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Artificial Intelligence (AI) has taken the lead in technological advancement across the pharmaceutical sector. It is expected to transform pre-clinical drug discovery through reducing costs by up to 40% and creating a market worth up to $50 billion in the next decade.

Are you ready for this transformative period?
Hear first-hand the latest AI advancements across the pharmaceutical sector directly from experts in the field and obtain strategies to support your operations at the highly anticipated AI in Drug Discovery conference. Driven by a move towards personalised therapies and novel drug candidates, Artificial Intelligence (AI) has taken the lead in technological advancement across the pharmaceutical sector. Increased efficiency of the drug discovery process, through innovations including automation, in-silico modelling, and machine learning, accelerate growth in an ever-expanding field, focused on revolutionising modern healthcare.

Why attend? 

This event gives you the opportunity to drive the field forwards, from clinical trial design to drug target selection, allowing for a cheaper, more successful research and development process, in the pursuit to improve disease outcomes globally. 
 

  • Expand your knowledge of drug target selection techniques, with insights from leading biotechs and big pharma
  • Develop your understanding of neural networks with a focus on digital twins, novel drug design and in-silico modelling
  • Utilise AI in the implementation of autonomous systems and high-throughput screening, in a bid to enhance drug manufacturing processes
  • Uncover the widespread capabilities of machine learning, from molecule design to clinical trial selection, alongside crucial patient centric considerations for AI modulation
  • Discuss the limitations of current AI/ML tools and methods to ensure their most efficient and accurate application
     

Key job titles include:

  • Head of AI
  • Head of Informatics
  • Head of Data
  • Head of Computational and Systems Toxicology
  • Director/Head of Strategic Data & Digital
  • Director/Head of Medicinal Chemistry
  • Director/Head of Molecular Design
  • Director/Head of Computational Chemistry
  • Director/Head of Chemical Sciences
  • Director of Therapeutic Technology
  • Chief Information Scientist
  • Chief Scientific Officer
  • Principal Scientist (Computational/Medicinal Chemistry)
  • Senior Application Scientist
  • Senior Research Scientist
  • Senior Bioinformatician
  • Molecular Modelling Team Leader
  • Data Team Leader
     

Previous Attendees Include:

Akros Pharma Inc.; AnalytiCon Discovery; Arctoris; Astellas Pharma; Astex Pharmaceuticals; AstraZeneca; Bayer AG; CAS; CCDC; Cloud Pharmaceuticals; Cresset; EMBL-EBI; Envisagenics; Exscientia; Galapagos; Genentech; Google Cloud; GSK; H Lundbeck A/S, Library & Info Ctr; H. Lundbeck A/S; Institut de Recherches Servier; Jazz Pharmaceuticals; Mcule; Merck KGaA; MHRA; Multiomic Health; Novo Nordisk; Novo Nordisk A/S; OpenEye Scientific Software; Optibrium; Optibrium Ltd.; PrecisionLife; Sanofi-Aventis Deutschland GmbH; Schrodinger GmbH; Sosei Heptares; The Ohio State University ; UCB; Valo Health; X-Chem, Inc.; Xelaro Ltd;

Conference agenda

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8:30

Registration & Coffee

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8:50

Chair's Opening Remarks

Dr Darren Green, Head of Cheminformatics & Data Science, Senior Felllow, GSK

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9:00

Improving AI Analytical and Predictive Power in Drug Discovery through Deep Learning Methodologies and Industry Collaboration

Dr Tom Diethe, Executive Director, Head of the Centre for Artificial Intelligence, AstraZeneca

  • The Power of Deep Learning in Drug Discovery: Discuss the potential of deep learning to revolutionize drug discovery by analysingvast datasets with speed and precision
  • Industry Collaboration Accelerating Progress: Highlight the critical role ofcollaboration between AI and pharmaceutical industries in advancingdrug discovery
  • Ethical Considerations and Future Outlook: Address ethical concerns, suchas data privacy and bias, while emphasizing the future potential of AI inpersonalized medicine and targeted therapies
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    9:30

    QSAR Studio: An Enterprise Machine Learning Platform

    Dr James Lumley, Associate Director, Cheminformatics and Data Science, GSK

  • Applying automation and solving the ML Ops problemto build and scale models in minimal time
  • Addressing the challenges of data quality,  model build best practices and employing uncertainty estimation for active learning
  • Where next – the role of DNN versus tree based models;  monitoring and evolving models in production
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    10:00

