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AI in Drug Discovery
10 March - 11 March 2025
AI in Drug Discovery

SAE Media Group is proud to present the 6th Annual AI in Drug Discovery Conference on 10th-11th March 2025, in London, UK. Join us for the biggest AI in drug discovery event in the UK, that brings together the highest number of big pharma speakers for focused discussions and networking. 


With the global AI in drug discovery market expected to reach 7.94 billion by 2030, AI is leading technological advancement across the pharmaceutical sector, and is expected to transform pre-clinical drug discovery.
 

Are you ready for this transformative period?


With ever-increasing pressures on drug pipelines, strategies for faster, cheaper and more successful R&D are required – this conference will showcase how AI-driven technologies are turning these strategies into reality. Through detailed case-study presentations and interactive panel-discussion sessions, we will navigate data volume and quality challenges, create strategies for increasing efficiency of R&D, and explore the impact of generative AI on drug discovery pipelines.


This year’s event will feature hot topics including applications of AI/ML within target identification and computational drug design, the impact of generative AI on ways of working and clinical updates from the first AI-designed drug candidates.


Our two-day agenda offers you peer-to-peer networking with industry experts from an extensive selection of big pharma and breakthrough biotech’s in the field, including directors and heads of informatics, data & AI, molecular design, and computational chemistry. Don’t miss the chance to hear the latest advancements from leaders in the industry!
 

 


 

FEATURED SPEAKERS

Fabian Heinemann

Fabian Heinemann

Lead of IT Data Science Chapter, Boehringer Ingelheim
Henrik Moebitz

Henrik Moebitz

Director, CADD, Global Discovery Chemistry, Novartis
James Lumley

James Lumley

Head of Cheminformatics, GSK
Lewis Vidler

Lewis Vidler

Senior Director - Structure Based Drug Design, Eli Lilly
Maureen Makes

Maureen Makes

VP Engineering, Recursion
Peter Clark

Peter Clark

VP, Computational Drug Design, Novo Nordisk

Christoph Grebner

Senior Principal Scientist, Sanofi
Christoph Grebner

I am working as a computational chemist in drug design ranging from early hit discovery until lead optimization and candidate selection.
Some of my research topics are deep learning and AI-based methods for drug design processes like property prediction and de novo design.

12/2018 – present Senior Principal Scientist; Artificial Intelligence and Computational Chemistry; SMD-IDD, Sanofi-Aventis Deutschland GmbH; (Frankfurt, Germany)
07/2016 – 11/2018 Senior Scientist Computational Chemistry; AstraZeneca (Hit Discovery, Discovery Sciences iMED), Göteborg, Sweden
04/2014 – 06/2016 PostDoc in Computational Chemistry: “Novel in silico approaches for modeling the dynamic nature of proteins”; AstraZeneca (Medicinal Chemistry, CVMD iMED), Göteborg, Sweden and Barcelona Supercomputing Center, Spain
 

Darren Green

Director, ChemPlus Cheminformatics Consultants
Darren Green

Darren has recently retired from GSK after a career spanning 33 years and many roles, most recently as Global Head of Cheminformatics. He is now semi-retired, working as a Consultant and is Honorary Professor of Chemistry at University College London where he continues to pursue novel computational methods- based on simulation, cheminformatics and machine learning- which will improve the speed and efficiency of small molecule drug discovery.

Fabian Heinemann

Lead of IT Data Science Chapter, Boehringer Ingelheim
Fabian Heinemann

Fabian is heading the IT Data Science Chapter and the Central Data Science EMEA I team within Boehringer Ingelheim. The team explores and develops of AI applications for all functions of the company.

Prior to his current role, Fabian served as the head of the Biomedical AI lab at Boehringer Ingelheim’s research organization. His team specialized in the development and application of deep learning-based tools for extracting quantitative information from histological sections, specifically for pharmaceutical research.

