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BOINC Synergy

World Community Grid


World Community Grid Logo

World Community Grid uses BOINC to accelerate science by creating a supercomputer empowered by a global community of volunteers.

Why World Community Grid?

World Community Grid began in 2004 as a philanthropic initiative of IBM Corporate Social Responsibility, the corporate social responsibility and philanthropy division of IBM. Through Corporate Social Responsibility, IBM donates its technology and talent to address some of the world's most pressing social and environmental issues.

In 2021, IBM transferred the World Community Grid assets to Krembil Research Institute, part of the University Health Network (UHN).

While sharing the goals and principles of WCG, Krembil Research Institute aims to expand the mission of citizen science, youth outreach and integrative computational biology.https://www.worldcommunitygrid.org/about/about.s

Goal

Help scientists to identify the most important results to study in the lab, bringing them one step closer to discoveries that save lives and address global problems.

Methods

Dr. Igor Jurisica’s research is connected to the Toronto Western Hospital and the hospital’s research arm, the Krembil Research Institute; a non-profit academic biomedical research institute. Research within Krembil is focused on the development of diagnostics, treatments and management strategies in the following three programmatic areas: [i] chronic neurological/neurosurgical disorders (e.g., Parkinson's disease, stroke, epilepsy, spinal cord injuries, dementia, concussion, pain and depression); [ii] ophthalmologic disorders (e.g., glaucoma, macular degeneration, retinopathy); and [iii] musculoskeletal system disorders (e.g., osteoarthritis, rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis).

Research overview

Active research (2)

# OpenPandemics - COVID-19 Enabling the platform for fast response to global outbreaks by finding better drugs faster.

# Mapping Cancer Markers Decoding cancer by finding robust biomarkers and fathoming cancer-rewired networks.


Intermittent research (3)

# Africa Rainfall Project From improved weather modeling to better farming in sub-Saharan Africa.

# Smash Childhood Cancer Finding the best drugs for targeting key molecules in childhood cancers.

# Help Stop TB Empowering drug discovery for tuberculosis.


Completed research (26)

 
Microbiome Immunity Project

Did you know that trillions of bacteria live inside and on your body? In this comprehensive study of the human microbiome, you can help scientists understand the role these bacteria play in disease.

 
Smash Childhood Cancer

Since 2009, World Community Grid has been part of the fight against childhood cancer. This project continues that work by expanding the search for better treatments to more types of childhood cancer.

 
OpenZika

Help scientists search for antiviral drugs to combat the Zika virus, which can cause severe neurological problems, including birth defects in children whose mothers were infected during pregnancy.

 
Help Stop TB

Tuberculosis killed 1.5 million people in 2014, making it one of the world's deadliest diseases. You can help researchers learn more about this disease and how to overcome it.

 
FightAIDS@Home - Phase 2

The first phase of FightAIDS@Home made significant advances in HIV research. As the virus evolves, the research team is now pioneering the use of new analysis techniques to better identify promising anti-HIV...

 
Outsmart Ebola Together

Ebola is a deadly virus that kills up to 90% of infected victims. Use your computer to help scientists find the most promising drug leads to fight the Ebola virus!

 
Uncovering Genome Mysteries

Uncovering Genome Mysteries expects to examine close to 200 million genes from a wide variety of life forms, such as seaweeds from Australian coastlines and microbes found in Amazon river samples.

 
Computing for Sustainable Water

The mission of the Computing for Sustainable Water project is to study the effects of human activity on a large watershed and gain deeper insights into what actions can lead to restoration, health and sustainability of this...

 
Say No to Schistosoma

The mission of the Say No to Schistosoma project is to identify potential drug candidates that could possibly be developed into treatments for schistosomiasis. The extensive computing power of World Community Grid will...

 
GO Fight Against Malaria

The mission of the GO Fight Against Malaria project is to discover promising new drug candidates that could be developed into new drugs that cure drug resistant forms of malaria. The computing power of World Community Grid...

 
Drug Search for Leishmaniasis

The mission of Drug Search for Leishmaniasis is to identify potential drug candidates that could possibly be developed into treatments for Leishmaniasis. The extensive computing power of World Community Grid will be used...

 
Computing for Clean Water

The mission of Computing for Clean Water is to provide deeper insight on the molecular scale into the origins of the efficient flow of water through a novel class of filter materials. This insight will in turn guide future...

