Expanding the frontiers of synthetic lethality

Better molecules, by design

Discover the platformDiscover the pipeline
What we do

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 significantly increase the chance of clinical success. At Evariste, we apply our platform to identify novel targets and new biomarkers for known targets by integrating multi-modal data using AI-driven approaches.

Our proprietary algorithms are able to parse through large noisy multi-omic biological and chemical datasets – including proprietary and public data – and discover novel, druggable, and mechanistically actionable targets.

We have chosen to focus our work on identifying novel synthetic lethal relationships, exploiting an untapped resource in oncology.

Targets discovered by Evariste are validated in disease-relevant models to ensure their clinical applicability, and patient populations are deduced to determine which groups will benefit the most.

We have already found multiple completely novel synthetic lethal targets that have received early in vitro validation, as well as novel biomarkers for clinically validated targets that provide differentiated patient populations.

Frobenius Discovery

We begin by using various hit finding techniques ranging from trillion compound virtual screens to covalent fragments to identify novel hits, including for previously undrugged target families.

Our proprietary algorithms massively accelerate the identification of the most promising hit matter and downstream development. In contrast to conventional deep learning approaches, our machine learning models excel at working with small, noisy datasets.

After finding successful hits, we design and score novel small molecules on a scale beyond human capability.

We use probabilistic modeling for multi-parameter optimization across potency, selectivity, and pharmacokinetics. This allows us to prioritize drug candidates for testing and thus de-risks preclinical discovery. 

Combining wet-lab and in silico data, we generate novel structures and use statistical techniques to prioritize candidates, achieving a 10-fold potency increase for every 30 compounds tested.

Our platform efficiently designs synthetically accessible molecules, achieving best-in-class potency and selectivity in under 50 compounds for one of our projects. 


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


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 a potential for a significantly expanded and differentiated patient population, and designed novel inhibitors with high potency and selectivity.

Novel Target 1

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.

Novel Target 2

Undisclosed kinase target for CNS tumors. We have identified and validated this synthetic lethal relationship as a highly ranked target for glioblastoma and neuroblastoma. There are no clinical competitors for this target, and we have designed potent inhibitors optimized for CNS permeability.

Novel SL Biomarker
Novel Target 1
Haem Malignancies
Novel Target 2
CNS Tumors
10+ Synthetic Lethals
Multiple Indications

We are an exceptional team with expertise spanning drug discovery, mathematics and AI

Join us

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.

Anna Hercot
Anna Hercot

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
Alfie Brennan

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
Oliver Vipond

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
Daniel Miller

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
Jan Lanz

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
Tracy Mak

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
James Aaronson

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
Noah Harrison

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
Florencia Skuras

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.

Victor Kleshnev
Victor Kleshnev
Board of Directors

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.

Mike Waring

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.

Olivia Rossanese

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

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.

Xavier Jacq

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

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

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