World Community Grid

World Community Grid
World Community Grid logo
Project
Status	Active
Category	Biomedical / Humanitarian science
Compute	CPU
Requires	None
Development
Developer	United Devices (2004); IBM (2004–2022); Krembil Research Institute / UHN (2022–present)
Author	IBM Corporate Social Responsibility
Sponsor	University Health Network
Maintainer	Dr. Igor Jurisica, Krembil Research Institute
Initial release	November 16, 2004  (22 years ago)
Software
Operating system	Windows, Linux, macOS, Android, Raspberry Pi OS
BOINC statistics
Stats as of	January 1, 2023  (3 years ago)
Performance	402 TFLOPS
Active users	23,248
Total users	79,354
Active hosts	57,672
Total hosts	5,517,865
Metadata
Website	https://www.worldcommunitygrid.org/

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

Open Pandemics - COVID-19 BOINC Screensaver

World Community Grid (WCG) is a volunteer computing platform dedicated to humanitarian and biomedical scientific research. It harnesses the idle processing power of everyday devices (personal computers, laptops, Android smartphones, and Raspberry Pi systems) to perform large-scale scientific calculations that would otherwise require decades of supercomputing time. Since its founding in 2004, the project has expanded to cover diseases including HIV/AIDS, cancer, tuberculosis, dengue fever, Ebola, Zika virus, and COVID-19, as well as research into clean energy, water purification, food security, and climate science.

Why World Community Grid?

World Community Grid began on November 16, 2004, as a philanthropic initiative of IBM Corporate Social Responsibility, the corporate social responsibility and philanthropy division of IBM. The project was inspired by a successful predecessor: in 2003, IBM and other partners sponsored the United Devices Smallpox Research Grid Project, which used a distributed computing grid to screen 35 million potential drug molecules against several smallpox proteins. In the first 72 hours alone, 100,000 results were returned, and by the project's end, 44 strong treatment candidates had been identified. Encouraged by those results, IBM launched World Community Grid with the goal of creating a permanent technical environment where humanitarian research of this kind could be run continuously.

Through Corporate Social Responsibility, IBM donated its technology and talent to address some of the world's most pressing social and environmental issues. The platform was initially Windows-only and used the proprietary Grid MP client software from United Devices. Demand for broader platform support led to the addition of the open-source BOINC (Berkeley Open Infrastructure for Network Computing) framework in November 2005, bringing Mac OS X and Linux support to the project. By 2007, the Grid MP client had been fully retired and the project consolidated entirely on BOINC.

In September 2021, IBM announced that it had transferred ownership of World Community Grid to the Krembil Research Institute, part of the University Health Network (UHN) in Toronto, Ontario, Canada. Operational management formally transferred to Krembil in February 2022.

Goal

The overarching goal of World Community Grid is to help scientists identify the most important results to study in the laboratory, bringing them one step closer to discoveries that save lives and address global problems. Rather than replacing lab research, WCG acts as a filter: by computationally screening millions — sometimes billions — of candidate molecules or parameter sets, researchers can focus their scarce lab resources on only the most promising leads.

"WCG continues to support open-source and open-data research and helps reduce computational time to allow scientists to address the world's most pressing questions at no cost to the researchers."

All data generated by World Community Grid volunteers must be released into the public domain and made freely available to the scientific community — a foundational requirement for any project accepted onto the platform.

How It Works

World Community Grid runs on top of BOINC, an open-source middleware system developed at the University of California, Berkeley, originally under a National Science Foundation grant. After downloading the WCG client (a pre-configured BOINC installer) from the official website, the software runs quietly in the background. It monitors available system resources and, when the device is idle, downloads a work unit from the WCG servers, performs the required calculations, and sends the results back.

To ensure accuracy, the servers distribute multiple copies of each work unit to different volunteers. When results are returned, they are validated against each other, and statistical outliers are discarded before final data is accepted.

Credits and Points

Volunteer contributions are tracked using the BOINC Credit System. Upon completing a work unit, the BOINC client reports a point value based on software benchmarks (measured in cobblestones, where $1\text{cobblestone}=\frac{1}{200}\text{GigaFLOP-day}$). The WCG servers compare claims from each machine that processed the same work unit, discard outliers, and award the averaged value to each contributor. Points allow users to track their personal contribution and compete on leaderboards.

