Insilico Medicine now has an
Our mission is to accelerate drug discovery and development by leveraging our rapidly evolving, proprietary PHARMA.AI platform across biology, chemistry and clinical development. By utilising our platform and collaborating with academic partners, we strive to expedite and enhance drug research and development, reducing the cost and time needed to provide life-saving medications to patients.

Due to an overwhelming number of requests from external lab partners, we have created a process for us to evaluate potential academic grant partners, including the form on this page. In these applications, we look for innovative projects that could use the PHARMA.AI platform for target and biomarker identification, generative drug design and/or clinical trial design with the goal of uncovering basic biological principles underlying disease and developing safe and efficacious therapeutics.
We are looking for projects that will achieve the following objectives:
Demonstrate how the tool can be used
to support drug development
(in terms of speed, accuracy, novelty, and/or efficacy)
in any of the following domains
Accelerate the pathway from primary research data to pipeline nomination
Enhance the quality and timeliness of the data supporting target selection
Validate the efficacy of drugs designed using generative AI
Demonstrate the use of AI to improve the likelihood of clinical success
Collaboration results can be demonstrated by co-authored publication
Ideally, the proposal will
  • Work on a specific problem that has been identified as a priority in a country or region
  • Contribute to the body of evidence related to AI use across health, gender, financial inclusion, agriculture, and educational goals
  • Enable an improved understanding of the potential of AI for drug discovery and development
Priority will be given to proposals that have
  • been comprehensively written and are near-ready to submit
  • an explicit request for project where AI is a key component
  • Those projects that are supported by pilot omics data
    (e.g. RNA-seq, microarray, proteomics data)
  • Projects that have lessons/tools that can be transferred to other use cases / situations/ contexts with minimal change
  • 1
    Proposals that present a high leverage and scalability opportunity
  • 2
    Proposals that can easily be adapted or modified either across 1) Geographies or 2) Fields
  • 3
    Proposals that outline a clear, feasible and reproducible methodology
  • 4
    Proposals that focus on identifying risks to responsible use and develop ways to mitigate those risks
  • 5
    Proposals that have timely access to data, decision-maker time and interest in using AI
  • 6
    Proposals that articulate how the project will lead to impact in the near-term and how those benefits will be sustained past the lifetime of the project
  • 7
    Proposals that are driven by a shared commitment to open science, data sharing, and building collaboration and analysis infrastructure to enable discoveries that will benefit people everywhere
We will not consider funding
for proposals that:
  • Do not explicitly use/reference the use of AI and the Pharma.AI platform (PandaOmics, Chemistry42, and/or inClinico) in their project execution
  • Do not have timely access to necessary data, decision-maker time, commitment, and interest
  • Have a weak methodology
  • Have no or weak validation plan
If you have questions regarding the process of co-applying with Insilico Medicine for academic grant, please contact
  • Morten Schneybe-Knudsen, MD, PhD
    University of Copenhagen
    Insilico Medicine has been a crucial partner in the advancement of our research in my lab. As a scientist focused on understanding the cellular and organismal consequences of DNA damage, our ongoing projects are at the cutting edge of developing interventions for age-associated diseases. Through the utilization of Insilico Medicine's advanced artificial intelligence, we have been able to delve into intricate datasets to uncover previously unidentified anti-cancer and anti-aging targets and mechanisms. This has significantly broadened our understanding of how persistent DNA damage contributes to the aging process and how small molecules may be able to alter these foci.
    Our collaborative efforts have resulted in numerous high-impact publications in esteemed scientific journals, with more underway. The synergy between our teams, with Insilico Medicine valuing our expertise and feedback, has been exceptional. I wholeheartedly endorse other researchers to collaborate with Insilico Medicine, as it enhances groundbreaking research with AI-driven insights. Together, we are shaping the future of aging research and generating the knowledge that can provide hope to individuals worldwide, with the aim of enabling everyone to live healthier, happier, and more productive lives.
  • Evgenyi Izumchenko, PhD
    University of Chicago
    Insilico Medicine has played an important role in the evolution of my research. As a cancer biologist, our ongoing projects have been at the forefront of precision medicine, aiming to discover the drivers of head and neck cancer progression and using that information to inform therapeutic drug target selections. By harnessing Insilico Medicine's state-of-the-art artificial intelligence, we delved into complex datasets to identify previously unknown biomarkers and therapeutic targets, expanding our understanding of drug resistance mechanisms. Our joint efforts resulted in multiple high-impact publications in respected scientific journals, with more in progress. The collaborative atmosphere, with Insilico Medicine valuing my expertise and feedback, has been exemplary. I wholeheartedly recommend other researchers to collaborate with Insilico Medicine, as it empowers cutting-edge research with AI-driven insights. Together, we are shaping the future of cancer research and generating the knowledge that can provide hope to patients and scientists worldwide.
  • Alessandro Luciani, PhD
    University of Zurich
    Delivering state-of-the-art artificial intelligence technology may transform the way the therapeutics are discovered while cutting costs and development time, thereby mitigating risks in several key aspects of preclinical drug discovery at academic centres. With the power of PandaOmics target discovery artificial intelligence, we were able to identify, rank, and annotate novel drug targets for treating dysregulated homeostasis in cystinosis patients.
    Loss-of-function mutations in CTNS cause cystinosis, a lysosomal storage disease characterized by loss of differentiation and dysfunction of the tubular cells, causing life- threatening complications and chronic kidney disease/CKD. Treatment of cystinosis patients with cysteamine is limited by side effects and poor tolerance and it does not alleviate PT dysfunction. Therefore, there is an urgent need to identify novel transformative therapies for young children with cystinosis. By synergizing PandaOmics target discovery artificial intelligence with a cross-species screening and validation workflow, we catalysed the discovery of evidence-based therapeutic interventions for children living with cystinosis, accelerating their effective translation to the clinic.
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