 
The Clean Energy Project - Phase 2

The mission of The Clean Energy Project is to find new materials for the next generation of solar cells and later, energy storage devices. By harnessing the immense power of World Community Grid, researchers can...

 
Discovering Dengue Drugs - Together - Phase 2

Scientists from The University of Texas Medical Branch and the University of Chicago have joined with World Community Grid researchers to combat some of the most widespread viral diseases in the developed and developing...

 
Help Cure Muscular Dystrophy - Phase 2

World Community Grid and researchers supported by Decrypthon, a partnership between AFM (French Muscular Dystrophy Association), CNRS (French National Center for Scientific Research), Universite Pierre...

 
Influenza Antiviral Drug Search

The mission of the Influenza Antiviral Drug Search project is to find new drugs that can stop the spread of an influenza infection in the body. The research will specifically address the influenza strains that have become drug...

 
Help Fight Childhood Cancer

The mission of the Help Fight Childhood Cancer project is to find drugs that can disable three particular proteins associated with neuroblastoma, one of the most frequently occurring solid tumors in children...

 
The Clean Energy Project

The mission of the Clean Energy Project is to find new materials for the next generation of solar cells and later, energy storage devices. By harnessing the immense power of World Community Grid, researchers can...

 
Nutritious Rice for the World

The objective of this project is to predict the structure of proteins of major strains of rice. The intent is to help farmers breed better rice strains with higher crop yields, promote greater disease and pest resistance, and utilize a...

 
Help Conquer Cancer

The Ontario Cancer Institute (OCI), Princess Margaret Hospital and University Health Network have teamed with World Community Grid to improve the results of protein X-ray crystallography in order to increase understanding of...

 
AfricanClimate@Home

The mission of AfricanClimate@Home is to develop more accurate climate models of specific regions in Africa. This will serve as a basis for understanding how the climate will change in the future so...

 
Discovering Dengue Drugs - Together

The mission of Discovering Dengue Drugs – Together is to identify promising drug leads to combat the Dengue, Hepatitis C, West Nile, Yellow Fever, and other related viruses. The extensive computing power of World...

 
Help Cure Muscular Dystrophy

This project is investigating protein-protein, protein-DNA and protein-ligand interactions for the 10,000 proteins whose structures are known, with particular focus on those proteins that play a role in neuromuscular diseases...

 
Genome Comparison

World Community Grid and the Oswaldo Cruz Institute, Fiocruz, will be comparing genomic information to improve the quality and interpretation of biological data and our understanding of biological systems...

 
Help Defeat Cancer

World Community Grid and The Cancer Institute of New Jersey, Rutgers University and UMDNJ - Robert Wood Johnson Medical School will be examining Tissue Microarrays to determine how to improve the treatment of...

 
Human Proteome Folding - Phase 2

Human Proteome Folding Phase 2 (HPF2) continues where the first phase left off. The two main objectives of the project are to: 1) obtain higher resolution structures for specific human proteins and pathogen proteins and 2...

 
FightAIDS@Home

FightAIDS@Home is a project focused on using computation methods to identify candidate drugs that have the right shape and chemical characteristics to block HIV protease. This approach is called "Structure-Based...

 
Human Proteome Folding

The Human Proteome Folding project will provide scientists with data that predicts the shape of a very large number of human proteins. These predictions will give scientists the clues they need to identify the biological functions of...

Project team / Sponsors

Dr. Igor Jurisica. Krembil Research Institute, part of the University Health Network (UHN).

Scientific publications

Source: https://boinc.berkeley.edu/pubs.php#World

World Community Grid (Computing for Clean Water)

  1. Cao, Wei, Jin Wang and Ming Ma. Carbon nanostructure based mechano-nanofluidics. Journal of Micromechanics and Microengineering (2018). DOI: 10.1088/1361-6439/aaa782.
  2. Ma, Ming, François Grey, Luming Shen, Michael Urbakh, Shuai Wu, Jefferson Zhe Liu, Yilun Liu and Quanshui Zheng. Water transport inside carbon nanotubes mediated by phonon-induced oscillating friction. Nature Nanotechnology (2015). DOI: 10.1038/nnano.2015.134.
  3. Ma, Ming D., Luming Shen, John Sheridan, Jefferson Zhe Liu, Chao Chen and Quanshui Zheng. Friction of water slipping in carbon nanotubes. Physical Review E (2011). DOI: 10.1103/PhysRevE.83.036316.

