About us
Syntensor provides mechanistic insights into drug efficacy and toxicity, enabling informed decision-making in drug development and related fields.
Mechanistic understanding drives progress in engineering fields
In aerospace and chip design, tools like ANSYS Fluent and Cadence Virtuoso provide detailed visualizations and mechanistic insights. Biology lacks equivalent tools for drug development and clinical decision-making.
Our mission: Transform medicine through mechanistic biological insights
Our platform offers detailed explanations of cellular mechanisms (SMoA) that drive drug efficacy and toxicity.
Solving the 'complexity problem' in biology
We're building towards foundation models for genomics and chemical perturbation of cells. We take a dynamical systems view towards providing a mechanistic understanding of cells.
A general platform for novel pharmaceutical assets.
Our generalizable platform allows scientists to:
- Simulate drug effects across cell lines
- Visualize mechanisms of action
- Identify biological indicators of efficacy and toxicity
The team
We’re a multi-disciplinary team of ML Researchers, Drug Discoverers, Software Engineers and Product Designers building a platform to predict drug efficacy and toxicity: a transformative tool for drug development and precision prescriptions.
Clayton Rabideau
Cofounder and CEO/CTO
Syntensor is the result of Clayton’s life-long passion for algorithms and biology. After an undergraduate degree in genetics, Clayton’s PhD research was devoted to computational synthetic biology. He launched Syntensor out of Cambridge University’s entrepreneurial ecosystem.
Rosie Higgins
Cofounder and Advisor
Rosie is a product leader who specializes in building multidisciplinary teams to bring emerging technologies to market. Previously, she has been VP at Novartis, Data42 and COO at BenevolentAI, and prior to her move into biotech, built and scaled global brands at London-based startups.
Gabrielle Griffin, PhD
VP, Bioinformatics and Product
Before joining Syntensor, Gabi was Director of Bioinformatics at Sumitovant Biopharma and Lead Bioinformatics and Drug Discovery Scientist at BenevolentAI where she was technical lead for their partnership with AstraZeneca.
Josiah Bjorgaard, PhD
VP, ML Engineering
Experienced in leading the development of new software using emerging technologies, he held technical roles at AWS and Cray/HPE prior to joining Syntensor. Josiah works at the intersection of machine learning, high performance computing, and big data for applications in chemistry and biology. He has a background in computational and physical chemistry with extensive research in atomistic modeling.
Callum Birch-Sykes, PhD
ML Researcher
Previously as a particle physics researcher at CERN, Callum developed GNN models to search for hypothetical Higgs bosons at the LHC. He now applies his background in geometric ML to the biological domain as a researcher at Syntensor.
Annija Jekale
VP Operations
After her MPhil (Management) at the University of Cambridge, Anni started her career as a Wealth Analyst in the city. At Syntensor, she applies her diverse ops toolkit with precision, while wearing many hats.
Maksym Korablyov
ML Ops + Engineering Contributor
Maksym is a PhD candidate in Yoshua Bengio’s lab at MILA where he is building large scale generative models. Prior to MILA, he attended MIT and was a Harvard Medical School Visiting Fulbright Scholar. He holds a Masters in Bioinformatics and studied Genetics as an undergraduate.
Michael Poli
ML Research Contributor
Michael is a PhD candidate at Stanford working at the intersection of deep learning, generative models, numerical methods and control of dynamical systems. He cofounded DiffeqML with Stefano Massaroli and is a core-maintainer of the Torchdyn library for neural differential equations.
Stefano Massaroli
ML Research Contributor
Stefano is a post doc in Yoshua Bengio’s lab at MILA working in deep learning, dynamical systems, optimization & control. He cofounded DiffeqML with Michael Poli and is a core-maintainer of the Torchdyn library for neural differential equations.