Fabian's experience includes working as a data scientist at Roche and conducting research at research institutes like the Max-Planck Institute for Biochemistry. His educational background is physics, with specializations in biophysics and theoretical physics. Throughout his career, he has conducted research in various fields, ranging from biophysics, life science, material science, optics, to simulations of biological processes, and most recently applied machine learning
 

Florian Nigsch

Director Data Science, Novartis
Florian Nigsch

Florian Nigsch currently works in Discovery Sciences at Novartis Biomedical Research where he leads a data science team focused on enabling early drug discovery through multi-disciplinary approaches including a variety of genomics and computational technologies. He has a mixed education in molecular biology and chemistry, holds a Masters degree in Physical and Theoretical Chemistry, Paris/France, and a PhD in Computational Chemistry, Cambridge/UK.

Fred Manby

Co-Founder and CTO, Iambic Therapeutics
Fred Manby

Fred Manby is a scientist and entrepreneur focused on integration of AI technologies and high-throughput experimentation in small-molecule drug discovery. He received a DPhil in Chemistry from the University of York, and had a prolific 20-year academic career in quantum and computational chemistry, primarily as a Professor at the University of Bristol. In 2020 Fred left his academic role to co-found Iambic Therapeutics, where he is Chief Technology Officer. Iambic is a clinical-stage biopharma which innovates in machine learning and laboratory automation to create technologies that drive an internal pipeline of drug discovery programs, with a primary focus in oncology.

Generoso Ianniciello

Chief Business Officer, Anima Biotech
Generoso Ianniciello

Generoso Ianniciello is the Chief Business Officer at Anima Biotech, a tech.bio company revolutionizing Target and Drug Discovery through its pioneering Visual Biology platform, Lightning. He leads the company’s business strategy, overseeing corporate development and driving the commercialization of the Lightning platform through strategic collaborations with pharmaceutical companies.

Generoso brings deep expertise in the healthcare industry, genomics, multi-omics, platform services, and business leadership.
Before joining Anima, he served as Chief Business Officer at Dante Genomics, a leading multi-omics company. He played a key role in scaling the start-up into a successful enterprise with over 200 employees and $100 million in annual revenue. He co-led the development and launch of the Dante MyGenome Platform for Longevity, Personalized Medicine, and Rare Disease Diagnostics, establishing strategic partnerships with major hospital chains, biopharma companies, and key research institutions.
During the COVID-19 pandemic, as Senior Director of Innovation, Generoso managed the setup of large-scale labs in Italy, the UK, and Dubai, and established molecular biology labs on 90 cruise ships, overseeing the execution of over 3 million RT-PCR tests.
Generoso holds an MSc in Health Biology and a BSc in Biology from the University of L’Aquila, with research experience in Molecular Biology at the University of Aberdeen, Scotland.

 

Hannah Bruce Macdonald

Associate Principal Scientist, CHARM Therapeutics
Hannah Bruce Macdonald

Hannah is a computational chemist and member of the modeling and informatics group at Charm therapeutics. Hannah started her interest in computational modelling during her PhD at the University of Southampton and postdoc at Memorial Sloan Kettering Cancer Center in New York. She then moved to applying these methods to drug discovery at MSD in London, prior to joining Charm in 2022.

Henrik Moebitz

Director, CADD, Global Discovery Chemistry, Novartis
Henrik Moebitz

Henrik Möbitz is a Director Data Science in Global Discovery Chemistry, CADD. Working at the interface between chemistry, biology and pharmacology, Henrik is keen to utilize computation to drug challenging targets with an emphasis on physicochemical properties and physics-based methods. Since joining Novartis in 2005, Henrik has contributed to 4 clinical compounds and 10 additional DRF/development candidates (most recently the first in class WRN inhibitor HRO761). He currently leads a CADD group in Basel and medicinal chemistry projects.

