New Iktos business model and offerings - EFMC-ISMC Virtual · 2020. 9. 7. · • Recent technology (First paper published in 2016 - Gomez-Bombarelli et al. 2016) • Several approaches

Artificial Intelligence for new drug design – confidential and proprietary material -1- www.iktos.ai – © Iktos 2020

ARTIFICIAL INTELLIGENCE FOR

NEW DRUG DESIGN

Iktos business model and offerings

July 2020


Iktos facts & figures

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 drugdiscovery programmes: ~20 projects delivered or in progress

Business model: services, research collaborations, software

Our company Our customers


Pharma R&D life cycle: long, costly, inefficient…

Hit Lead

Clinical trials

(10 years)

1200 M€

Medicinal chemistry

(5 years)

Drug on the market

Success rate 10%

1400 M€

100 000 molecules 1 molecule

Success rate 10%

Our focus


Iktos positioning and strategy

• In silico company

• AI technology provider for pharma companies: services, SaaS software, custom implementation

• Partner for in silico drug design in short-term or long-term research collaborations, mostly with service agreement model (Fee for service + Success/Milestone fees)

➔Make the technology available to customers and become the world leading AI software/technology provider in drug design

➔No wish to become a biopharma company

➔In time, ambition to develop an early stage portfolio of pharma IP assets through collaborations or in-house projects

Iktos positioning Ambition


Iktos offerings

• de novo design platform:• Ligand-based approach: Acceleration of Lead

Optimization

• Structure-based approach: hit/lead design, hit-to-lead acceleration, lead optimization

• Retrosynthesis platform• Access to Iktos SaaS software or API• Custom implementation of AI-powered, data-

driven retrosynthesis technology software:• Your data• Your starting points

• Service agreements• SaaS agreement• Software agreement

• Service agreements• Research collaborations

• SaaS agreement• Software agreement

(implementation services + SW license)


Deep generative

models for de novo

design in medicinal

chemistry


The challenge of medchem: multi-parameter optimization (MPO)

Solving the Rubik’s cube:• Simultaneous optimization on activity, potency, ADME, tox, selectivity…

• The chemical space is huge (1060). Does the solution even exist? Can we ever find it?

☹ : Gain on one objective usually results in loss on the other ones


Traditional in silico approaches don’t help much…

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

Predictive approaches:

• QSAR, data science

• Molecular modeling

Compound de novo design approaches:

Evolutionary algorithms

Slow, limited diversity, compound feasibility issues


State of the art Deep Neuronal Network perform outstanding tricks

Automatic colorization of black and white images

Automatic picture generation

Automatic image caption generation

Automatic game playing

Why not generate molecules instead of images of cats?


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


Iktos deep generative modeling platform for de novo design

Trained on 86

million of

molecules

LSTM Reinforcement

learning1) Molecules

are generated

2) molecules are scored by the

multi-objective fitness function

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.

Generative model (AI) Policy Gradient (AI) ✔QSAR models

✔Docking score

✔Metrics, descriptors

✔Sub-structures

…

Traditional

approaches

A state-of-the-art platform for in silico chemical optimization


Iktos de novo design offering across the value chain

Hit discovery

Goal: Identifying new easily accessible molecules with a high docking score

Method: Generating molecules very similar to accessible/commercial molecules and simultaneously maximizing a docking score

Structure-based

Ligand-based (scaffold hopping)

Goal: Identifying new active and patentable molecules for fast follower programs

Method: Generating molecules with FTO, based on knowledge extracted from the competitors

Lead identification

Structure-based

Ligand-based (exploitation

of HTS results)

Goal: From initial hits or fragments, identify molecules with higher activity on the target and drug-like characteristics

Method: Generating molecules very similar to hits or growing active fragments, while simultaneously maximizing a docking score and imposing drug-like characteristics

Goal: Identifying most promising series to focus on regarding several ADME criteria

Method: Generating molecules very similar to the best 2000 hits and simultaneously maximizing in house/external predictors (ADME for instance)

Lead optimisation

Early stage Late stage

Goal: starting from ~100 molecules, identifying those within the scaffold of a project which fix simultaneously a given number of objectives in a minimum number of cycles

Method: Generating molecules very similar to initial dataset to maximize a set of predicted criteria

Goal: starting from well- documented chemical series (~500 molecules), identifying those within the scaffold of a project which fix simultaneously a given number of objectives



Accelerating Lead Optimization

✓ Molecules and 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)


Predictors Generator


Servier success story

Activity 5-HT2A 5-HT2B a1 D1 NaV 1.2 hERG RLM HLMCaco-2

FAbs

Caco-2

Efflux

83 7 18 7 -9 2 3 57 75 97 7

Activity 5-HT2A 5-HT2B a1 D1 NaV 1.2 hERG RLM HLMCaco-2

FAbs

Caco-2

Efflux

194 20.0 18.0 1.0 4.0 0.0 19.0 82.8 63.3 88.9 26.2

Best Servier Best Iktos

Facts and figures

11 objectives880 molecules+10 years of research+5 chemists on the projectNo molecule meeting simultaneously the11 objectives of the blueprint…

Iktos work11 molecules synthesized and tested8 molecules matching 9 objectives3 molecules matching 10 objectives1 molecule matching 11 objectives

✓ 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


Hit/Lead generation with structure-based approach

✓ Client database of molecules✓ Commercially available molecules

DockingGenerator

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


Hit finding with deep generative models guided by docking

DockingGenerator

✓ High predicted value

✓ Important interactions arepresent

✓ All important PhysChemproperties are present

Contact ScoreDocking Score TPSA LogP


Makya, Iktos SaaS platform for de novo design

AutoMLmodule

Datasetupload

TPP definition

“ideal” in silico propositions

Already licensed and deployed at


Already licensed and deployed at:

Kaya, Iktos python package for de novo design


Retrosynthesis

technology


AI 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)


Marvin Segler et al. 2018

M. H. S. Segler, M. Preuss, M. P. Waller, Nature 555, 604–610 (2018)


How does it work?

Target compound

1st step: disconnection identification> A probability is given for each disconnection (rules)

2nd step: Application of the rule> Application of rule Rz for instance

Rx

Ry

RzRw

RvRu

Rt

+

Rz

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 andstarting materials identified.


Our 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 withpharma companies

https://beta.spaya.ai/


Custom implementation of Spaya software

✓ Client Reactions database (ELN?)✓ US Patent database✓ Other (Reaxys?)

Retro-Synthetic

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


Retrosynthesis SW custom implementation

Reactions

o USPTOo Reaxyso ELN

Treatment

o Cleaning reactions

o Transforming reactions

o Training learning algorithms

o Adapting retrosynthesis to the client needs (designing a custom reward...)

Output

o Retrosynthesis web application powered by AI and integrated to your information system (in-house and commercial building blocks, procurement)

Input

Molecules

o Commercial Compoundso In house starting materials,

scaffolds

Output Process

4-6 weeks


Contact

Yann Gaston-MathéCEO

[email protected]

+33 6 30 07 99 26

Quentin PerronCSO

[email protected]

+33 7 68 80 50 76

mailto:[email protected]

mailto:[email protected]

Documents

New Iktos business model and offerings - EFMC-ISMC Virtual · 2020. 9. 7. · • Recent technology (First paper published in 2016 - Gomez-Bombarelli et al. 2016) • Several approaches