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
Biomarker-led approaches to target discovery greatly enhance the chances of clinical success. At Evariste, we use our platform to identify novel targets and new biomarkers for known targets by integrating multi-modal data with AI-driven techniques. Our proprietary algorithms sift through large, noisy multi-omic datasets — including both proprietary and public data — to uncover novel, druggable, and actionable targets.
The platform is ideally suited to identify synthetic lethal relationships, an untapped resource in oncology. Targets discovered by Evariste are validated in disease-relevant models to ensure clinical relevance, while patient populations are identified to determine who benefits most. So far, we've found multiple novel synthetic lethal targets with early in vitro validation, as well as biomarkers for clinically validated targets that help differentiate patient populations.
Frobenius Discovery
We start by using various hit-finding techniques, from trillion-compound virtual screens to covalent fragments, to identify novel hits, including for previously undrugged target families. Our proprietary algorithms accelerate the identification of promising hits and streamline downstream development. Unlike conventional deep learning, our machine learning models excel with small, noisy datasets.
After identifying successful hits, we design and score novel indication-agnostic small molecules at a scale beyond human capability. Using probabilistic modeling for multi-parameter optimization across potency, selectivity, and pharmacokinetics, we prioritize drug candidates for testing and reduce preclinical risks.
By combining wet-lab and in silico data, we generate new structures and use statistical techniques to prioritize candidates, achieving a 10-fold potency increase with just 30 compounds tested. Our platform efficiently designs synthetically accessible molecules, reaching best-in-class potency and selectivity in under 50 compounds for one project.
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
PKMYT1 inhibitor
Targeting PKMYT1 drives synthetic lethality in cancer cells with high replication stress. We have identified and validated a novel biomarker for PKMYT1 inhibition sensitivity, with an expanded and differentiated patient population, and designed novel, inhibitors with high potency, selectivity and in vivo efficacy.
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 inhibitor
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.
Discovery
Optimization
Enabling
We are an exceptional team with expertise spanning drug discovery, mathematics and AI
Anna graduated pre-med from Northwestern University as a neuroscience major. Post pre-med, she studied computer science. She previously helped businesses implement digital tools at a tech consulting company. She now applies her background knowledge in both computer science, business and science to run Evariste.
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Anna Hercot
CHIEF EXECUTIVE OFFICER
Alfie received a PhD in Medicinal Chemistry from Newcastle University, where he worked on fragment based drug discovery. He then moved to the Institute of Cancer Research, working on lead optimization of PPI inhibitors. Alfie has worked on a variety of targets including kinases, bromodomains, sulfatases, PPIs, and E3 ligases.
Alfie Brennan
CHIEF SCIENTIFIC OFFICER
Oliver studied for his Mathematics PhD at Oxford. He worked on developing new data analytic tools to describe the topology and geometry of data sets with interesting spatial structure, as well as the asymptotic topological properties of random simplicial complexes. Oliver develops statistically robust models applied to drug discovery.
Oliver Vipond
HEAD QUANT
Daniel received a PhD in Cell and Molecular Biology from the Francis Crick Institute, studying the signalling dynamics of the TGF-β family, before working with AstraZeneca to develop therapeutic antibodies. He then worked at the Institute of Cancer Research, where his work focused on understanding the mechanism of action of molecular glue-like small molecules.
Daniel Miller
PRINCIPAL SCIENTIST BIOLOGY
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.
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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.
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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
Florencia has a MSc in Genomics Informatics where she worked on identifying and validating novel therapeutic targets for Oesophageal Adenocarcinoma. She has conducted studies in oncology, infectious diseases, & clinical genetics. Florencia aims to identify novel druggable targets in areas of unmet clinical need.
Florencia Skuras
BIOINFORMATICIAN
Victor is a recent graduate from Harvard University, where he earned his Bachelor's degree in Chemistry. His final-year research involved a molecular glue synthesis project that was aided by computational drug discovery tools. Combining his foundational scientific knowledge with prior business management experience, Victor helps drive the day-to-day operations at Evariste.
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Victor Kleshnev
OPERATIONS ANALYST
We are advised by a board of world-class scientists and visionary leaders, each bringing unparalleled expertise and commitment to Evariste
Mike Waring is Chair of Medicinal Chemistry at Newcastle University and Head of Chemistry for the Cancer Research UK Newcastle Drug Discovery Unit and Director of the EPSRC Centre for Doctoral Training in Molecular Sciences. He was Principal Scientist in Medicinal Chemistry at AstraZeneca, where he led the TagrissoⓇ chemistry teams. He is a highly experienced medicinal chemist with a track record of delivering drug-discovery projects through the clinic.
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Mike Waring
SCIENTIFIC DIRECTOR
Olivia Rossanese is Director of Drug Discovery and Head of Division for Cancer Therapeutics at the Institute of Cancer Research (ICR), and was previously a member of GSK’s TafinlarⓇ discovery team. Olivia has extensive experience leading and contributing to discovery and target validation programmes, as well as identification of tool molecules, lead compounds, clinical candidates.
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Olivia Rossanese
SCIENTIFIC DIRECTOR
Ollie Watson received his PhD in Number Theory from University of Pennsylvania and has since worked in finance at D. E. Shaw and Tudor Capital, where he led a quantitative research team. He is an expert on optimization and statistical modeling in low signal-to-noise environments, and has taught courses on fitting predictive Bayesian models on noisy data.
Oliver Watson
CHAIRMAN OF THE BOARD
Xavier Jacq currently serves as the CSO of Moa Technology. He was Co-founder of Mission Therapeutics, former VP of Biology at Almac Discovery, and former SVP at Dunad Therapeutics. With over 20 years of experience in the biotech industry, Xavier has been instrumental in driving innovative research and development in the field of therapeutic discovery.
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Xavier Jacq
SCIENTIFIC DIRECTOR
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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