James Lumley

Head of Cheminformatics, GSK
James Lumley

James is Head of Cheminformatics at GSK leading a team delivering project support across multiple modalities via the development of proprietary methods, models & platforms for automated design. The team have responsibility for GSK’s validated automated design platform BRADSHAW and the underlying machine learning platform QSAR Studio. His initial career includes 10 years in Biotech as Head of Computational Chemistry, Arrow therapeutics then later as a ReViral Ltd cofounder. He has inventorship on multiple antiviral drug candidates including AZD-7295 and later Sisunatovir (RV521). In 2010 James joined Eli Lilly responsible for global Cheminformatics support. He led projects delivering award winning projects across small molecule research including multiple CIO awards and a BioIT World Innovative Practice Award (Research.Data 2020). James has a PhD in Computational Chemistry and over 25 years experience in drug discovery Research with 4 years at GSK.

Kinga Bercsenyi

Chief Business Officer, Arctoris
Kinga Bercsenyi

Kinga Bercsenyi, PhD is the Chief Business Officer of Arctoris, a tech-enabled CRO based in Oxford.

Kinga completed her PhD at Cancer Research UK, followed by a Sir Henry Wellcome postdoctoral fellowship at King’s College London. She has extensive research experience spanning from early, through preclinical drug discovery, all the way to clinical trials. In her role at Arctoris, she is working tirelessly to support biotech and pharma clients in getting better data, which leads to better decisions and ultimately, better drugs for patients

Le Mu

Product Line Manager for End-to-End AI/ML, Product Owner and Tech Lead for Roche pRED-MLOps service, Roche
Le Mu

Having worked 10+ years in digitalization across different industries, Le (Muller) Mu is combining his IT experience with his knowledge in molecular biology to help accelerate the drug discovery process in Roche Pharma Research and Early Development (pRED). He is currently the product line manager for End-to-End AI/ML and product owner and tech lead for the Roche pRED MLOps Service team, driving the operationalization of many different machine learning models, which helps to bring better future medicines faster into the hands of the patients.

Lewis Vidler

Senior Director - Structure Based Drug Design, Eli Lilly
Lewis Vidler

Lisa Schneider

Machine Learning Scientist, Bayer
Lisa Schneider

Lisa is a Machine Learning Researcher at Bayer AG, focusing on developing machine learning methods to enhance drug discovery and clinical care. Her research evolves mainly around computer vision and multi-modal applications. Lisa’s academic background includes a Master’s in Digital Engineering and soon a doctoral degree from Charité - University Medicine Berlin.

Marcel Verdonk

Research Fellow, Astex Pharmaceuticals
Marcel Verdonk

Marcel Verdonk is a Research Fellow at Astex Pharmaceuticals. Marcel received his PhD in computational chemistry and crystallography from Utrecht University in 1995. He then spent four years at the Cambridge Crystallographic Data Centre, where he was responsible for the development of various structure-based design tools. Since November 2000, Marcel has been at Astex, where he headed up a group developing informatics, machine learning and structure-based design software applications.

Maureen Makes

VP Engineering, Recursion
Maureen Makes

Maureen Makes is a tech leader currently working to decode biology to radically improve lives at Recursion as VP of Engineering and site lead for the London office. Maureen is passionate about building systems that have a meaningful positive impact on society. Prior to Recursion, Maureen worked to democratize access to higher education and technology learning at Pluralsight and Lambda School. She was also the co-founder of Women Who Code SLC in 2018, Utah Business 40 under Forty in 2023 and Technology Leader of the Year for 2019 by the Women Tech Council.

Peter Clark

VP, Computational Drug Design, Novo Nordisk
Peter Clark

Petrina Kamya

President, Insilico Medicine Canada Inc., and Global Head of AI Platforms, VP, Insilico Medicine
Petrina Kamya

Petrina Kamya, PhD, is the Global Head of AI Platforms and President of Insilico Medicine Canada, overseeing Insilico’s end-to-end generative AI-driven drug discovery platform, Pharma. AI.
Before joining Insilico, Dr. Kamya had a career in Market Access and held several positions at a software company that developed CADD tools for drug discovery and development.
She holds a Ph.D. in theoretical chemistry and a BS in biochemistry from Concordia University.
 