    End-to-End AI Integration in Drug Discovery:

    Dr Petrina Kamya

    Dr Petrina Kamya, President of Insilico Medicine Canada and Vice President, Head of AI Platforms, Insilico Medicine

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    10:30

    Morning Coffee

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    11:00

    Virtual Patients and Causal Disease Models: New Tools to Predict Drug Efficacy In Silico

    Professor Philippe Moingeon, Professor, AI and Drug Sciences, Paris-Saclay University and Head of Immuno-Inflammation Portfolio, Servier Pharmaceuticals

  • New forms of disease representation to simulate the efficacy of drug candidates
  • Mixed modality approaches to drug development combining predictive modeling and confirmatory empirical studies
  • Point to consider: regulatory acceptance of evidence generated by in silico models

     

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    11:30

    Foundation Models, LLMs, and the Impact of Large-Scale AI Models in Drug Discovery

    Daniel Cohen

    Daniel Cohen, President, Valence Labs and Vice President, Recursion Pharmaceuticals

  • The combination of scaled compute, data, and model size are leading to significant performance gains across the AI industry
  • Bringing these advances to drug discovery comes with constraints not present elsewhere in AI
  • Hear how Recursion and Valence Labs are bringing next-generation AI tools to drug discovery 
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    12:00

    Provisional Briefing Details: Enhancing the Accurate Modelling, Simulation, and Accessibility of ADME and PK-Relevant Data with AI Methods

    Dr Grégori Gerebtzoff

    Dr Grégori Gerebtzoff, Associate Director, Team Lead Data Science, Novartis Institues for BioMedical Research (NIBR)

  • Improving accuracy with improved data sets from diverse & historical sources
  • Refining models with improved methodologies 
  • Improving accessibility of AI-generated output for scientists 
  • Future development – seeking the full integration of human-PK predictions
     
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    12:30

    Operationalising ML at Speed and at Scale to Improve Integration & Decision-Making across the Roche Drug Discovery Pipeline

    Dr Christophe Chabbert

    Dr Christophe Chabbert, Senior Scientist, Discovery Informatics, , Roche Pharma Research and Early Development (pRED)

  • Using AI to contribute to a more agile framework for discovery and delivery 
  • Strategising ML deployment across research workflows
  • Choosing and refining a portfolio of prioritised ML models to prioritise 
  • Challenges to operationalisation
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    13:00

    Networking Lunch

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    14:00

    Pioneering Augmented Drug Design for Small Molecules

    Dr Anders Hogner

    Dr Anders Hogner, Associate Director, Medicinal Chemistry, AstraZeneca

  • The new opportunities offered by integrating AI into the drug design pipeline
  •  Building AI platforms for small molecule design and prediction at AstraZeneca
  • Where we are – platforms and practices.
  • Where are we going with AI-driven drug discovery?
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    14:30

    Next Generation Active Learning in Drug Discovery: Novel Methodologies for Small Molecules

  • Examining novel methods for batch learning of small molecules
  • Advantages demonstrated in early use
  • Wider applications of methodology to wider models and parts of the drug discovery pipeline
     
  • Thomas Licher

    Thomas Licher, Head of Integrated Drug Discovery, Sanofi

    Gerhard Hessler

    Gerhard Hessler, Head of Synthetic Molecular Design, Sanofi

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    15:00

    Using AI to Facilitate Improved Property Prediction

  • Current challenges with Small Molecule property prediction
  • Unpacking the D2P2 platform for Advanced Analytics
  • Next steps for advanced analytics
     
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    15:30

    Afternoon Tea

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    16:00

    Scale Up Your Experts: Harnessing Artifi cial Intelligence for Augmented Interactive Design in Fragment-Based Drug Discovery

    Carl Poelking, Senior Researcher, Computational Chemistry & Informatics, Astex Pharmaceuticals

    • The rich structural context of fragment-based drug discovery opens up unique opportunities how artificial intelligence (AI) can assist us with the design of preclinical candidates
    • Augmented Interactive Design captures our approach to integrating AI-driven approaches with human expertise – thus adding scale to the tradition of carefully handcrafted design
    • I will discuss some of our specialised predictive and generative technology that incorporates prior knowledge and structural, synthetic and directional constraints into the design process

