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Artificial Intelligence for drug discoveryGTC Europe 2017
10 October 2017
Dean Plumbley
Senior Machine Learning Scientist
Table of contents
1. About us
2. Hypothosis Generation
3. Current Success!
4. Drug Design using AI
5. AutoChem – Chemical Property
Prediction
6. EvoChem – Drug generation
7. Looking Ahead
We are
Since our foundation in 2013, our mission has
been to bring together the best of technology
and scientific research to enable us to create
better medicines.
BenevolentAI harnesses artificial intelligence to
enhance and accelerate scientific discovery by
making sense of highly fragmented information
to create new insights and usable knowledge
that benefit society.
BenevolentAI Overview
Founded in 2013
$BN company
80+ team of world class scientists and technologists (50+
doctorates/advanced degrees)
Technology enabling previously impossible tasks in bioscience
Rich patent portfolio of over 400 patents
10/10/2017 4
Drug Discovery is broken!
10/10/2017 5
BenevolentAI is challenging traditional approaches
10/10/2017 6
Proprietary Knowledge and Inference Models
10/10/2017 7
BenevolentAI finds potential treatments for ALS
10/10/2017 8
AI driven compound design
Overview of AI driven compound design
10/10/2017 10
Drug design using AI
10/10/2017 11
MoleculeNet: A Benchmark for Molecular Machine Learning Wu et al, arXiv
2017, https://arxiv.org/abs/1703.00564
Property Prediction
Molecule Generation
AutoChemDrug property prediction
QSAR modelling
10/10/2017 13
Drug structures as SMILES Representation as fingerprints /
physchem descriptors
QSAR modelling
10/10/2017 14
Low Data Drug Discovery with One-shot Learning Altae-Tran
et al, 2016, https://arxiv.org/abs/1611.03199
AutoChem – The system
10/10/2017 15
EvoChemGenerative Chemistry
Bespoke molecule generation
• Multiparameter optimisation of drugs is hard!
• Can AI help?
10/10/2017 17
Diversity-Oriented Synthesis: Developing New Chemical Tools to Probe and Modulate
Biological Systems Galloway et al, 2014, http://www-
spring.ch.cam.ac.uk/publications/pdf/2014_DOS_379.pdf
Generative models for drug design
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Automatic chemical design using a data-driven continuous representation
of molecules Gomez-Bombarelli et al, 2016,
https://arxiv.org/abs/1610.02415