Prakash Rathi

Augmented Drug Design Engineering Lead, AstraZeneca
Prakash Rathi

Prakash finished his PhD in computational protein design from University of Dusseldorf in 2014. He then worked at Astex Pharmaceuticals, Cambridge, UK as a scientific software developer. Since Dec 2019 Prakash is working within R&D IT at AstraZeneca where he is currently leading multiple teams building data, AI, and software solutions for small molecule and antibody drug discovery. He has a keen interest in the intersection of science and technology to discover innovative pharmaceuticals.

Ravi Gupta

Advisor - Clinical Data Automation, Data & Analytics, Eli Lilly & Company
Ravi Gupta

Ravi Gupta is Advisor in Clinical Data Automation at Eli Lilly & Company, with expertise in the integration of AI, machine learning, and advanced analytics into clinical trial data. With extensive experience in the field, he has spearheaded initiatives to automate data flows, increase efficiency, and provide real-time, data-based decision support. He specializes in automation tool design and using cloud technologies to streamline workflows and enhance data quality. Ravi has been a student at IIM Bangalore and is a strategic leader in bringing data innovations to clinical research and operations

Steve Gardner

CEO, PrecisionLife
Steve Gardner

Steve is a pioneer in precision medicine and has over 30 years’ experience developing and commercializing ground-breaking data science in the life sciences, with an established track record in building world-class companies and products.
He is Chair of the UK Bioindustry Association’s Genomic Advisory Committee, Co-Chair of the Metrodora Foundation Scientific Advisory Board, a former Global Director of Research Informatics for Astra A/B and has consulted with drug discovery and development teams in over 20 biopharma companies.
 

Vladislav Kim

Research Scientist, Bayer
Vladislav Kim

Vlad, an ML researcher at Bayer, is developing AI-driven computer vision solutions for early-stage drug discovery. Leveraging foundation models for complex bioimaging data, Vlad is working on accelerating target discovery and improving the safety and efficacy profiles of preclinical drug candidates.

Yogesh Sabnis

Director of Lead Design, UCB Biopharma srl
Yogesh Sabnis

Yogesh completed his PhD in Synthetic & Computational Chemistry from the University of Mississippi in 2002 and finished post doctorate at Uppsala University, Sweden in 2006. After which, he joined Pfizer where his work was seminal to the discovery of candidate (PF-06263276), toward plaque psoriasis program which went to Phase 1. He joined the Global CADD at UCB in Belgium in 2011 and has since successfully contributed to clinical PET programs ([18F]UCB-K and UCB2897). With the ambition of “Faster to Candidate”, he led the in-house development and implementation of D2P2 (data-driven predictive platform) to enable better decision-making for NCE discovery.

sponsors

Conference agenda

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8:00

Registration & Coffee

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8:50

Chair's Opening Remarks

Peter Clark

Peter Clark, VP, Computational Drug Design, Novo Nordisk

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9:00

Kick starting ML Projects with minimal data & evolving models for embedded DMTA support

James Lumley, Head of Cheminformatics, GSK

  • How to kick start an ML project with minimal data
  • Continuous monitoring of data and model drift to improve models
  • Success stories of Active Learning & alternate methods in model evolution
  • Combining ML with Physics based methods

     

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    9:30

    Leveraging AI in drug discovery - the art of balancing exploration and exploitation

    Le Mu, Product Line Manager for End-to-End AI/ML, Product Owner and Tech Lead for Roche pRED-MLOps service, Roche

  • The exploration-exploitation dilemma in drug discovery (e.g. discovering novel chemical space v.s. screening through existing compound libraries) also manifest itself when it comes to leveraging the power of AI for accelerating innovation and reducing cost.
  • Supporting exploratory v.s. exploitative activities in the AI/ML space in pharma research requires different technologies and different skillset from people – yet we would need to make sure both types of activities work cohesively to maximize the value
  • Our journey of establishing and growing a MLOps service in Roche pRED in the past 4 years is likely to help to shed some lights on balancing exploration and exploitation in a big pharma research organization
  • Closing reflections on key factors that drive a successful AI strategy in drug discovery, with a focus on mastering the balance between exploration and exploitation across different organizational environments.
     