Teams and Partners

Users may join teams created by organizations or individuals, fostering community identity and friendly competition. As of April 2021, World Community Grid had 452 recognized partner organizations promoting the grid within their communities.

CPU Throttling

The BOINC client is designed not to slow down the host computer. World Community Grid sets conservative defaults: the CPU throttle is 60% by default, meaning the client runs at full load for roughly 3 seconds, then pauses for 2 seconds, cycling continuously. This pattern avoids sustained heat buildup. Windows users can additionally install TThrottle, a third-party add-on that reads CPU and GPU temperatures in real time and adjusts computation accordingly.

Methods

Screensaver HUMAN PROTEOME FOLDING Phase2. World Community Grid solving the Human Proteome Folding Project.

Dr. Igor Jurisica's research drives World Community Grid's current scientific direction. Dr. Jurisica is a Senior Scientist at the Krembil Research Institute and a Professor at the University of Toronto, with appointments at Toronto Western Hospital. His work focuses on integrative computational biology — combining large-scale data analysis, machine learning, and network biology to understand complex diseases.

Research within Krembil is focused on the development of diagnostics, treatments and management strategies across three programmatic areas:

1. Chronic neurological and neurosurgical disorders — including Parkinson's disease, stroke, epilepsy, spinal cord injuries, dementia, concussion, pain, and depression.
2. Ophthalmologic disorders — including glaucoma, macular degeneration, and retinopathy.
3. Musculoskeletal system disorders — including osteoarthritis, rheumatoid arthritis, systemic lupus erythematosus, and ankylosing spondylitis.

The primary computational technique used across WCG's biomedical projects is molecular docking, in which candidate drug molecules are algorithmically fitted to target protein structures to predict binding affinity. A typical project may dock tens of millions of compounds against one or more proteins — a task that would require tens of thousands of years of computing time on a single machine, but can be completed in months across the volunteer grid.

Project Team / Sponsors

World Community Grid is currently managed by Dr. Igor Jurisica and his team at the Krembil Research Institute, part of the University Health Network (UHN) in Toronto, Ontario, Canada.

UHN has Canada's largest hospital-based research program, comprising four major teaching hospitals: Toronto Western Hospital, Toronto General Hospital, Princess Margaret Cancer Centre, and Toronto Rehabilitation Institute, as well as The Michener Institute of Education.

Previously, the project was funded and operated by IBM from its launch in November 2004 through February 2022. IBM provided all server infrastructure, administrative overhead, and technical support during that nearly two-decade period. The project is grateful for IBM's extensive financial and operational support.

Research Overview

World Community Grid operates as an umbrella platform hosting multiple research projects simultaneously. Users are enrolled in all active projects by default but may opt out of any they choose. Over the life of the project, WCG volunteers have collectively donated the equivalent of more than 2 million years of computing time and completed more than 6 billion work units.

Active Research

1. OpenPandemics - COVID-19 — Launched to enable a rapid-response platform for global disease outbreaks, the project uses molecular docking to screen drug candidates against SARS-CoV-2 proteins in partnership with scientists at Scripps Research. The goal is to identify compounds that could block viral replication, potentially forming the basis of antiviral drugs for COVID-19 and future pandemic pathogens.

1. Mapping Cancer Markers — One of WCG's longest-running and most ambitious projects, this research aims to identify robust molecular biomarkers associated with various cancer types. By decoding cancer-rewired biological networks, researchers hope to enable earlier detection and more personalized treatment strategies.

Intermittent Research

1. Africa Rainfall Project — Uses regional climate modelling to improve weather forecasts and agricultural planning across sub-Saharan Africa, where rain-fed agriculture supports the food supply for hundreds of millions of people.

1. Smash Childhood Cancer — An expansion of earlier WCG work on neuroblastoma, this project searches for the best drug candidates targeting key molecular proteins across a broader range of childhood cancers.

1. Help Stop TB — Focuses on finding new drug leads for tuberculosis (TB), which remains one of the world's leading infectious disease killers. The project performs virtual screening of millions of compounds against TB target proteins.