World Community Grid (Discovering Dengue Drugs)

  1. Viswanathan, Usha, Suzanne M. Tomlinson, John M. Fonner, Stephen A. Mock and Stanley J. Watowich. Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery Web portal. Journal of Chemical Information and Modeling (2014). DOI: 10.1021/ci500531r.
  2. Tomlinson, S. M., R. D. Malmstrom and S. J. Watowich. New Approaches to Structure-Based Discovery of Dengue Protease Inhibitors. Infectious Disorders - Drug Targets (2009). DOI: 10.2174/1871526510909030327.

World Community Grid (Drug Search for Leishmaniasis)

  1. Ochoa, Rodrigo, Stanley J. Watowich, Andrés Flórez, Carol V. Mesa, Sara M. Robledo and Carlos Muskus. Drug search for leishmaniasis: a virtual screening approach by grid computing. Journal of Computer-Aided Molecular Design (2016). DOI: 10.1007/s10822-016-9921-4.
  2. Flórez, Andrés F., Stanley Watowich, Carlos Muskus, Andrés F. Flórez, Stanley Watowich and Carlos Muskus. Current Advances in Computational Strategies for Drug Discovery in Leishmaniasis. (2012).

World Community Grid (FightAIDS@Home)

  1. Goodsell, David S., Michel F. Sanner, Arthur J. Olson and Stefano Forli. The AutoDock suite at 30. Protein Science (2021). DOI: 10.1002/pro.3934.
  2. Craveur, Pierrick, Anna T. Gres, Karen A. Kirby et al. Novel Intersubunit Interaction Critical for HIV-1 Core Assembly Defines a Potentially Targetable Inhibitor Binding Pocket. mBio (2019). DOI: 10.1128/mBio.02858-18.
  3. Xia, Junchao, William Flynn, Emilio Gallicchio, Keith Uplinger, Jonathan D. Armstrong, Stefano Forli, Arthur J. Olson and Ronald M. Levy. Massive-Scale Binding Free Energy Simulations of HIV Integrase Complexes Using Asynchronous Replica Exchange Framework Implemented on the IBM WCG Distributed Network. Journal of Chemical Information and Modeling (2019). DOI: 10.1021/acs.jcim.8b00817.
  4. Forli, Stefano and Arthur J. Olson. Computational Challenges of Structure-Based Approaches Applied to HIV. The Future of HIV-1 Therapeutics (2015).
  5. Xia, Junchao, William F. Flynn, Emilio Gallicchio, Bin W. Zhang, Peng He, Zhiqiang Tan and Ronald M. Levy. Large-scale asynchronous and distributed multidimensional replica exchange molecular simulations and efficiency analysis. Journal of Computational Chemistry (2015). DOI: 10.1002/jcc.23996.
  6. Gallicchio, Emilio, Junchao Xia, William F. Flynn, Baofeng Zhang, Sade Samlalsingh, Ahmet Mentes and Ronald M. Levy. Asynchronous replica exchange software for grid and heterogeneous computing. Computer Physics Communications (2015). DOI: 10.1016/j.cpc.2015.06.010.
  7. Perryman, Alexander L., Daniel N. Santiago, Stefano Forli, Diogo Santos-Martins and Arthur J. Olson. Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein–ligand binding challenge. Journal of Computer-Aided Molecular Design (2014). DOI: 10.1007/s10822-014-9709-3.
  8. Perryman, Alexander L., Qing Zhang, Holly H. Soutter, Robin Rosenfeld, Duncan E. McRee, Arthur J. Olson, John E. Elder and C. David Stout. Fragment-Based Screen against HIV Protease. Chemical Biology & Drug Design (2010). DOI: 10.1111/j.1747-0285.2009.00943.x.
  9. Perryman, Alex L., Stefano Forli, Garrett M. Morris et al. A Dynamic Model of HIV Integrase Inhibition and Drug Resistance. Journal of Molecular Biology (2010). DOI: 10.1016/j.jmb.2010.01.033.
  10. Cosconati, Sandro, Stefano Forli, Alex L Perryman, Rodney Harris, David S Goodsell and Arthur J Olson. Virtual Screening with AutoDock: Theory and Practice. Expert Opinion on Drug Discovery (2010). DOI: 10.1517/17460441.2010.484460.
  11. Morris, Garrett M., Ruth Huey, William Lindstrom, Michel F. Sanner, Richard K. Belew, David S. Goodsell and Arthur J. Olson. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry (2009). DOI: 10.1002/jcc.21256.
  12. Chang, Max W., William Lindstrom, Arthur J. Olson and Richard K. Belew. Analysis of HIV Wild-Type and Mutant Structures via in Silico Docking against Diverse Ligand Libraries. Journal of Chemical Information and Modeling (2007). DOI: 10.1021/ci700044s.