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    10:00

    Augmenting DMTA using predictive AI modelling at AstraZeneca

    Prakash Rathi, Augmented Drug Design Engineering Lead, AstraZeneca

  • Building data products are key to fully exploit AI in drug discovery
  • We built data and AI solutions to expedite small molecule and antibody discovery at AZ
  • The impact on projects and consequently number of users are growing
     
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    10:30

    Morning Break

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    11:00

    Schrodinger's latest advancements in in-silico drug design

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    11:30

    Augmenting target discovery and early drug discovery with AI

    Florian Nigsch, Director Data Science, Novartis

  • Criteria for AI-enabled workflows for early drug discovery
  • Levelling up the basic data package for any target, at scale
  • Combining cutting edge AI methods with wet-lab follow-up
     
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    12:00

    Mapping Cardiovascular Health through Multimodal Analysis of UK Biobank Data

  • Combining phenotypes from the UK Biobank’s multimodal dataset including cardiac MRI, ECG, and ventricular point cloud data
  • Harnessing unsupervised deep learning models for unbiased extraction and characterization of modality-specific phenotypes
  • Integration of phenotypic information from multiple modalities through contrastive learning.
  • Development of a strategy to uncover novel genetic determinants of heart disease leveraging these holistic phenotypes

     
  • Lisa Schneider, Machine Learning Scientist, Bayer

    Vladislav Kim, Research Scientist, Bayer

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    12:30

    Networking lunch

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    13:30

    Transforming Target and Drug Discovery with Visual Biology derived AI disease models

    Generoso Ianniciello, Chief Business Officer, Anima Biotech

  • Leveraging Anima’s Lightning.AI platform to run millions of visualizations in high throughput, imaging hundreds of cellular pathways in both healthy and diseased cells
  • Using this data to train neural networks that visually learn the difference between healthy and diseased cells
  • Identifying novel targets backed by experimental validation using visual biology knowledge graphs
  • Discovery of small molecule drugs that visually reverse disease phenotypes by modulating their mRNA biology
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    14:00

    Practical Machine Learning applications for fragment-based drug discovery

    Marcel Verdonk, Research Fellow, Astex Pharmaceuticals

  • Using ML for auto-templated docking in a structure-rich environment
  • Using LLMs for characterisation and clustering of protein-ligand binding sites
  • Discuss integration of the above in Astex’s fragment-based drug discovery platform
     
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    14:30

    Design Principles for Balancing Lipophilicity and Permeability in beyond Rule of 5 Space

    Henrik Moebitz, Director, CADD, Global Discovery Chemistry, Novartis

  • Principals of the beyond the rule of 5 space and applications for PROTAC design
  • Combining computation and measurement to create computationally consistent datasets
  • Designing novel drugs to reach previously undruggable targets
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    15:00

    Afternoon Break

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    15:30

    How to make better informed decisions in small molecule drug design

    Yogesh Sabnis, Director of Lead Design, UCB Biopharma srl

  • One of the most significant changes in drug discovery today is the accelerated adoption of digital technologies that is transforming the pharma industry.
  • As this digital revolution gains momentum, one of our strategic priorities at UCB is to fully harness these technologies to boost effectiveness and efficiency of drug discovery. 
  • Our ability to bring further innovation to the fingertips of researchers will allow new ways to discover and make medicines in a much shorter time frame than is currently possible. I will share how we have built this framework to help us become better decision makers.