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    16:30

    Unlocking the Potential of AI for High-Throughput Immunotherapy Drug Discovery Through RNA Splicing

    Dr Martin Akerman, Co-Founder & CTO, Envisagenics

    Envisagenics presents SpliceCore, an AI-powered target discovery platform with a focus on the identifi cation of novel, highly tumor-specific, and safe epitopes for immunotherapeutic development.
    • Explore the cutting-edge technology behind SpliceCore and delve into its scientifi c foundation of alternative splicing, a molecular process that simultaneously drives tumor progression while generating novel epitopes
    within cancer cells — offering a promising foundation for novel therapies.
    • Through compelling case studies and rigorous experimental validations, we will showcase the robust predictive capabilities of the SpliceCore platform and its ability to fi nd novel tumor-specifi c drug targets

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    17:00

    Chair's Closing Remarks

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    8:50

    Chair's Opening Remarks

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    9:00

    How Merck is Refining AI Tools to Accelerate Early-Stage Drug Discovery Projects

    Dr Chrisina Schindler

    Dr Chrisina Schindler, Associate Director, Head of Computational Drug Design, Merck KGaA

  • Building a sustainable AI & 3D modelling platform for hit ID and hit-to-lead optimization
  • Case-studies of AI applications for small molecule inhibitor and degrader discovery projects 
  • Improving communication around AI methods and generating impact
  •  
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    9:30

    Tackling Drug Discovery problems by Quantum Mechanics and Leveraging the Power of Google’s Tensor Processing Units

    Dr Andreas Göller, Principal Scientist, Computational Molecular Design and Senior Science Fellow, Bayer AG

  • Cancerogenicity risk assessment via QM activation pathway calculations
  • Combining QM and ML leverages the best of both worlds
  • Google’s TPU hardware brings QM to unprecedented scale
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    10:00

    Virtual Briefing: Applying the Mahol-A-Ba AI Platform to Facilitate Drug Discovery

    Dr Haruna Iwaoka, Senior Director, Advanced Modeling & Display, Discovery Intelligence, Astellas Pharma Inc.

  • Combination of Deep learning analysis using Yokogawa Electric corporation's software and automation enabled us to quantify the degree of iPSC differentiation from live-cell imaging data over time.
  • Performing analysis on just 10 or fewer training images allows researchers to easily and user-friendly perform cell image analysis in a short time.
  • Demonstrating utility when performing atypical experiments such as assay development
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    10:30

    Morning Coffee

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    11:00

    Maximizing Machine Learning Utility in Drug Discovery with Enhanced Data Generation

    Dr Martin-Immanuel Bittner, Co-Founder and CEO, Arctoris

  • Recently, the rise of AI/ ML for drug discovery has put a clear and increasing focus on the need for improved data  depth and data quality as key factors for successful AI/ ML applications
  •  On the other hand, laboratory automation for the longest time has primarily been associated with high-throughput screening applications for
  • hit identification
  • This is changing rapidly, as the talk will show: based on a brief introduction into the evolution of the AI/ ML and lab automation space over the past 10 years, the focus will be on the unique synergy between the two technologies, including the rationale, case studies, and the latest trends in utilizing lab automation as a key enabling technology for AI drug discovery
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    11:20

    Mapping the Druggable Transcriptome – Exploring the Intersection of AI, the Human Genome, and Drug Discovery

    Rabia Khan, CEO, Serna.bio

  • Building an appropriate & accurate dataset to inform valid AI mapping
  • ML methodologies applied to build the platform
  • Lessons drawn from ML: the StaR rules
  • Leveraging the new RNA world for drug discovery – unlocking classically undruggable proteins and unexplored potential therapeutic targets
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    11:40

    Data Generation Joint Q&A

    Rabia Khan, CEO, Serna.bio

    Dr Martin-Immanuel Bittner, Co-Founder and CEO, Arctoris

    Dr Darren Green, Head of Cheminformatics & Data Science, Senior Felllow, GSK

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    12:00

    Knowledge Graphs to Maximise Success Rate of Small Molecule Screening

    Dr Thierry Dorval, Head of Data Sciences & Data Management, Servier Pharmaceuticals

  • Current weaknesses in approaches to small molecule screening
  • Using knowledge graphs to address those weaknesses
  • Anticipating future progress
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    12:30