World Community Grid (GO Fight Against Malaria)

  1. Perryman, Alexander L., Weixuan Yu, Xin Wang et al. A Virtual Screen Discovers Novel, Fragment-Sized Inhibitors of Mycobacterium tuberculosis InhA. Journal of Chemical Information and Modeling (2015). DOI: 10.1021/ci500672v.

World Community Grid (Genome Comparison)

  1. Lifschitz, Sérgio, Carlos Juliano M. Viana, Cristian Tristão, Marcos Catanho, Wim M. Degrave, Antonio Basílio de Miranda, Márcia Bezerra and Thomas D. Otto. Design and Implementation of ProteinWorldDB. Advances in Bioinformatics and Computational Biology (2012).
  2. Otto, Thomas Dan, Marcos Catanho, Cristian Tristão et al. ProteinWorldDB: querying radical pairwise alignments among protein sets from complete genomes. Bioinformatics (2010). DOI: 10.1093/bioinformatics/btq011.

World Community Grid (Help Conquer Cancer)

  1. Kotseruba, Yulia, Christian A. Cumbaa and Igor Jurisica. High-throughput protein crystallization on the World Community Grid and the GPU. Journal of Physics: Conference Series (2012). DOI: 10.1088/1742-6596/341/1/012027.
  2. Cumbaa, Christian A. and Igor Jurisica. Protein crystallization analysis on the World Community Grid. Journal of Structural and Functional Genomics (2010). DOI: 10.1007/s10969-009-9076-9.
  3. Snell, Edward H., Angela M. Lauricella, Stephen A. Potter et al. Establishing a training set through the visual analysis of crystallization trials. Part II: crystal examples. Acta Crystallographica Section D: Biological Crystallography (2008). DOI: 10.1107/S0907444908028059.
  4. Snell, Edward H., Joseph R. Luft, Stephen A. Potter et al. Establishing a training set through the visual analysis of crystallization trials. Part I: ∼150 000 images. Acta Crystallographica Section D: Biological Crystallography (2008). DOI: 10.1107/S0907444908028047.

World Community Grid (Help Cure Muscular Dystrophy)

  1. Dequeker, Chloé, Elodie Laine and Alessandra Carbone. Decrypting protein surfaces by combining evolution, geometry, and molecular docking. Proteins: Structure, Function, and Bioinformatics (2019). DOI: 10.1002/prot.25757.
  2. Lagarde, Nathalie, Alessandra Carbone and Sophie Sacquin-Mora. Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions. Proteins: Structure, Function, and Bioinformatics (2018). DOI: 10.1002/prot.25506.
  3. Laine, Elodie and Alessandra Carbone. Protein social behavior makes a stronger signal for partner identification than surface geometry. Proteins: Structure, Function, and Bioinformatics (2017). DOI: 10.1002/prot.25206.
  4. Vamparys, Lydie, Benoist Laurent, Alessandra Carbone and Sophie Sacquin-Mora. Great interactions: How binding incorrect partners can teach us about protein recognition and function. Proteins: Structure, Function, and Bioinformatics (2016). DOI: 10.1002/prot.25086.
  5. Lopes, Anne, Sophie Sacquin-Mora, Viktoriya Dimitrova, Elodie Laine, Yann Ponty and Alessandra Carbone. Protein-Protein Interactions in a Crowded Environment: An Analysis via Cross-Docking Simulations and Evolutionary Information. PLOS Computational Biology (2013). DOI: 10.1371/journal.pcbi.1003369.
  6. Bertis, Viktors, Raphaël Bolze, Frédéric Desprez and Kevin Reed. From Dedicated Grid to Volunteer Grid: Large Scale Execution of a Bioinformatics Application. Journal of Grid Computing (2009). DOI: 10.1007/s10723-009-9130-7.
  7. Engelen, Stefan, Ladislas A. Trojan, Sophie Sacquin-Mora, Richard Lavery and Alessandra Carbone. Joint Evolutionary Trees: A Large-Scale Method To Predict Protein Interfaces Based on Sequence Sampling. PLOS Computational Biology (2009). DOI: 10.1371/journal.pcbi.1000267.
  8. Sacquin-Mora, Sophie, Alessandra Carbone and Richard Lavery. Identification of Protein Interaction Partners and Protein–Protein Interaction Sites. Journal of Molecular Biology (2008). DOI: 10.1016/j.jmb.2008.08.002.

