Iktos business model and offerings - ARTIFICIAL INTELLIGENCE FOR NEW DRUG DESIGN July 2020 - EFMC-ISMC Virtual Event 2020
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ARTIFICIAL INTELLIGENCE FOR
NEW DRUG DESIGN
Iktos business model and
offerings
July 2020
Artificial Intelligence for new drug design – confidential and proprietary material -1- www.iktos.ai – © Iktos 2020
Iktos facts & figures
Our company Our customers
Paris-based AI company founded late 2016
30 employees
Specializing in AI applied to chemistry:
• Deep Generative models for de novo drug design
• Data-driven retrosynthesis
Concrete real-life experience of delivering value to drug
discovery programmes: ~20 projects delivered or in
progress
Business model: services, research collaborations,
software
Artificial Intelligence for new drug design – confidential and proprietary material -2- www.iktos.ai – © Iktos 2020
Pharma R&D life cycle: long, costly, inefficient…
Our focus
Medicinal chemistry Clinical trials
(5 years) (10 years)
Hit Lead Drug on the market
100 000 molecules 1 molecule
1200 M€ 1400 M€
Success rate 10% Success rate 10%
Artificial Intelligence for new drug design – confidential and proprietary material -3- www.iktos.ai – © Iktos 2020
Iktos positioning and strategy
Iktos positioning Ambition
• In silico company ➔Make the technology available to
customers and become the world
• AI technology provider for pharma leading AI software/technology
companies: services, SaaS software, provider in drug design
custom implementation
➔No wish to become a biopharma
• Partner for in silico drug design in short- company
term or long-term research
collaborations, mostly with service ➔In time, ambition to develop an
agreement model (Fee for service + early stage portfolio of pharma IP
Success/Milestone fees) assets through collaborations or in-
house projects
Artificial Intelligence for new drug design – confidential and proprietary material -4- www.iktos.ai – © Iktos 2020
Iktos offerings
• de novo design platform:
• Ligand-based approach: Acceleration of Lead • Service agreements
Optimization • SaaS agreement
• Software agreement
• Structure-based approach: hit/lead design, hit- • Service agreements
to-lead acceleration, lead optimization • Research collaborations
• Retrosynthesis platform
• Access to Iktos SaaS software or API • SaaS agreement
• Custom implementation of AI-powered, data- • Software agreement
driven retrosynthesis technology software: (implementation
• Your data services + SW license)
• Your starting points
Artificial Intelligence for new drug design – confidential and proprietary material -5- www.iktos.ai – © Iktos 2020
Deep generative
models for de novo
design in medicinal
chemistry
Artificial Intelligence for new drug design – confidential and proprietary material -6- www.iktos.ai – © Iktos 2020
The challenge of medchem: multi-parameter optimization (MPO)
Solving the Rubik’s cube:
• Simultaneous optimization on activity, potency, ADME, tox, selectivity…
☹ : Gain on one objective usually results in loss on the other ones
• The chemical space is huge (1060). Does the solution even exist? Can we ever find it?
Artificial Intelligence for new drug design – confidential and proprietary material -7- www.iktos.ai – © Iktos 2020
Traditional in silico approaches don’t help much…
Predictive approaches: Compound de novo design approaches:
• QSAR, data science
• Molecular modeling Virtual screening: Brute-force
Limited by computational power and space
Only very small portions of the chemical space are
explored (109 vs 1060)
Virtually Zero chance of finding “the” molecule
Evolutionary algorithms
Slow, limited diversity, compound feasibility issues
Artificial Intelligence for new drug design – confidential and proprietary material -8- www.iktos.ai – © Iktos 2020
State of the art Deep Neuronal Network perform outstanding tricks
Automatic colorization of black and white images Automatic image caption generation
Automatic game playing Automatic picture generation
Why not generate molecules instead of images of cats?
Artificial Intelligence for new drug design – confidential and proprietary material -9- www.iktos.ai – © Iktos 2020
Deep generative models for de novo compound design
• Recent technology (First paper published in 2016 - Gomez-Bombarelli et al. 2016)
• Several approaches published in the literature
• High potential for exploring the chemical space: Speed, Diversity, Novelty, Quality
• Many hurdles preventing widespread adoption in Drug Discovery:
- Mostly academic works at this stage, not industry-ready
- Many different approaches: which one to select?
