
Advantages of Cloud Infrastructure for Biotech
The biotechnology and pharmaceutical industries are overwhelmed with an increasing amount of diverse data. Startups commonly struggle to analyze vast amounts of genomic, transcriptomic, and other types of data using local IT infrastructure. The challenges of distributed teams, data sharing, and accessing compute resources emphasize these issues. Moreover, constantly evolving industry compliance standards can create additional hurdles for both startups and established companies. The solution? Transitioning to a cloud-based infrastructure. Cloud services offer scalability, adaptability, and efficiency, which are critical in today’s fast-paced biotech environment. Cloud-based workspaces, such as JupyterLab Notebooks or R Studio, empower teams to collaboratively extract insights from data using Bioinformatics, Artificial Intelligence (AI), and Machine Learning (ML) tools. Transitioning to the cloud may seem daunting, but the benefits far outweigh the initial challenges. For those aiming to leverage the latest AI and ML capabilities, cloud infrastructure is essential. It allows for automated workflows, enhanced collaboration, and adherence to evolving compliance standards.
Cloud Onboarding Made Simple
One of the main obstacles biotech and pharma companies face in cloud adoption is transitioning from their existing IT infrastructure. Startups, in particular, may lack the resources and expertise to optimize the setup for their unique needs. A properly configured cloud environment is secure, user-friendly, and equipped with essential features like regulatory and compliance frameworks.
Our One-Week Cloud Onboarding service rapidly equips biotech organizations with the tools and infrastructure needed to start collecting and analyzing data, enabling AI and machine learning integration within just one week. This streamlined process ensures a seamless transition to a secure, scalable cloud environment tailored for bioinformatics and data science workflows. With expert guidance and robust support, your team will be fully operational and AI-ready in record time.
How Does Cloud Onboarding for Biotech Work?
- Accelerate data processing: Reduce timelines by an order of magnitude by using Nextflow pipelines.
- Rapid decision-making: Shorten the time for data to reach decision-makers outside the lab from weeks to minutes.
- Optimized environments: Enable bioinformaticians to work in preferred environments (Jupyter Lab or RStudio) within minutes.
- Overcome hardware limitations: Eliminate local PC hardware constraints within minutes.

What You Can Achieve with Cloud-Based Infrastructure
-Bioinformatics on the Cloud
For companies needing to analyze large datasets, such as genomic or transcriptomic data, we set up cloud-based workspaces in RStudio and pipelines in Nextflow. This setup streamlines bioinformatics data processing, automates multi-step analyses, and facilitates collaboration, empowering bioinformaticians in an interactive environment.
-Cloud-Based AI Model Training Platform for Biotech
For organizations looking to integrate AI and ML into their workflows, we create an interactive Jupyter Notebook workspace where researchers can train and evaluate models. We also establish code repositories and a continuous development environment, enabling data science teams to write and manage code efficiently.
-Cloud Security and Compliance for Biotech and Pharma
We prioritize security and regulatory compliance, ensuring that your cloud environment is both secure and accessible. Our services include integrating user management with your preferred Single Sign-On provider and supporting adherence to evolving regulatory standards.
-Budget Notifications
Cloud services can quickly become costly if not properly managed. We help with controlling expenses by setting up budget notifications, ensuring you stay within your financial limits.
Accelerate Biotech Innovation with Cloud Solutions
If you are looking to accelerate biotech innovation, Ardigen can help you make a seamless transition to a cloud-based data infrastructure by providing the support you need to start collaborating effectively. This isn’t just about moving to the cloud—it’s about unlocking the full potential of your data and workflows.
In addition to our One-Week Cloud Onboarding, Ardigen offers a wide range of services across the entire drug discovery pipeline—from data tools to scientific insights—to increase the success rate of drug discovery. As your company grows, we provide data lakehouse solutions and workflow automation, along with training for data scientists, bioinformaticians, and wet lab staff.
Read more here!
Works Cited:
- Swinney, David C., and Jonathan A. Lee. “Recent advances in phenotypic drug discovery.” F1000Research9 (2020). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431967/
- Swinney, David C., and Jason Anthony. “How were new medicines discovered?.” Nature reviews Drug discovery10.7 (2011): 507-519. https://www.nature.com/articles/nrd3480
- Haasen, Dorothea, et al. “How phenotypic screening influenced drug discovery: lessons from five years of practice.” Assay and drug development technologies15.6 (2017): 239-246. https://www.liebertpub.com/doi/abs/10.1089/adt.2017.796
- Vincent, Fabien, et al. “Phenotypic drug discovery: recent successes, lessons learned and new directions.” Nature Reviews Drug Discovery21.12 (2022): 899-914. https://www.nature.com/articles/s41573-022-00472-w
- Wells, Elizabeth, et al. “Vamorolone, a dissociative steroidal compound, reduces pro-inflammatory cytokine expression in glioma cells and increases activity and survival in a murine model of cortical tumor.” Oncotarget8.6 (2017): 9366. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5354737/
- Ratni, Hasane, et al. “Discovery of risdiplam, a selective survival of motor neuron-2 (SMN2) gene splicing modifier for the treatment of spinal muscular atrophy (SMA).” (2018): 6501-6517. https://pubs.acs.org/doi/full/10.1021/acs.jmedchem.8b00741
- Belema, Makonen, and Nicholas A. Meanwell. “Discovery of daclatasvir, a pan-genotypic hepatitis C virus NS5A replication complex inhibitor with potent clinical effect.” (2014): 5057-5071. https://pubs.acs.org/doi/full/10.1021/jm500335h
- Van Goor, Fredrick, et al. “Correction of the F508del-CFTR protein processing defect in vitro by the investigational drug VX-809.” PNAS108.46 (2011): 18843-18848. https://www.pnas.org/doi/abs/10.1073/pnas.1105787108
- Hanada, Takahisa. “The discovery and development of perampanel for the treatment of epilepsy.” Expert opinion on drug discovery 9.4 (2014): 449-458. https://www.tandfonline.com/doi/full/10.1517/17460441.2014.891580