    Networking Lunch

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    13:30

    Patrimony: a high-throughput AI-powered platform to support target prioritization in drug discovery

    Dr Francois-Xavier Baludin de Thé, Computational Scientist, Servier Pharmaceuticals

    Importance of a deep understanding of disease pathophysiology to prioritize targets
    Adaptability of the Patrimony platform to different therapeutic areas: example in neurology
    Importance of integrating Patrimony with other platforms within the whole R&D process

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    14:00

    Validating Deep-Learning Methodologies to Address Outstanding Limitationsand Biases in Current AI Tools

    Martin Buttenschoen, Research Student, Oxford Protein informatics Group, University of Oxford

  • Current claims & validation tools of deep learning tools
  • Issues demonstrated by examination of current validation methods
  • Introducing PoseBusters – a new standard of ‘state of the art’ performance
  • Wider lessons for the field
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    14:30

    Knowing Why: Target Prediction with Explainable Large Language Models

    Dr Nicola Richmond

    Dr Nicola Richmond, VP of AI, BenevolentAI

  • Understanding the ‘why’ in science oftens hold equal or greater signifi cance than comprehending the ‘what’ – for bench scientists, this insight is particularly invaluable
  • The Large Language Model – a complex and notoriously black box – poses a formidable challenge for understanding the ‘why’
  • BenevolentAI’s journey demonstrates how you can harness the power of LLMs to identify potential therapeutic targets – whilst ensuring that the scientist is always equipped with the why to make full use of those targets

     

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    14:50

    Recognising the Limits of Current Generic LLMs for Drug Discovery – and How we can Apply Generative AI Better

    Dr Guglielmo Iozzia

    Dr Guglielmo Iozzia, Associate Director - Data Science, ML/AI, Computer Vision, MSD Ireland

  • Limits of ChatGPT and Generic LLMs for Drug Discovery
  • Transforming LLM technology to create solutions for Drug Discovery
  • Why building, training, and scaling bespoke models is a good investment
  • Negotiating concerns around ethics, privacy, security, and regulation
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    15:10

    Afternoon Tea

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    15:40

    AbLang: Building Language Models for Antibody Engineering

    Dr Iain Moal

    Dr Iain Moal, Principal Investigator and GSK Fellow, Computational Antibody Engineering, GSK

    Advantages of specific models over generic LLMs
    Specific utilities for antibody engineering
    Overview of the AbLang model
    Ensuring accurate output
     

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    16:00

    Generative AI Beyond ChatGPT: Enhancing Drug Discovery with Generative Chemistry

    Dr Nadine Schneider

    Dr Nadine Schneider, Lead, Generative Chemistry, Novartis, Biomedical Research

  • Leveraging generative and predictive models to efficiently explore chemical space
  • Challenges of data sparsity and combination of diverse ML models
  • Early learnings in drug discovery projects and future directions
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    16:20

    Generative AI Joint Q&A

    Dr Nadine Schneider

    Dr Nadine Schneider, Lead, Generative Chemistry, Novartis, Biomedical Research

    Dr Guglielmo Iozzia

    Dr Guglielmo Iozzia, Associate Director - Data Science, ML/AI, Computer Vision, MSD Ireland

    Dr Iain Moal

    Dr Iain Moal, Principal Investigator and GSK Fellow, Computational Antibody Engineering, GSK

    Dr Nicola Richmond

    Dr Nicola Richmond, VP of AI, BenevolentAI

    Dr Darren Green, Head of Cheminformatics & Data Science, Senior Felllow, GSK

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    16:40

    Panel Session: Strategising the Next Stage of AI Integration into Drug Discovery and Pharmaceuticals

  • What is the long-term goal for AI in Drug Discovery, and how do we get there?
  • How do we address the current weaknesses and issues with AI in Drug Discovery?
  • How can we fully integrate AI tools into the pharmaceutical industry as well as drug discovery – and how disruptive will it be current business models?
  • Predicting & overcoming ethical and regulatory barriers
     
  • Dr Darren Green, Head of Cheminformatics & Data Science, Senior Felllow, GSK

    Dr Thierry Dorval, Head of Data Sciences & Data Management, Servier Pharmaceuticals

    Professor Dr. Alexander Hillisch, Head of Global CADD, UCB

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    17:10

    Chair's Closing Remarks

    Dr Darren Green, Head of Cheminformatics & Data Science, Senior Felllow, GSK

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