- Very limited and theoretical proof of concepts, not representative of the
complexity of real-life projects
Iktos purpose: industrialize this new technology, make it industry-ready,
demonstrate its value for real-life drug discovery projects
Artificial Intelligence for new drug design – confidential and proprietary material -10- www.iktos.ai – © Iktos 2020Iktos deep generative modeling platform for de novo design
✔QSAR models
Generative model (AI) Policy Gradient (AI) ✔Docking score Traditional
✔Metrics, descriptors approaches
✔Sub-structures
…
Trained on 86
million of
molecules 2) molecules are scored by the
multi-objective fitness function
Reinforcement
LSTM 1) Molecules
are generated learning
3) the weights of the model
are adjusted to maximize the
probability of generating
molecules similar to those
maximizing the global score
using a policy gradient
algorithm.
A state-of-the-art platform for in silico chemical optimization
Artificial Intelligence for new drug design – confidential and proprietary material -11- www.iktos.ai – © Iktos 2020Iktos de novo design offering across the value chain
Hit discovery Lead identification Lead optimisation
Ligand-based Ligand-based
Structure- Structure- Early stage Late stage
(scaffold (exploitation
based based
hopping) of HTS results)
Goal: starting from Goal: starting from
Goal: Identifying new Goal: Identifying Goal: Identifying
Goal: From initial hits ~100 molecules, well- documented
easily accessible new active and most promising series
or fragments, identify identifying those chemical series
molecules with a high patentable to focus on regarding
molecules with higher within the scaffold of (~500 molecules),
docking score molecules for fast several ADME criteria
activity on the target a project which fix identifying those
follower programs
and drug-like simultaneously a within the scaffold
Method: Generating Method: Generating
characteristics given number of of a project which
molecules very similar Method: molecules very
objectives in a fix simultaneously a
to Generating similar to the best
Method: Generating minimum number of given number of
accessible/commercial molecules with FTO, 2000 hits and
molecules very similar cycles objectives
molecules and based on simultaneously
to hits or growing
simultaneously knowledge maximizing in
active fragments, while Method: Generating Method:
maximizing a docking extracted from the house/external
simultaneously molecules very Generating
score competitors predictors (ADME for
maximizing a docking similar to initial molecules very
instance)
score and imposing dataset to maximize a similar to initial
drug-like characteristics set of predicted dataset to maximize
criteria a set of predicted
criteria
Artificial Intelligence for new drug design – confidential and proprietary material -12- www.iktos.ai – © Iktos 2020Accelerating Lead Optimization
✓ Molecules and Predictors Generator
pharmacological data on a
given project
Goal: Identify molecules within the scaffold of a project which meet the Pre-clinical candidate TPP
• Early-stage LO: starting from ~100 molecules ➔ aim to reduce the nb of cycles needed to get to an optimized lead
• Late-stage LO: identify suitable optimized leads from well-documented chemical series (~500 molecules)
Method: Generating molecules very similar to initial dataset to maximize a set of predicted criteria
Artificial Intelligence for new drug design – confidential and proprietary material -13- www.iktos.ai – © Iktos 2020Servier success story
Facts and figures Iktos work
11 objectives 11 molecules synthesized and tested
880 molecules 8 molecules matching 9 objectives
+10 years of research 3 molecules matching 10 objectives
+5 chemists on the project 1 molecule matching 11 objectives
No molecule meeting simultaneously the
11 objectives of the blueprint…
Caco-2 Caco-2 Caco-2 Caco-2
Activity 5-HT2A 5-HT2B a1 D1 NaV 1.2 hERG RLM HLM Activity 5-HT2A 5-HT2B a1 D1 NaV 1.2 hERG RLM HLM
FAbs Efflux FAbs Efflux
194 20.0 18.0 1.0 4.0 0.0 19.0 82.8 63.3 88.9 26.2 83 7 18 7 -9 2 3 57 75 97 7
Best Servier Best Iktos
✓ First ever report of a successful use of deep learning generative models in a drug discovery project
✓ Results presented as a poster at the 2018 EFMC meeting in Ljubljana
Artificial Intelligence for new drug design – confidential and proprietary material -14- www.iktos.ai – © Iktos 2020Hit/Lead generation with structure-based approach
✓ Client database of molecules Generator Docking
✓ Commercially available molecules
Goal: Identify new easily accessible molecules with high activity on the target and drug-like characteristics, using a
structure-based approach
Method: Generating molecules very similar to accessible/commercial molecules and simultaneously maximizing a
docking score and/or interactions with key atoms within the pocket
Artificial Intelligence for new drug design – confidential and proprietary material -15- www.iktos.ai – © Iktos 2020Hit finding with deep generative models guided by docking
✓ High predicted value
✓ Important interactions are
present
✓ All important PhysChem
Generator properties are present
Docking
Docking Score Contact Score TPSA LogP
Artificial Intelligence for new drug design – confidential and proprietary material -16- www.iktos.ai – © Iktos 2020Makya, Iktos SaaS platform for de novo design
Dataset TPP
upload definition
AutoML “ideal” in silico
module propositions
Already licensed and deployed at
Artificial Intelligence for new drug design – confidential and proprietary material -17- www.iktos.ai – © Iktos 2020Kaya, Iktos python package for de novo design
Already licensed and deployed
at:
Artificial Intelligence for new drug design – confidential and proprietary material -18- www.iktos.ai – © Iktos 2020Retrosynthesis
technology
Artificial Intelligence for new drug design – confidential and proprietary material -19- www.iktos.ai – © Iktos 2020AI Powered Retrosynthesis
Traditional automated retrosynthesis systems are based on expert designed rules.