     
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    16:00

    AI based technologies for mining chemical space in drug design

    Christoph Grebner, Senior Principal Scientist, Sanofi

  • Discussing key challenges in AI-based drug design: exploring chemical space, interpretability, data analysis
  • (Large) Language Models in searching and exploring virtual chemical spaces
  • New technologies for interpretation of structure-activity-relationships
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    16:30

    Panel Discussion: The future of AI-driven drug design

  • Reviewing recent advancements in AI-powered target identification and drug design
  • Discussing how to overcome current challenges including trust in AI-derived results, data privacy and quality, and the bias towards positive results seen in datasets
  • Where are we heading? What does the future of AI-designed drugs look like?
  •  
  • Yogesh Sabnis, Director of Lead Design, UCB Biopharma srl

    Marcel Verdonk, Research Fellow, Astex Pharmaceuticals

    Henrik Moebitz, Director, CADD, Global Discovery Chemistry, Novartis

    Darren Green, Director, ChemPlus Cheminformatics Consultants

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    17:00

    Chair's Closing Remarks and Close of Day One

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    8:30

    Registration & Coffee

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    9:00

    Chair's Opening Remarks

    Maureen Makes, VP Engineering, Recursion

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    9:10

    A data-focused approach to drug discovery

    Maureen Makes, VP Engineering, Recursion

  • What fit for purpose drug discovery data looks like and why it is key to innovation
  • Systems that make data available and usable for discovery
  • Overview of scaling models with the combination of compute and data
     
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    9:40

    Advances in Computational Approaches to Accelerate Therapeutic Discovery & Development

    Peter Clark

    Peter Clark, VP, Computational Drug Design, Novo Nordisk

  • Molecular design & optimization in the age of AI
  • Strategies and impact on for integrating AI into the drug discovery process- updates and advancements from Novo Nordisk
  • Reviewing the current state of the industry - challenges and future perspectives
     
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    10:10

    The Increasing Role of Lab Automation Beyond Screening: Maximizing Machine Learning Utility in Drug Discovery with Enhanced Data Generation

    Kinga Bercsenyi , Chief Business Officer, 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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    10:40

    Morning Break

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    11:10

    Transcending data scarcity in AI-driven drug discovery

    Fred Manby, Co-Founder and CTO, Iambic Therapeutics

  • Iambic’s AI-driven experimental platform
  • Issues associated with developing AI-models with a lack of appropriate data
  • Clinical updates from Iambic’s lead program
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    11:40

    Custom AI applications for quantitative histopathology in pharmaceutical research

    Fabian Heinemann, Lead of IT Data Science Chapter, Boehringer Ingelheim

    • AI to automate tasks, which were previously limited to pathologists. Examples will include 
          o Quantifying histopathological parameters of non-alcoholic steatohepatitis (NASH)
          o Quantifying parameters of lung fibrosis.
    • Applications with unsupervised and generative methods.
         o An algorithm for anomaly detection in histopathological images will be presented which can support early tox assessment.
         o Approaches using generative adversarial networks to convert histological stains that generate virtual stains that can be indistinguishable for pathologists.
     

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    12:10

    Evolving virtual assistants using Large Language Models

    Lewis Vidler

    Lewis Vidler, Senior Director - Structure Based Drug Design, Eli Lilly

  • Early efforts here involved using expert systems to deliver a virtual assistant like experience to users
  • We were able to generate successful POC in medicinal chemistry
  • The advent of LLMs has massively increased the opportunities for virtual agents to provide value to users
  • The philosophy, historical work and current/future directions in this space will be described
     
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    12:40

    Networking Lunch

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    13:40

    AI-driven prediction of protein-ligand co-folding for discovery of transformational medicines

    Hannah Bruce Macdonald, Associate Principal Scientist, CHARM Therapeutics

  • DragonFold – an end-to-end deep learning algorithm for 3D protein-ligand co-folding prediction
  • Validation of AI-targets in an integrative loop
  • Updates on novel AI-derived drug candidates for cancers and previously undruggable targets
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    14:10

    Causal AI & Mechanostics - Increasing Probability of R&D Success from Concept to Clinic

    Steve Gardner, CEO, PrecisionLife

  • Discovering, developing, and launching AI driven precision medicines for complex, chronic diseases
  • Creating better diagnostic tools and more personalized treatment options for unmet medical needs
  • Improving the discovery and selection of novel targets matched to patient biology
  • Informing the design of clinical trials that are faster to readout and more likely to succeed
  • Linking patients to effective treatments via their mechanism of disease