Completed Research (28)

Over the course of the project's history, 28 research projects have been completed. These include:

Project	Focus Area	Notable Outcome
Human Proteome Folding (Phase 1 & 2)	Protein structure prediction	Produced a database of ~120,000 protein domain structures; computation that would have taken 100 years conventionally was done in one year.
FightAIDS@Home (Phase 1 & 2)	HIV/AIDS drug discovery	Discovered two compounds representing a potentially new class of AIDS-fighting drugs; identified new vulnerabilities on the HIV-1 capsid protein as a possible new drug target.
Help Fight Childhood Cancer	Neuroblastoma	Screened over 3 million drug candidates; identified 7 compounds that destroy neuroblastoma cells without apparent side effects.
The Clean Energy Project (Phase 1 & 2)	Solar cell materials	Published a database of over 2.3 million organic molecules; identified 35,000 compounds with potential to double the efficiency of carbon-based organic solar cells.
OpenZika	Zika virus drug discovery	Identified compound FAM 3, which inhibits the NS3 Helicase protein of the Zika virus, reducing viral replication by up to 86%.
GO Fight Against Malaria	Malaria and drug-resistant TB	First WCG project to complete a billion docking calculations; discovered several molecules effective against malaria and drug-resistant tuberculosis including TDR-TB.
Discovering Dengue Drugs Together (Phase 1 & 2)	Dengue fever and Flaviviridae	Identified several new dengue protease inhibitors, many of which also inhibit the West Nile virus protease.
Help Conquer Cancer	Protein crystallography for cancer	Analysis that would have taken 162 years on conventional computers was completed in under 2 years.
Nutritious Rice for the World	Food security / crop genetics	Predicted protein structures for major rice strains to help breed higher-yield, more disease-resistant varieties.
Computing for Clean Water	Nanotechnology / water filtration	Studied molecular-scale water flow through novel filter materials to guide development of low-cost water filters.
Drug Search for Leishmaniasis	Neglected tropical disease	Tested top 10 compounds in vivo; one compound induced near-complete curing of lesions in hamsters.
AfricanClimate@Home	Climate modelling	Developed more accurate regional climate models for Africa.
Outsmart Ebola Together	Ebola drug discovery	Screened millions of compounds against Ebola viral proteins to identify drug leads.
Microbiome Immunity Project	Human microbiome	Comprehensive study of the role of gut bacteria in human disease.
Uncovering Genome Mysteries	Genomics	Examined close to 200 million genes from diverse organisms.
Help Cure Muscular Dystrophy (Phase 1 & 2)	Neuromuscular diseases	Investigated protein interactions for more than 2,200 structurally known proteins linked to muscular dystrophy and related diseases.
Influenza Antiviral Drug Search	Influenza	Searched for drugs effective against drug-resistant and novel influenza strains.
Smash Childhood Cancer	Pediatric cancers (broader)	Expanded neuroblastoma drug discovery to additional childhood cancer types.
Help Defeat Cancer	Tissue microarray analysis	Examined cancer tissue microarrays to improve precision medicine diagnosis and treatment.
Genome Comparison	Comparative genomics	Compared genomic information to improve biological data quality and host-pathogen understanding; led by Fiocruz (Oswaldo Cruz Institute), Brazil.
Say No to Schistosoma	Schistosomiasis	Identified potential drug candidates for schistosomiasis, a neglected tropical disease affecting hundreds of millions.
Computing for Sustainable Water	Watershed ecology	Modelled nutrient flows and ecological responses across 64,000 km2 of the Chesapeake Bay watershed.

Publications

Papers using BOINC-computed data

Computing for Clean Water

1. (2018).Carbon nanostructure based mechano-nanofluidics. Journal of Micromechanics and Microengineering. DOI: 10.1088/1361-6439/aaa782.
2. (2015).Water transport inside carbon nanotubes mediated by phonon-induced oscillating friction. Nature Nanotechnology. DOI: 10.1038/nnano.2015.134.
3. (2011).Friction of water slipping in carbon nanotubes. Physical Review E. DOI: 10.1103/PhysRevE.83.036316.

Discovering Dengue Drugs

1. (2014).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. DOI: 10.1021/ci500531r.
2. (2009).New Approaches to Structure-Based Discovery of Dengue Protease Inhibitors. Infectious Disorders - Drug Targets. DOI: 10.2174/1871526510909030327.