Can we leverage the knowledge of a (big) reaction database with AI to build a better retrosynthesis
system?
Segler et al.1 demonstrated that such a system is possible.
Iktos has implemented this paper using USPTO dataset and public commercial compounds database
Iktos proposes to:
o Adapt the system to its clients needs and assets.
o Provide a software to ease the interaction between the chemist and the AI.
1 – M. H. S. Segler, M. Preuss, M. P. Waller, Nature 555, 604–610 (2018)
Artificial Intelligence for new drug design – confidential and proprietary material -20- www.iktos.ai – © Iktos 2020Marvin Segler et al. 2018 M. H. S. Segler, M. Preuss, M. P. Waller, Nature 555, 604–610 (2018) Artificial Intelligence for new drug design – confidential and proprietary material -21- www.iktos.ai – © Iktos 2020
How does it work?
Ry
1st step: disconnection identification Rw Rz
Rv
> A probability is given for each disconnection (rules) Ru
Rx
2nd step: Application of the rule Rt
> Application of rule Rz for instance
Rz
+
Target compound 3rd step: In scope filter
> Check if the reaction is chemically feasible (not the case with Rz!)
4th step: Monte Carlo Tree Search (MCTS)
> Iterative application of steps 1, 2 & 3 until a feasible synthetic scheme is founded and
starting materials identified.
Artificial Intelligence for new drug design – confidential and proprietary material -22- www.iktos.ai – © Iktos 2020Our retrosynthesis software, SPAYA
• Beta version freely available at spaya.ai
• Discussions at finalization stage towards
deployment of Spaya with a first
customer
• Many early stage opportunities with
pharma companies
Artificial Intelligence for new drug design – confidential and proprietary material -23- www.iktos.ai – © Iktos 2020Custom implementation of Spaya software
✓ Client Reactions database (ELN?) Retro-Synthetic
✓ US Patent database
✓ Other (Reaxys?) algorithm
(Segler, Nature 2018)
Goal: Co-development and implementation of retro-synthesis analysis software customized to client reactions know-
how and starting materials.
Method: Data driven reaction rules extraction and training of Neural Network based on Iktos methodology inspired
by Segler 2018 paper
Artificial Intelligence for new drug design – confidential and proprietary material -24- www.iktos.ai – © Iktos 2020Retrosynthesis SW custom implementation
Input Process Output
Reactions Treatment
o USPTO Output
o Reaxys o Cleaning reactions
o ELN o Retrosynthesis web
o Transforming reactions application powered by AI
Molecules and integrated to your
o Commercial Compounds o Training learning algorithms information system
o In house starting materials, (in-house and commercial
scaffolds o Adapting retrosynthesis to the client needs building blocks,
(designing a custom reward...) procurement)
4-6 weeks
Artificial Intelligence for new drug design – confidential and proprietary material -25- www.iktos.ai – © Iktos 2020Contact
Yann Gaston-Mathé
CEO
yann.gaston.mathe@iktos.com
+33 6 30 07 99 26
Quentin Perron
CSO
quentin.perron@iktos.com
+33 7 68 80 50 76
Artificial Intelligence for new drug design – confidential and proprietary material -26- www.iktos.ai – © Iktos 2020You can also read
