Expanding the frontiers of synthetic lethality
Better molecules, by design
Evariste is an AI-enabled Drug Discovery company
We develop small molecule therapeutics targeting
synthetic lethal pathways in oncology
Novel Targets
We discover novel synthetic lethal targets and biomarkers, tailoring treatment to underserved patients in an industry fixated on the same old approaches
Better Molecules
Our automated modeling designs high quality, differentiated small molecule candidates at unprecedented scale, speed, and efficiency
Frobenius is Evariste's AI drug discovery platform.
Using Frobenius, we de-risk and progress projects with unrivaled efficiency.
Each stage of our platform has been validated in challenging internal and collaborative projects
Frobenius Target
Identification of Novel Synthetic Lethal Targets and Biomarkers
We integrate multi-modal data to identify novel drug targets and biomarkers, particularly focusing on synthetic lethal relationships in oncology.
Our proprietary algorithms analyze large multi-omic datasets to uncover actionable targets, which are validated in disease-relevant models to ensure clinical relevance and guide patient stratification.
This approach has led to the discovery of multiple novel synthetic lethal targets with early validation and biomarkers for existing targets, alongside the creation of a clinically-annotated dataset for cancers with high unmet need, paving the way for next-generation therapies.
Frobenius Discovery
Small Molecule Drug Design
We start by combining advanced hit-finding techniques, from trillion-compound virtual screens to covalent fragments, with proprietary algorithms to identify novel hits, including for undrugged target families.
Our machine learning models excel at working with small, noisy datasets, enabling efficient design and scoring of small molecules at an unparalleled scale. After finding initial hits, we use probabilistic modeling for multi-parameter optimization across potency, selectivity, and pharmacokinetics to prioritize drug candidates for testing to minimize preclinical risks.
This integrated approach, leveraging both wet-lab and in silico data, has achieved best-in-class potency and selectivity in fewer than 50 compounds, delivering a roughly 10-fold potency increase for every 30 compounds tested.
Using Frobenius, we have developed a pipeline of precision oncology therapeutics
Our pipeline features best-in-class inhibitors of validated synthetic lethal targets expanded to new indications, and first-in-class inhibitors of novel targets
Discovery
Optimization
Enabling
PKMYT1 inhibitor
Targeting PKMYT1 drives synthetic lethality in cancer cells with high replication stress. We have identified and validated a novel biomarker for PKMYT1 with an expanded and differentiated patient population, and nominated a development candidate that is well-tolerated and efficacious in multiple relevant tumor models.
VRK1 inhibitor
VRK1 inhibition is synthetic lethal with VRK2 under-expression. We have identified and validated this synthetic lethal relationship, implicating VRK1 as a highly ranked target in glioblastoma and neuroblastoma. There are no clinical competitors for this target, and we have designed potent inhibitors optimized for CNS permeability.
Novel SL Target
Undisclosed metabolic enzyme target for heavily pretreated hematological malignancies. We have identified a new synthetic lethal target present in a subset of acute myeloid leukemia and diffuse large B-cell lymphoma patients, as well as in HR-deficient solid tumors. We have designed the first ever inhibitors for this target.
We are an exceptional team with expertise spanning drug discovery, mathematics, and AI
Jan received a PhD in Medicinal Chemistry from the University of Southern Denmark, working on AMPK modulators. He then joined the Institute of Cancer Research, optimizing inhibitors of a kinesin motor protein and subsequently worked on the development of antibody-drug conjugates at AstraZeneca.
Jan Lanz
PRINCIPAL SCIENTIST CHEMISTRY
Tracy has a PhD in Endocrinology from the University of Edinburgh and a MBA from Quantic School of Business and Technology. After the exciting field of AI enticed her from academia, Tracy worked at BenevolentAI in operations and programme management. She uses her scientific and business background to implement Evariste’s business goals.
Tracy Mak
DIRECTOR OF OPERATIONS
James graduated from his Mathematics doctorate from Magdalen College, University of Oxford, where he studied combinatorics, focusing on problems with an arithmetic flavor. James applies these techniques to solve combinatorial and statistical problems in drug discovery.
James Aaronson
QUANT ANALYST
Noah is a graduate of the University of Oxford where he received a MSc in Biochemistry. His research focused on molecular dynamic simulations of mitochondrial supercomplexes and their interactions with lipid molecules. Since joining Evariste Noah has been developing the structure-based virtual screening pipeline, enriching the ability of Evariste's Hit Discovery platform.
Noah Harrison
CHEMOINFORMATICIAN
We are advised by a board of world-class scientists and visionary leaders, each bringing unparalleled expertise and commitment to Evariste
Zoë Walters is a lecturer in translational epigenomics in the school of Cancer Sciences and is a Module Lead on the MSc Genomics course within the Faculty of Medicine at the University of Southampton. Zoë is a highly experienced cancer biologist whose expertise lies in target identification and validation in cancer and developmental disorders. Zoë has 18+ years' experience in molecular genetics, developmental biology, and cancer biology.
Zoë Walters
SCIENTIFIC DIRECTOR
Nicholas Firth studied for his PhD in Chemoinformatics at the Institute of Cancer Research, where he developed de novo design software that implemented a fragment-based approach to explore chemical space. Nick has a deep understanding of many computational chemistry algorithms, feature engineering and machine learning for molecules.
Nicholas Firth
DIRECTOR
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