World Community Grid (Help Defeat Cancer)

  1. Foran, David J, Lin Yang, Wenjin Chen et al. ImageMiner: a software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology. Journal of the American Medical Informatics Association (2011). DOI: 10.1136/amiajnl-2011-000170.
  2. Wang, Fusheng. Grid-Enabled, High-performance Microscopy Image Analysis. (2010).
  3. Lin Yang, Wenjin Chen, P. Meer, G. Salaru, L.A. Goodell, V. Berstis and D.J. Foran. Virtual Microscopy and Grid-Enabled Decision Support for Large-Scale Analysis of Imaged Pathology Specimens. IEEE Transactions on Information Technology in Biomedicine (2009). DOI: 10.1109/TITB.2009.2020159.
  4. Lin Yang, O. Tuzel, Wenjin Chen, P. Meer, G. Salaru, L.A. Goodell and D.J. Foran. PathMiner: A Web-Based Tool for Computer-Assisted Diagnostics in Pathology. IEEE Transactions on Information Technology in Biomedicine (2009). DOI: 10.1109/TITB.2008.2008801.
  5. DiPaola, Robert S., Dmitri Dvorzhinski, Anu Thalasila et al. Therapeutic starvation and autophagy in prostate cancer: A new paradigm for targeting metabolism in cancer therapy. The Prostate (2008). DOI: 10.1002/pros.20837.

World Community Grid (Help Fight Childhood Cancer)

  1. Fukuda, Mayu, Atsushi Takatori, Yohko Nakamura, Akiko Suganami, Tyuji Hoshino, Yutaka Tamura and Akira Nakagawara. Effects of novel small compounds targeting TrkB on neuronal cell survival and depression-like behavior. Neurochemistry International (2016). DOI: 10.1016/j.neuint.2016.04.017.
  2. Nakamura, Yohko, Akiko Suganami, Mayu Fukuda et al. Identification of novel candidate compounds targeting TrkB to induce apoptosis in neuroblastoma. Cancer Medicine (2014). DOI: 10.1002/cam4.175.

World Community Grid (Help Stop TB)

  1. Groenewald, Wilma, Ricardo A. Parra-Cruz, Christof M. Jäger and Anna K. Croft. Revealing solvent-dependent folding behavior of mycolic acids from Mycobacterium tuberculosis by advanced simulation analysis. Journal of Molecular Modeling (2019). DOI: 10.1007/s00894-019-3943-5.

World Community Grid (Human Proteome Folding)