Drug Search for Leishmaniasis

1. (2016).Drug search for leishmaniasis: a virtual screening approach by grid computing. Journal of Computer-Aided Molecular Design. DOI: 10.1007/s10822-016-9921-4.
2. (2012).Current Advances in Computational Strategies for Drug Discovery in Leishmaniasis.

FightAIDS@Home

1. (2022).Structure-based virtual screening workflow to identify antivirals targeting HIV-1 capsid. Journal of Computer-Aided Molecular Design. DOI: 10.1007/s10822-022-00446-5.
2. (2021).The AutoDock suite at 30. Protein Science. DOI: 10.1002/pro.3934.
3. (2019).Novel Intersubunit Interaction Critical for HIV-1 Core Assembly Defines a Potentially Targetable Inhibitor Binding Pocket. mBio. DOI: 10.1128/mBio.02858-18.
4. (2019).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. DOI: 10.1021/acs.jcim.8b00817.
5. (2015).Computational Challenges of Structure-Based Approaches Applied to HIV. The Future of HIV-1 Therapeutics.
6. (2015).Large-scale asynchronous and distributed multidimensional replica exchange molecular simulations and efficiency analysis. Journal of Computational Chemistry. DOI: 10.1002/jcc.23996.
7. (2015).Asynchronous replica exchange software for grid and heterogeneous computing. Computer Physics Communications. DOI: 10.1016/j.cpc.2015.06.010.
8. (2014).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. DOI: 10.1007/s10822-014-9709-3.
9. (2010).Fragment-Based Screen against HIV Protease. Chemical Biology & Drug Design. DOI: 10.1111/j.1747-0285.2009.00943.x.
10. (2010).A Dynamic Model of HIV Integrase Inhibition and Drug Resistance. Journal of Molecular Biology. DOI: 10.1016/j.jmb.2010.01.033.
11. (2010).Virtual Screening with AutoDock: Theory and Practice. Expert Opinion on Drug Discovery. DOI: 10.1517/17460441.2010.484460.
12. (2009).AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry. DOI: 10.1002/jcc.21256.
13. (2007).Analysis of HIV Wild-Type and Mutant Structures via in Silico Docking against Diverse Ligand Libraries. Journal of Chemical Information and Modeling. DOI: 10.1021/ci700044s.

GO Fight Against Malaria

1. (2015).A Virtual Screen Discovers Novel, Fragment-Sized Inhibitors of Mycobacterium tuberculosis InhA. Journal of Chemical Information and Modeling. DOI: 10.1021/ci500672v.

Genome Comparison

1. (2012).Design and Implementation of ProteinWorldDB. Advances in Bioinformatics and Computational Biology.
2. (2010).ProteinWorldDB: querying radical pairwise alignments among protein sets from complete genomes. Bioinformatics. DOI: 10.1093/bioinformatics/btq011.

Help Conquer Cancer

1. (2012).High-throughput protein crystallization on the World Community Grid and the GPU. Journal of Physics: Conference Series. DOI: 10.1088/1742-6596/341/1/012027.
2. (2010).Protein crystallization analysis on the World Community Grid. Journal of Structural and Functional Genomics. DOI: 10.1007/s10969-009-9076-9.
3. (2008).Establishing a training set through the visual analysis of crystallization trials. Part II: crystal examples. Acta Crystallographica Section D: Biological Crystallography. DOI: 10.1107/S0907444908028059.
4. (2008).Establishing a training set through the visual analysis of crystallization trials. Part I: ~150 000 images. Acta Crystallographica Section D: Biological Crystallography. DOI: 10.1107/S0907444908028047.

