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Technology

Pioneering the next generation of drug discovery

Prediction to patient

It isn’t effective to start with a single target in mind.

Rare diseases are often not well studied and there is a limited understanding of many of the aspects necessary to support a drug discovery programme. Our AI platform, Healnet, overcomes these challenges by analysing millions of drug and disease data points to find novel connections that could be turned into new treatment opportunities. By applying frontier technologies across the discovery and development pipeline, we can run multiple stages in parallel and at scale.

 

This isn’t traditional drug discovery

One disease, one target, one drug: it’s an overly simply model, yet it’s the one used by nearly all pharmaceutical companies.

The next-generation of drug discovery is AI-powered, parallel and hypothesis-free. Bringing together the key three drug discovery paradigms.

Our approach

Biomedical literature and other texts

Curated biological disease data

Commercial and biochemical information

Equiv. to
300+
years of reading

100M
high confidence
relationships

1B
extracted
entities

Genetics

Transcriptomics

Orphan drug designations

Compound gene associations

in vitro/ in vivo models

AI-reasoning predictions and knowledge graph to identify novel disease-compound relationships

Drug discovery expert analysis

These predictions are reviewed by our team of experts and progressed to lab testing.

 

Lab testing and
drug-bioactivity relationships

Monotherapies
Combination therapies
Enhanced molecules

Clinical trials

Publications

25 June 2020
AKBC SciNLP Workshop 2020
Data augmented relation extraction (DARE) with GPT-2
Yannis Papanikolaou Andrea Pierleoni
20 April 2020
Reference Module in Biomedical Science
Drug combination modeling
Anna H C Vlot Daniel J Mason Krishna C Bulusu Andreas Bender
1 November 2019
EMNLP DeepLo Workshop 2019
Deep bidirectional transformers for relation extraction without supervision
Yannis Papanikolaou Ian Roberts Andrea Pierleoni
1 November 2019
EMNLP DeepLo Workshop 2019
Reasoning over paths via knowledge base completion
Saatviga Sudhahar Ian Roberts Andrea Pierleoni
1 October 2018
Nature Reviews Drug Discovery
Drug repurposing: progress, challenges and recommendations
Sudeep Pushpakom Francesco Iorio Patrick A. Eyers K. Jane Escott Shirley Hopper Andrew Wells Andrew Doig Tim Guilliams Joanna Latimer Christine McNamee Alan Norris Philippe Sanseau David Cavalla Munir Pirmohamed
1 June 2018
Human Molecular Genetics
The antidepressant tianeptine reverts synaptic AMPA receptor defects caused by deficiency of CDKL5
Marco Tramarin Laura Rusconi Lara Pizzamiglio Isabella Barbiero Diana Peroni Linda Scaramuzza Tim Guilliams David Cavalla Flavia Antonucci Charlotte Kilstrup-Nielsen
1 February 2016
Nature Reviews Drug Discovery
Antibiotic resistance breakers: can repurposed drugs fill the antibiotic discovery void?
David Brown
1 February 2016
Drug discovery today
Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives
Krishna Bulusu Rajarshi Guha Daniel J Mason Rich Lewis Eugene Muratov Yasaman Kalantarmotamedi Murat Cokol Andreas Bender
1 February 2016
Journal of Medicinal Chemistry
Prediction of antibiotic interactions using descriptors derived from compound molecular structure
Daniel J Mason Ian P Stott Stephanie Kay Ashenden Zohar B Weinsteindil Idill Karakoc Selin Meral Nurdan Kuru Andreas Bender Murat Cokol
1 October 2018
Frontiers in Pharmacology
Using machine learning to predict synergistic antimalarial compound combinations with novel structures
Daniel J Mason Richard T Eastman Rich Lewis Ian P Stott Rajarshi Guha Andreas Bender
1 March 2019
Neuropharmacology
Repurposing available drugs for neurodevelopmental disorders: the fragile X experience
Michael R Tranfaglia Clare Thibodeaux Daniel J Mason David Brown Ian Roberts Tim Guilliams Patricia Cogram
30 August 2018
Bioinformatics
Understanding and predicting disease relationships through similarity fusion
Erin Oerton Ian Roberts Patrick S H Lewis Tim Guilliams Andreas Bender