Expanding the frontiers of drug discovery

Bettermolecules, by design

Discover the platform
Who we are

Evariste is an AI-driven Drug Discovery company.

Purpose

Design successful therapies for every patient

Approach

Automate every stage of drug discovery

Reach

Scalable platform to maximize output

The Evariste Platform

Our AI platforms find novel hits and design candidates with higher clinical success rates.

Target Identification

The Evariste Target ID Platform combines AI modeling with expert curation to identify fully-rationalized, novel targets in well-defined patient populations. The platform amalgamates large multiomic biological and chemical datasets, including proprietary and public data. Targets are validated in disease-relevant models to ensure their clinical applicability. We identify specific predictive biomarkers or novel synthetic lethalities to ensure that the right patients will receive our treatments.

Hit Discovery

The Evariste Hit Discovery platform searches ultra large libraries for hit-like chemical matter using statistically robust proprietary scoring functions. Probabilistic algorithms exploit the structure of combinatorial libraries, and facilitate virtual screening of libraries consisting of trillions of diverse compounds. The output of our screen is a diverse set of compounds in energetically favorable conformations hyper-enriched in key protein-ligand interactions.

Hit-to-Candidate

The Evariste Hit-to-Candidate platform designs and scores synthetically accessible molecules on a scale that would not be feasible for a team of chemists. Probabilistic modeling allows for principled combination of multiple endpoints, facilitating multi-parameter optimization. Our machine learning models are ideally suited to drug discovery as they are input agnostic and designed to work on small, noisy datasets.

In numbers

Since our inception

Ongoing Projects

Evariste’s highly scalable, end-to-end platform allows projects to be rapidly onboarded and excised.

8

Screening Time

Evariste’s Hit ID platform searches larger libraries at unprecedented speed, screening trillions of compounds in less than 24 hours.

24hrs

Potency increase

Evariste’s Hit-to-Candidate platform designs and scores thousands of synthetically accessibly molecules in minutes, increasing potency by at least 10x every 30 compounds tested and optimising multiple ADME endpoints.

10×
The Evariste Pipeline

Our internal projects

Meet our team

Join us

Oliver Watson

FOUNDER & CEO

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.

Olivier Waston

Founder / CEO

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

FOUNDER & CEO

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.

Nicholas Firth

CO-FOUNDER & DIRECTOR

Nicholas Firth studied for his PhD in Chemoinformatics at the Institute of Cancer Research, where he developed de novo design software. The software he developed uses 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.

Anna Hercot

CHIEF OPERATING OFFICER

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 and science to run Evariste's operations.

Zoë Walters

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 within the Faculty of Medicine at 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.

Mike Waring

SCIENTIFIC DIRECTOR

Mike Waring holds the Chair of Medicinal Chemistry at Newcastle University, he is 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. He is a highly experienced medicinal chemist with a track record of delivering drug-discovery projects through the clinic.

Charles Jaskel

STRATEGIC ADVISOR

Charles has a finance background with a focus on investing, fundraising, and building technology focused businesses. This experience was gained during his tenure at Silver Lake Partners and more recently at QuantPort, a multi-billion dollar quantitative fund. His expertise covers investment and business development, from deal-structuring through to recruiting raw talent.

Alfie Brennan

PRINCIPAL SCIENTIST CHEMISTRY

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.

Oliver Vipond

PRINCIPAL QUANT

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.

Daniel Miller

PRINCIPAL SCIENTIST BIOLOGY

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.

Noah Harrison

CHEMOINFORMATICIAN

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.

James Aaronson

QUANT ANALYST

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.

Florencia Skuras

BIOINFORMATICIAN

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.‍

Jan Lanz

PRINCIPAL SCIENTIST CHEMISTRY

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.

Partnerships

The Evariste Hit-to-Candidate platform designs and scores synthetically accessible molecules on a scale that would not be feasible for a team of chemists. Probabilistic modeling allows for principled combination of multiple endpoints, facilitating multi-parameter optimization. Our machine learning models are ideally suited to drug discovery as they are input agnostic and designed to work on small, noisy datasets.

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