of native ligand as < 2 Å [43] and then applied these parameters for other ligands.…”

Section: Molecular Dockingmentioning

confidence: 99%

Virtual Screening on Indonesian Herbal Compounds as COVID-19 Supportive Therapy: Machine Learning and Pharmacophore Modelling Approaches

Erlina

Paramita

Kusuma

et al. 2020

Preprint

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Background: The latest development of COVID-19 spread in Indonesia has reached 311,176 cases, with 11,374 patients died, updated on October 6, 2020. Unfortunately, these numbers continue to overgrow, and no drug has yet been approved for effective treatment. This study aims to determine the potential candidate compounds in Indonesian herbal medicine as a COVID-19 supportive therapy using a machine learning and pharmacophore modelling approach.Methods: For the machine learning approach, we used three classification methods that have different ways in decision making, such as Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Random Forest (RF). Moreover, for the pharmacophore modelling approach, we performed a structure-based method on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and used the SARS, MERS, and SARS-CoV-2 repurposing drugs known from the literature as data sets on the ligand-based method. Finally, we used molecular docking to analyse the interactions between the 3CLpro protein (main protease) and 14 hit compounds from the Indonesian Herbal Database (HerbalDB) and Lopinavir as a positive control.Results: The machine learning approach with SVM, RF, and MLP methods and pharmacophore modelling approach were used for screening in herbal compounds obtained from HerbalDB. Based on the screening on HerbalDB using these two prediction approaches, we got 14 hit compounds. We then performed molecular docking to determine the interaction of these compounds with the main protease SARS-CoV-2 as an inhibiting agent. From the molecular docking analysis, it was found that six potential compounds as the main proteases of the SARS-CoV-2 inhibitor, i.e. Hesperidin, Kaempferol-3,4'-di-O-methyl ether (Ermanin); Myricetin-3-glucoside, Peonidine 3-(4’-arabinosylglucoside); Quercetin 3-(2G-rhamnosylrutinoside); and Rhamnetin 3-mannosyl-(1-2)-alloside.Conclusions: We used layered virtual screening with machine learning and pharmacophore modelling approaches that could provide more objective and optimal virtual screening and avoid subjective decision making on research results. Herbal compounds from various plants have potential as antiviral candidates for SARS-CoV-2. Based on our research and literature study, one of Indonesia's potential commodity crops is Psidium guajava (guava), and people can use it directly as a preventive effort.

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“…For the protein-ligand docking prediction (PLDP), Wang et al [16] use the master-slave model for Lamarckian genetic algorithm (LGA) (one kind of hybrid genetic algorithm for which the genetic algorithm (GA) plays the role of global search while the local search algorithm plays the role of fine-tuning the search results found by GA) to speed up the computation time of the docking prediction process. Various successful works have been presented in recent years.…”

Section: Related Workmentioning

confidence: 99%

A High Performance Cloud-Based Protein-Ligand Docking Prediction Algorithm

Chen

Tsai²,

Chiang³

et al. 2013

BioMed Research International

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The potential of predicting druggability for a particular disease by integrating biological and computer science technologies has witnessed success in recent years. Although the computer science technologies can be used to reduce the costs of the pharmaceutical research, the computation time of the structure-based protein-ligand docking prediction is still unsatisfied until now. Hence, in this paper, a novel docking prediction algorithm, named fast cloud-based protein-ligand docking prediction algorithm (FCPLDPA), is presented to accelerate the docking prediction algorithm. The proposed algorithm works by leveraging two high-performance operators: (1) the novel migration (information exchange) operator is designed specially for cloud-based environments to reduce the computation time; (2) the efficient operator is aimed at filtering out the worst search directions. Our simulation results illustrate that the proposed method outperforms the other docking algorithms compared in this paper in terms of both the computation time and the quality of the end result.

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Design and Implementation of Parallel Lamarckian Genetic Algorithm for Automated Docking of Molecules

Cited by 5 publications

References 4 publications

Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches

Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches

Virtual Screening on Indonesian Herbal Compounds as COVID-19 Supportive Therapy: Machine Learning and Pharmacophore Modelling Approaches

A High Performance Cloud-Based Protein-Ligand Docking Prediction Algorithm

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