  1. Baltz, Alexander G., Mathias Munschauer, Björn Schwanhäusser et al. The mRNA-Bound Proteome and Its Global Occupancy Profile on Protein-Coding Transcripts. Molecular Cell (2012). DOI: 10.1016/j.molcel.2012.05.021.
  2. Pentony, M. M., P. Winters, D. Penfold-Brown, K. Drew, A. Narechania, R. DeSalle, R. Bonneau and M. D. Purugganan. The Plant Proteome Folding Project: Structure and Positive Selection in Plant Protein Families. Genome Biology and Evolution (2012). DOI: 10.1093/gbe/evs015.
  3. Drew, Kevin, Patrick Winters, Glenn L. Butterfoss et al. The Proteome Folding Project: Proteome-scale prediction of structure and function. Genome Research (2011). DOI: 10.1101/gr.121475.111.
  4. Boxem, Mike, Zoltan Maliga, Niels Klitgord et al. A Protein Domain-Based Interactome Network for C. elegans Early Embryogenesis. Cell (2008). DOI: 10.1016/j.cell.2008.07.009.
  5. Bonneau, Richard, Marc T. Facciotti, David J. Reiss et al. A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell. Cell (2007). DOI: 10.1016/j.cell.2007.10.053.
  6. Malmström, Lars, Michael Riffle, Charlie E. M. Strauss, Dylan Chivian, Trisha N. Davis, Richard Bonneau and David Baker. Superfamily assignments for the yeast proteome through integration of structure prediction with the gene ontology. PLoS biology (2007). DOI: 10.1371/journal.pbio.0050076.
  7. Andersen-Nissen, Erica, Kelly D. Smith, Richard Bonneau, Roland K. Strong and Alan Aderem. A conserved surface on Toll-like receptor 5 recognizes bacterial flagellin. Journal of Experimental Medicine (2007). DOI: 10.1084/jem.20061400.
  8. Avila-Campillo, Iliana, Kevin Drew, John Lin, David J. Reiss and Richard Bonneau. BioNetBuilder: automatic integration of biological networks. Bioinformatics (2007). DOI: 10.1093/bioinformatics/btl604.
  9. Malmström, Lars. Genome-wide structural and functional protein characterization by ab initio protein structure prediction. Report / Department of Electrical Measurements. Lund Institute of Technology (2005).

World Community Grid (Mapping Cancer Markers)

  1. Hauschild, Anne-Christin, Chiara Pastrello, Andrea E.M. Rossos and Igor Jurisica. Visualization of Biomedical Networks. Encyclopedia of Bioinformatics and Computational Biology (2019).
  2. Paulitti, Alice, Eva Andreuzzi, Dario Bizzotto et al. The ablation of the matricellular protein EMILIN2 causes defective vascularization due to impaired EGFR-dependent IL-8 production affecting tumor growth. Oncogene (2018). DOI: 10.1038/s41388-017-0107-x.
  3. Wong, Serene W.H., Chiara Pastrello, Max Kotlyar, Christos Faloutsos and Igor Jurisica. SDREGION: Fast Spotting of Changing Communities in Biological Networks. KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2018). DOI: 10.1145/3219819.3219854.
  4. Anne-Christin Hauschild, Christian A Cumbaa, Mike Tsay and Igor Jurisica. Network Motif Families for Lung Cancer Diagnostics: A World Community Grid Approach. (2017). DOI: 10.13140/RG.2.2.34687.51363.
  5. Fortney, Kristen, Joshua Griesman, Max Kotlyar, Chiara Pastrello, Marc Angeli, Ming Sound-Tsao and Igor Jurisica. Prioritizing Therapeutics for Lung Cancer: An Integrative Meta-analysis of Cancer Gene Signatures and Chemogenomic Data. PLOS Computational Biology (2015). DOI: 10.1371/journal.pcbi.1004068.
  6. Kotlyar, Max, Chiara Pastrello, Flavia Pivetta et al. In silico prediction of physical protein interactions and characterization of interactome orphans. Nature Methods (2015). DOI: 10.1038/nmeth.3178.

World Community Grid (Microbiome Immunity Project)

  1. Koehler Leman, Julia, Pawel Szczerbiak, P. Douglas Renfrew et al. Sequence-structure-function relationships in the microbial protein universe. Nature Communications (2023). DOI: 10.1038/s41467-023-37896-w.

World Community Grid (Nutritious Rice for the World)

  1. Hung, Ling-Hong and Ram Samudrala. Rice protein models from the Nutritious Rice for the World Project. (2016). DOI: 10.1101/091975.
  2. Hung, Ling-Hong and Ram Samudrala. fast_protein_cluster: parallel and optimized clustering of large-scale protein modeling data. Bioinformatics (2014). DOI: 10.1093/bioinformatics/btu098.
  3. Hung, Ling-Hong and Ram Samudrala. Accelerated protein structure comparison using TM-score-GPU. Bioinformatics (2012). DOI: 10.1093/bioinformatics/bts345.
  4. Hung, Ling-Hong, Michal Guerquin and Ram Samudrala. GPU-Q-J, a fast method for calculating root mean square deviation (RMSD) after optimal superposition. BMC Research Notes (2011). DOI: 10.1186/1756-0500-4-97.

