Cloud-based structural biology and drug discovery training module on google cloud platform
Bibliographic Data
| ID | 22167589 |
|---|---|
| Authors | Ojasvi Dutta (Louisiana State University), Sonalika Ray (0000-0002-4433-6046, Louisiana State University), Magesh Rajasekaran (0000-0001-9022-2157, Louisiana State University), Prajesh Shrestha (0000-0002-5468-1900, Louisiana State University), Kyle O’Connell (Biomedical Data Science Lab, Deloitte Consulting LLP), Kyle O'Connell (Deloitte (United States)), Logan Cole (Deloitte (United States)), Jonathan Pinney (Biomedical Data Science Lab, Deloitte Consulting LLP), J E Pinney (Deloitte (United States)), Konstantin G Kousoulas (Louisiana State University), Seetharama D Jois (Louisiana State University) |
| Year | 2026 |
| Volume | 11 |
| Publication date | 2026-07-07 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Education (JOURNAL) |
| Journal identifiers | ISSN: 2504-284X • E-ISSN: 2504-284X |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/feduc.2026.1837315 |
| OpenAlex | W7167574613 |
| Language | EN |
| References cited | 27 |
Training in structural biology and structure-based drug discovery commonly requires specialized software, local high-performance computing resources, and a complex technical setup - conditions that exclude many learners. We present a cloud-based Structural Biology and Drug Discovery curriculum implemented on the National Institute of General Medical Sciences (NIGMS) Sandbox platform, giving users browser-native access to research-grade computational workflows without local installation. The course is organized into four modular subunits covering protein structure analysis, protein-ligand docking, protein-protein interactions, and structure-guided drug design, delivered through interactive Jupyter notebooks running on Google Cloud's Vertex AI Workbench and integrate widely used tools including AlphaFold, PyMOL, AutoDock, and ClusPro. Pilot testing with graduate and postgraduate learners preceded two live workshops with 139 total registrants. Both workshops demonstrated reliable deployment of the cloud-based environment, and participant surveys indicated high satisfaction across clarity of instruction, relevance of activities, and technical robustness (mean scores approximately 4.1–4.4 on a 5-point scale). Together, these results show that cloud-native, reproducible computational environments can substantially reduce the technical barriers that limit access to structural biology training, making such curricula scalable across institutions and equipping learners with the computational skills now expected in modern drug discovery research
Cheminformatics · Cloud computing · Grand Challenges · Modular design · Scalability · Structural bioinformatics · Workbench · Workflow · Computational Drug Discovery Methods · Genetics, Bioinformatics, and Biomedical Research · Scientific Computing and Data Management · Drug Discovery
| Highly cited | No |
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