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AI in Drug Discovery
11 March - 12 March 2024
AI in Drug Discovery

Conference Overview

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.
 

 

 

FEATURED SPEAKERS

Daniel Cohen

Daniel Cohen

President, Valence Labs and Vice President, Recursion Pharmaceuticals
Dr Christina Schindler

Dr Christina Schindler

Associate Director, Head of Computational Drug Design, Merck KGaA
Dr Christophe Chabbert

Dr Christophe Chabbert

Principal Scientist in Data & Analytics, Roche Pharma Research and Early Development (pRED)
Dr Grégori Gerebtzoff

Dr Grégori Gerebtzoff

Director, Pharmacokinetic Sciences (PKS), Novartis Biomedical Research
Dr Guglielmo Iozzia

Dr Guglielmo Iozzia

Associate Director - Data Science, ML/AI, Computer Vision, MSD Ireland
Dr James Lumley

Dr James Lumley

Associate Director, Cheminformatics and Data Science, GSK
Dr Maria Wendt

Dr Maria Wendt

Global Head of Digital and and Biologics Strategy & innovation, Large Molecule Research, Sanofi
Dr Nicola Richmond

Dr Nicola Richmond

VP of AI, BenevolentAI
Dr Petrina Kamya

Dr Petrina Kamya

President, Insilico Medicine Canada Inc., and Global Head of AI Platforms, VP, Insilico Medicine
Dr Simone Fulle

Dr Simone Fulle

Head of Protein Engineering, Novo Nordisk A/S

Daniel Cohen

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

Dr Anders Hogner

Associate Director, Medicinal Chemistry, AstraZeneca
Dr Anders Hogner

Anders Hogner PhD, is a drug hunter with +20 years’ experience in the pharmaceutical industry in the field of chemistry/computer aided drug design. Anders is currently head of Computational Chemistry in Medicinal Chemistry in Early Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D.

Part of Anders role involves co-leading AstraZeneca’s CADD strategy, global lead of IT demands from the business, and part of team delivering AstraZeneca’s Augmented Drug Discovery environment. During 2017-2018 Anders also acted as interim head of Computational Chemistry for Respiratory, Inflammation and Autoimmunity (RIA) disease unit. He has proven-track record of impacting projects milestones through innovative approaches resulted in being named co-inventor on 3 clinical drug projects and ability to bring in new methodologies and collaborations. He is passionate about talent management, mentoring, and developing people to achieve key deliverables in a performance-driven setting.

Dr Anne Goupil-Lamy

Science Council Fellow and Principal Field Application Scientist, BIOVIA
Dr Anne Goupil-Lamy

Dr Carl Poelking

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

I joined Astex as a machine learner in 2020, having gone through a continuous transition from physics (MSc, Uni Heidelberg), to computational chemistry (PhD, MPI for Polymer Research), to machine learning and applied statistics (postdoc, Uni Cambridge). My research interests encompass machine-learnt atomistic and molecular representations, machine-learnt force fields, reactivity and generative modelling, as well as nonlinear filtering approaches for sparse datasets – mostly in relation to structure-based drug design.

 

Dr Christina Schindler

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

Christina Schindler is an Associate Director and Head of Computational Drug Design in the Medicinal Chemistry & Drug Design department at Merck KGaA in Darmstadt, Germany. In her current role, she is co-leading Merck’s computational chemistry group and driving the CADD strategy. She is also a project leader in early drug discovery and IT projects. Christina has an extensive background in computational small molecule inhibitor and degrader design with a special focus on physics-based methods and alchemical free energy calculations. She holds a Ph.D. in Theoretical Biophysics from the Technical University of Munich and has co-authored more than 20 peer-reviewed publications.

Dr Christophe Chabbert

Principal Scientist in Data & Analytics, Roche Pharma Research and Early Development (pRED)
Dr Christophe Chabbert

Christophe obtained his PhD in Bioinformatics and Molecular biology from the European Molecular Biology Laboratory (EMBL) in 2015. After working both in the pharmaceutical industry and academia as a Data Scientist in Oncology, he joined Roche 5 years ago in pharma research to work on IT product development. He is currently the Product Line Manager of a Machine Learning platform and together with his team, focuses on helping data scientists take their ML/AI proof of concepts to production.

Dr Darren Green

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

Darren Green is Director of Molecular Design and Senior Fellow, GlaxoSmithKline. Based at Stevenage, his group specialises in the application of molecular design, data analysis, predictive modelling and chemoinformatics methods to drug discovery. Darren also leads the Compound Collection Enhancement strategy for GSK.


Darren has a PhD in Theoretical Chemistry from the University of Manchester. He is a Fellow of the Royal Society of Chemistry and chair of the Advisory Board for the Hartree Centre, the UK national laboratory for high performance computing, simulation and cognitive science.
 

Dr Francois-Xavier Blaudin de Thé

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

François-Xavier BLAUDIN de THE graduated in engineering from Ecole Polytechnique (2008-2011). Following an internship in Genentech in San Francisco and a Master of Science at Imperial College in London (chemical engineering and biotechnologies), he received a PhD in neuroscience from the college de France in Paris (2015) and was a postdoctoral researcher at Columbia University in New York, working on neurodegenerative diseases until 2019.

He then joined Servier to work in the department of neurology supporting therapeutic projects’ experimental strategy. With the creation of the neurology and immuno-inflammation therapeutic area (NITA), he became in charge of the data analytic approaches for the department. He is now program director of the data analytic roadmap for Servier R&D, piloting the development of analytical tools to support the concept phases of drug discovery.