     
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    14:40

    Afternoon Break

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    15:10

    An end-to-end AI platform for novel drug discovery

    Petrina Kamya, President, Insilico Medicine Canada Inc., and Global Head of AI Platforms, VP, Insilico Medicine

  • Combining Insilico’s AI platform with a fully automated, nextgeneration laboratory for maximal success
  • Detailing the recent advances in Insilico’s clinical pipeline – results from phase 2
  • Challenges associated with progressing AI-designed molecules through the pipeline
     
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    15:40

    Unlocking AI's Potential in Clinical Trial Data: Innovations and Use Cases

    Ravi Gupta, Advisor - Clinical Data Automation, Data & Analytics, Eli Lilly & Company

  • Introduction: How AI revolutionizes clinical trials with enhanced insights, efficiency, and speed.
  • AI in Trials: Real-time analysis, streamlined processes, and faster drug development.
  • Use Cases: Predictive models, automated data management, and statistical support.
  • Challenges: Tackling data quality, integration, and ethical issues.
  • Future: The evolving role of AI in transforming clinical trials.

     

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    16:10

    Closing keynote panel: Will AI live up to the current hype?

  • How far have we come – what are the major breakthroughs in the past year?
  • What challenges remain to fully integrate AI into drug discovery processes?
  • Discussing the issues of ethical concerns, data privacy, and regulatory barriers
  • How far do we have to go – future looking perspectives on where the industry is headed.
     
  • Fred Manby, Co-Founder and CTO, Iambic Therapeutics

    Maureen Makes, VP Engineering, Recursion

    Petrina Kamya, President, Insilico Medicine Canada Inc., and Global Head of AI Platforms, VP, Insilico Medicine

    Peter Clark

    Peter Clark, VP, Computational Drug Design, Novo Nordisk

    Darren Green, Director, ChemPlus Cheminformatics Consultants

    clock

    16:40

    Chair's Closing Remarks and Close of Day 2

    Maureen Makes, VP Engineering, Recursion


    Senior Principal Scientist
    Sanofi
    Director
    ChemPlus Cheminformatics Consultants
    Lead of IT Data Science Chapter
    Boehringer Ingelheim
    Director Data Science
    Novartis
    Co-Founder and CTO
    Iambic Therapeutics
    Chief Business Officer
    Anima Biotech
    Associate Principal Scientist
    CHARM Therapeutics
    Director, CADD, Global Discovery Chemistry
    Novartis
    Head of Cheminformatics
    GSK
    Chief Business Officer
    Arctoris
    Product Line Manager for End-to-End AI/ML, Product Owner and Tech Lead for Roche pRED-MLOps service
    Roche
    Senior Director - Structure Based Drug Design
    Eli Lilly
    Machine Learning Scientist
    Bayer
    Research Fellow
    Astex Pharmaceuticals
    VP Engineering
    Recursion
    VP, Computational Drug Design
    Novo Nordisk
    President, Insilico Medicine Canada Inc., and Global Head of AI Platforms, VP
    Insilico Medicine
    Augmented Drug Design Engineering Lead
    AstraZeneca
    Advisor - Clinical Data Automation, Data & Analytics
    Eli Lilly & Company
    CEO
    PrecisionLife
    Research Scientist
    Bayer
    Director of Lead Design
    UCB Biopharma srl

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    Cresset

    Sponsors
    http://www.cresset-group.com

    Chemists in the world’s leading research organizations use Cresset solutions to discover, design, optimize, synthesize and track the best small molecules. By integrating their in silico CADD and design-make-test-analyze discovery solutions with Cresset’s first-class discovery research resources, researchers will have access to patented CADD Software, collaborative Torx® DMTA platform and expert Discovery CRO scientists. In helping organizations reach better design and synthesis decisions faster and more efficiently, we enable them to win the race to success in industries including: pharmaceuticals, agrochemicals, flavors and fragrances.