Help Cure Muscular Dystrophy

1. (2019).Decrypting protein surfaces by combining evolution, geometry, and molecular docking. Proteins: Structure, Function, and Bioinformatics. DOI: 10.1002/prot.25757.
2. (2018).Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions. Proteins: Structure, Function, and Bioinformatics. DOI: 10.1002/prot.25506.
3. (2017).Protein social behavior makes a stronger signal for partner identification than surface geometry. Proteins: Structure, Function, and Bioinformatics. DOI: 10.1002/prot.25206.
4. (2016).Great interactions: How binding incorrect partners can teach us about protein recognition and function. Proteins: Structure, Function, and Bioinformatics. DOI: 10.1002/prot.25086.
5. (2013).Protein-Protein Interactions in a Crowded Environment: An Analysis via Cross-Docking Simulations and Evolutionary Information. PLOS Computational Biology. DOI: 10.1371/journal.pcbi.1003369.
6. (2009).From Dedicated Grid to Volunteer Grid: Large Scale Execution of a Bioinformatics Application. Journal of Grid Computing. DOI: 10.1007/s10723-009-9130-7.
7. (2009).Joint Evolutionary Trees: A Large-Scale Method To Predict Protein Interfaces Based on Sequence Sampling. PLOS Computational Biology. DOI: 10.1371/journal.pcbi.1000267.
8. (2008).Identification of Protein Interaction Partners and Protein–Protein Interaction Sites. Journal of Molecular Biology. DOI: 10.1016/j.jmb.2008.08.002.

Help Defeat Cancer

1. (2011).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. DOI: 10.1136/amiajnl-2011-000170.
2. (2010).Grid-Enabled, High-performance Microscopy Image Analysis.
3. (2009).Virtual Microscopy and Grid-Enabled Decision Support for Large-Scale Analysis of Imaged Pathology Specimens. IEEE Transactions on Information Technology in Biomedicine. DOI: 10.1109/TITB.2009.2020159.
4. (2009).PathMiner: A Web-Based Tool for Computer-Assisted Diagnostics in Pathology. IEEE Transactions on Information Technology in Biomedicine. DOI: 10.1109/TITB.2008.2008801.
5. (2008).Therapeutic starvation and autophagy in prostate cancer: A new paradigm for targeting metabolism in cancer therapy. The Prostate. DOI: 10.1002/pros.20837.

Help Fight Childhood Cancer

1. (2016).Effects of novel small compounds targeting TrkB on neuronal cell survival and depression-like behavior. Neurochemistry International. DOI: 10.1016/j.neuint.2016.04.017.
2. (2014).Identification of novel candidate compounds targeting TrkB to induce apoptosis in neuroblastoma. Cancer Medicine. DOI: 10.1002/cam4.175.

Help Stop TB

1. (2019).Revealing solvent-dependent folding behavior of mycolic acids from Mycobacterium tuberculosis by advanced simulation analysis. Journal of Molecular Modeling. DOI: 10.1007/s00894-019-3943-5.

Human Proteome Folding

1. (2012).The mRNA-Bound Proteome and Its Global Occupancy Profile on Protein-Coding Transcripts. Molecular Cell. DOI: 10.1016/j.molcel.2012.05.021.
2. (2012).The Plant Proteome Folding Project: Structure and Positive Selection in Plant Protein Families. Genome Biology and Evolution. DOI: 10.1093/gbe/evs015.
3. (2011).The Proteome Folding Project: Proteome-scale prediction of structure and function. Genome Research. DOI: 10.1101/gr.121475.111.
4. (2008).A Protein Domain-Based Interactome Network for C. elegans Early Embryogenesis. Cell. DOI: 10.1016/j.cell.2008.07.009.
5. (2007).A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell. Cell. DOI: 10.1016/j.cell.2007.10.053.
6. (2007).Superfamily assignments for the yeast proteome through integration of structure prediction with the gene ontology. PLoS biology. DOI: 10.1371/journal.pbio.0050076.
7. (2007).A conserved surface on Toll-like receptor 5 recognizes bacterial flagellin. Journal of Experimental Medicine. DOI: 10.1084/jem.20061400.
8. (2007).BioNetBuilder: automatic integration of biological networks. Bioinformatics. DOI: 10.1093/bioinformatics/btl604.
9. (2005).Genome-wide structural and functional protein characterization by ab initio protein structure prediction. Report / Department of Electrical Measurements. Lund Institute of Technology.