World Community Grid (OpenZika)

  1. Mottin, Melina, Bruna Katiele de Paula Sousa, Nathalya Cristina de Moraes Roso Mesquita et al. Discovery of New Zika Protease and Polymerase Inhibitors through the Open Science Collaboration Project OpenZika. Journal of Chemical Information and Modeling (2022). DOI: 10.1021/acs.jcim.2c00596.
  2. Silva, Suely, Jacqueline Farinha Shimizu, Débora Moraes de Oliveira et al. A diarylamine derived from anthranilic acid inhibits ZIKV replication. Scientific Reports (2019). DOI: 10.1038/s41598-019-54169-z.
  3. Hernandez, Helen W., Melinda Soeung, Kimberley M. Zorn et al. High Throughput and Computational Repurposing for Neglected Diseases. Pharmaceutical Research (2019). DOI: 10.1007/s11095-018-2558-3.
  4. Mottin, Melina, Joyce Villa Verde Bastos Borba, Cleber Camilo Melo-Filho et al. Computational drug discovery for the Zika virus. Brazilian Journal of Pharmaceutical Sciences (2018). DOI: 10.1590/s2175-97902018000001002.
  5. Mottin, Melina, Joyce V.V.B. Borba, Rodolpho C. Braga et al. The A–Z of Zika drug discovery. Drug Discovery Today (2018). DOI: 10.1016/j.drudis.2018.06.014.
  6. Mottin, Melina, Rodolpho C. Braga, Roosevelt A. da Silva, Joao H. Martins da Silva, Alexander L. Perryman, Sean Ekins and Carolina Horta Andrade. Molecular dynamics simulations of Zika virus NS3 helicase: Insights into RNA binding site activity. Biochemical and Biophysical Research Communications (2017). DOI: 10.1016/j.bbrc.2017.03.070.
  7. Ekins, Sean, Alexander L. Perryman and Carolina Horta Andrade. OpenZika: An IBM World Community Grid Project to Accelerate Zika Virus Drug Discovery. PLOS Neglected Tropical Diseases (2016). DOI: 10.1371/journal.pntd.0005023.
  8. Ekins, Sean, John Liebler, Bruno J. Neves, Warren G. Lewis, Megan Coffee, Rachelle Bienstock, Christopher Southan and Carolina H. Andrade. Illustrating and homology modeling the proteins of the Zika virus. F1000Research (2016). DOI: 10.12688/f1000research.8213.2.

World Community Grid (The Clean Energy Project)

  1. Lopez, Steven A., Benjamin Sanchez-Lengeling, Julio De Goes Soares and Alán Aspuru-Guzik. Design Principles and Top Non-Fullerene Acceptor Candidates for Organic Photovoltaics. Joule (2017). DOI: 10.1016/j.joule.2017.10.006.
  2. Pyzer-Knapp, Edward O., Changwon Suh, Rafael Gómez-Bombarelli, Jorge Aguilera-Iparraguirre and Alán Aspuru-Guzik. What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery. Annual Review of Materials Research (2015). DOI: 10.1146/annurev-matsci-070214-020823.
  3. Pyzer-Knapp, Edward O., Kewei Li and Alan Aspuru-Guzik. Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery. Advanced Functional Materials (2015). DOI: 10.1002/adfm.201501919.
  4. Hachmann, Johannes, Roberto Olivares-Amaya, Adrian Jinich et al. Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project. Energy Environ. Sci. (2014). DOI: 10.1039/C3EE42756K.
  5. Olivares-Amaya, Roberto, Carlos Amador-Bedolla, Johannes Hachmann, Sule Atahan-Evrenk, Roel S. Sánchez-Carrera, Leslie Vogt and Alán Aspuru-Guzik. Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics. Energy & Environmental Science (2011). DOI: 10.1039/c1ee02056k.
  6. Hachmann, Johannes, Roberto Olivares-Amaya, Sule Atahan-Evrenk et al. The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid. The Journal of Physical Chemistry Letters (2011). DOI: 10.1021/jz200866s.
  7. Sokolov, Anatoliy N., Sule Atahan-Evrenk, Rajib Mondal et al. From computational discovery to experimental characterization of a high hole mobility organic crystal. Nature Communications (2011). DOI: 10.1038/ncomms1451.

Contributing

If you're interested in supporting this project, install and download BOINC and attach to the project using its official URL: https://www.worldcommunitygrid.org/.


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