    Schrödinger

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    http://www.schrodinger.com

    Schrödinger is transforming the way therapeutics and materials are discovered. Schrödinger has pioneered a physics-based computational platform that enables discovery of high-quality, novel molecules for drug development and materials applications more rapidly and at lower cost compared to traditional methods. The software platform is licensed by biopharmaceutical and industrial companies, academic institutions, and government laboratories around the world. Schrödinger’s multidisciplinary drug discovery team also leverages the software platform to advance a portfolio of collaborative and proprietary programs to address unmet medical needs.


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    Hilton London Kensington

    179-199 Holland Park Avenue
    London W11 4UL
    United Kingdom

    Hilton London Kensington

    At the heart of the Holland Park district, our hotel is 10 minutes from Westfield London shopping center. We're blocks from Shepherd's Bush Underground station, linking to central London, and Kensington Palace and Gardens are two miles from us. Enjoy 24-hour access to our fitness center.

     
    Join us in WestEleven for hearty buffet breakfast, a great way to start the day! Our Avenue Bar and Lounge serves light bites throughout the day as well as a delicious, seasonal dining menu.”
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    WHAT IS CPD?

    CPD stands for Continuing Professional Development’. It is essentially a philosophy, which maintains that in order to be effective, learning should be organised and structured. The most common definition is:

    ‘A commitment to structured skills and knowledge enhancement for Personal or Professional competence’

    CPD is a common requirement of individual membership with professional bodies and Institutes. Increasingly, employers also expect their staff to undertake regular CPD activities.

    Undertaken over a period of time, CPD ensures that educational qualifications do not become obsolete, and allows for best practice and professional standards to be upheld.

    CPD can be undertaken through a variety of learning activities including instructor led training courses, seminars and conferences, e:learning modules or structured reading.

    CPD AND PROFESSIONAL INSTITUTES

    There are approximately 470 institutes in the UK across all industry sectors, with a collective membership of circa 4 million professionals, and they all expect their members to undertake CPD.

    For some institutes undertaking CPD is mandatory e.g. accountancy and law, and linked to a licence to practice, for others it’s obligatory. By ensuring that their members undertake CPD, the professional bodies seek to ensure that professional standards, legislative awareness and ethical practices are maintained.

    CPD Schemes often run over the period of a year and the institutes generally provide online tools for their members to record and reflect on their CPD activities.

    TYPICAL CPD SCHEMES AND RECORDING OF CPD (CPD points and hours)

    Professional bodies and Institutes CPD schemes are either structured as ‘Input’ or ‘Output’ based.

    ‘Input’ based schemes list a precise number of CPD hours that individuals must achieve within a given time period. These schemes can also use different ‘currencies’ such as points, merits, units or credits, where an individual must accumulate the number required. These currencies are usually based on time i.e. 1 CPD point = 1 hour of learning.

    ‘Output’ based schemes are learner centred. They require individuals to set learning goals that align to professional competencies, or personal development objectives. These schemes also list different ways to achieve the learning goals e.g. training courses, seminars or e:learning, which enables an individual to complete their CPD through their preferred mode of learning.

    The majority of Input and Output based schemes actively encourage individuals to seek appropriate CPD activities independently.

    As a formal provider of CPD certified activities, SAE Media Group can provide an indication of the learning benefit gained and the typical completion. However, it is ultimately the responsibility of the delegate to evaluate their learning, and record it correctly in line with their professional body’s or employers requirements.

    GLOBAL CPD

    Increasingly, international and emerging markets are ‘professionalising’ their workforces and looking to the UK to benchmark educational standards. The undertaking of CPD is now increasingly expected of any individual employed within today’s global marketplace.

    CPD Certificates

    We can provide a certificate for all our accredited events. To request a CPD certificate for a conference , workshop, master classes you have attended please email events@saemediagroup.com

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    UK Office
    Opening Hours: 9.00 - 17.30 (local time)
    SAE Media Group , Ground Floor, India House, 45 Curlew Street, London, SE1 2ND, United Kingdom
    Tel: +44 (0) 20 7827 6000 Fax: +44 (0) 20 7827 6001
    Website: http://www.smgconferences.com Email: events@saemediagroup.com
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