Mapping Cancer Markers

1. (2026).IID 2025: Physical protein interaction data with detection types, co-purified protein sets, molecular docking, and immune cell networks. Nucleic Acids Research.
2. (2022).IID 2021: towards context-specific protein interaction analyses by increased coverage, enhanced annotation and enrichment analysis. Nucleic Acids Research. DOI: 10.1093/nar/gkab1034.
3. (2019).IID 2018 update: context-specific physical protein-protein interactions in human, model organisms and domesticated species. Nucleic Acids Research.
4. (2016).Integrated Interactions Database: tissue-specific view of the human and model organism interactomes. Nucleic Acids Research.
5. (2020).Informed Use of Protein-Protein Interaction Data: A Focus on the Integrated Interactions Database (IID). Methods in Molecular Biology.
6. (2023).USNAP: fast unique dense region detection and its application to lung cancer. Bioinformatics. DOI: 10.1093/bioinformatics/btad477.
7. (2015).Comparative network analysis via differential graphlet communities. Proteomics. DOI: 10.1002/pmic.201400233.
8. (2019).Visualization of Biomedical Networks. Encyclopedia of Bioinformatics and Computational Biology.
9. (2018).The ablation of the matricellular protein EMILIN2 causes defective vascularization due to impaired EGFR-dependent IL-8 production affecting tumor growth. Oncogene. DOI: 10.1038/s41388-017-0107-x.
10. (2018).SDREGION: Fast Spotting of Changing Communities in Biological Networks. KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. DOI: 10.1145/3219819.3219854.
11. (2017).Network Motif Families for Lung Cancer Diagnostics: A World Community Grid Approach.
12. (2015).Prioritizing Therapeutics for Lung Cancer: An Integrative Meta-analysis of Cancer Gene Signatures and Chemogenomic Data. PLOS Computational Biology. DOI: 10.1371/journal.pcbi.1004068.
13. (2015).In silico prediction of physical protein interactions and characterization of interactome orphans. Nature Methods. DOI: 10.1038/nmeth.3178.

Microbiome Immunity Project

1. (2023).Sequence-structure-function relationships in the microbial protein universe. Nature Communications. DOI: 10.1038/s41467-023-37896-w.

Nutritious Rice for the World

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

OpenZika

1. (2022).Discovery of New Zika Protease and Polymerase Inhibitors through the Open Science Collaboration Project OpenZika. Journal of Chemical Information and Modeling. DOI: 10.1021/acs.jcim.2c00596.
2. (2019).A diarylamine derived from anthranilic acid inhibits ZIKV replication. Scientific Reports. DOI: 10.1038/s41598-019-54169-z.
3. (2019).High Throughput and Computational Repurposing for Neglected Diseases. Pharmaceutical Research. DOI: 10.1007/s11095-018-2558-3.
4. (2018).Computational drug discovery for the Zika virus. Brazilian Journal of Pharmaceutical Sciences. DOI: 10.1590/s2175-97902018000001002.
5. (2018).The A–Z of Zika drug discovery. Drug Discovery Today. DOI: 10.1016/j.drudis.2018.06.014.
6. (2017).Molecular dynamics simulations of Zika virus NS3 helicase: Insights into RNA binding site activity. Biochemical and Biophysical Research Communications. DOI: 10.1016/j.bbrc.2017.03.070.
7. (2016).OpenZika: An IBM World Community Grid Project to Accelerate Zika Virus Drug Discovery. PLOS Neglected Tropical Diseases. DOI: 10.1371/journal.pntd.0005023.
8. (2016).Illustrating and homology modeling the proteins of the Zika virus. F1000Research. DOI: 10.12688/f1000research.8213.2.

The Clean Energy Project

1. (2017).Design Principles and Top Non-Fullerene Acceptor Candidates for Organic Photovoltaics. Joule. DOI: 10.1016/j.joule.2017.10.006.
2. (2015).What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery. Annual Review of Materials Research. DOI: 10.1146/annurev-matsci-070214-020823.
3. (2015).Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery. Advanced Functional Materials. DOI: 10.1002/adfm.201501919.
4. (2014).Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project. Energy Environ. Sci.. DOI: 10.1039/C3EE42756K.
5. (2011).Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics. Energy & Environmental Science. DOI: 10.1039/c1ee02056k.
6. (2011).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. DOI: 10.1021/jz200866s.
7. (2011).From computational discovery to experimental characterization of a high hole mobility organic crystal. Nature Communications. DOI: 10.1038/ncomms1451.

See Also

• BOINC
• Volunteer computing
• Folding@home
• SETI@home
• Rosetta@home
• Krembil Research Institute
• University Health Network

External Links

• World Community Grid official website
• Jurisica Lab WCG page
• Krembil Research Institute
• BOINC Publications list (World Community Grid section)
• WCG Completed Projects