Current scientific interests: artificial intelligence and computational modelling in support of target prioritization for complex diseases in the following therapeutic areas: neurology, immuno-inflammation and oncology.
 

Dr Gerhard Hessler

Head of Synthetic Molecular Design, Sanofi
Dr Gerhard Hessler

Dr Grégori Gerebtzoff

Director, Pharmacokinetic Sciences (PKS), Novartis Biomedical Research
Dr Grégori Gerebtzoff

After obtaining his PhD in biophysics from the University of Basel, Grégori Gerebtzoff spent 8 years at Roche in various departments: chemistry, preclinical safety, and research informatics. After a short interlude as a freelancer during which he implemented cheminformatics applications for Actelion, Grégori joined Novartis in 2015 in the PKS department, Biomedical Research.


Since 2020, Grégori leads the Data Science team within Modeling & Simulation, PKS, where he can share his experience and passion of data to his colleagues and drive the ADME/PK modeling strategy. He is also the business owner of Intuence, the next generation platform for small molecule Lead Optimization of Novartis.

 

Dr Guglielmo Iozzia

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

Guglielmo is a Biomedical Engineer with an extensive background in Software Engineering and ML/AI applied to different contexts, such as Biopharma Manufacturing, Healthcare and DevOps, just to mention the latest, and a lifelong learner. At MSD, as part of the Data Science and Applied Mathematics Team he is currently busy unlocking business value in the lab and manufacturing through ML/AI initiatives.
He is also an international speaker and author: currently he is writing his second technical book for Manning Publishing, where he is going to share his field experience on implementing domain-specific Large Language Models that can be also executed offline, on private data and on commodity hardware.

Dr Haruna Iwaoka

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

Haruna Iwaoka is a Senior Director of Advanced Modeling & Assay-Discovery Intelligence, Astellas Pharma Inc. She leads digital transformation of drug testing platforms leveraging AI and robotics. She received the Ph.D. degrees in agricultural chemistry from Tokyo University of Agriculture, in 2019. She joined Yamanouchi Pharma (Former Astellas) after graduate school of Tokyo University of Agriculture in 1994. Ever since she had been engaged in target discovery in drug discovery research, assay development in compound screening and assay using iPSCs. Recently, she has attracted worldwide attention, such as the development of new platform "Mahol-A-Ba" (SLAS Technol. 2023;28(5): 351-360 ) 
 

Dr Iain Moal

Scientific Leader, GSK Fellow, Computational Antibody Engineering, Medical Science & Technology, GSK Research & Development
Dr Iain Moal

Dr James Lumley

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

James has >23 years experience applying molecular design methods and software engineering practices to aid small-molecule drug discovery. At GSK he leads cross-disciplinary teams delivering small molecule project support including software & architecture.

His initial career includes 10 years in Biotech applying small-molecule design methods, primarily as Head of Computational Chemistry, Arrow therapeutics (subsidiary of AstraZeneca). He has inventorship on multiple antiviral drug candidates including AZD-7295 and later Sisunatovir (RV521) as a ReViral Ltd cofounder. In 2010 James joined Eli Lilly responsible for global Cheminformatics support. He led projects delivering award winning platforms, algorithms, models, and data pipelines including multiple CIO awards and a BioIT World Innovative Practice Award (Research.Data 2020).

Dr Maria Wendt

Global Head of Digital and and Biologics Strategy & innovation, Large Molecule Research, Sanofi
Dr Maria Wendt

Maria Wendt is the Global Head of Digital and Biologics Strategy and Innovation (DBSI), Large Molecule Research Platform at Sanofi, based in Cambridge, MA. She has spent over 20 years at the interface of computation and biology. At Sanofi, she oversees the strategic innovation portfolio creating the next generation of smart biologics medicines using innovative molecular engineering techniques, synthetic biology, and AI. Prior to her current position, she was Head of Biologics Research US (2019-2022) overseeing the antibody discovery and protein engineering operations supporting the US therapeutic biologics portfolio for all TAs. In 2021, she also established the Digital Biologics organization and launched LMR’s ambition in AI.

Prior to joining Sanofi, she held various positions at Genedata AG (2001-2018), culminating as Head of Science from 2015-2018. Genedata is a global informatics software and consulting firm for the life sciences, based in Basel, Switzerland. She was Founding Principal Scientist of several leading enterprise informatics platforms supporting end-to-end biologics research and development processes, used by the majority of top pharma and biotech worldwide. In the early part of her career, she focused on -omics Big Data and was one of the leaders of the EU Framework 6 InnoMed Predictive Toxicology consortium.

She obtained her Ph.D. in Chemical Engineering from Iowa State University and a B.S. in Chemical Engineering from the University of the Philippines.

Dr Martin Akerman

Co-Founder & CTO, Envisagenics
Dr Martin Akerman

Dr. Martin Akerman is the inventor of SpliceCore®, Envisagenics’ flagship platform born of his vision of applying machine learning to RNA information and discovering new drug targets in areas of unmet need. Martin trained as a postdoctoral fellow with Dr. Adrian Krainer at Cold Spring Harbor Laboratory, where he helped in the development of Spinraza®, the first FDA-approved RNA therapeutic for treating Spinal Muscular Atrophy. Dr. Akerman received his PhD in Bioinformatics from Technion, Israel Institute of Technology, where he studied how RNA splicing can boost functionality of the human genome and trigger diseases.

Dr Martin-Immanuel Bittner

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

Martin-Immanuel Bittner MD DPhil FRSA FRSB FIBMS is the Chief Executive Officer of Arctoris, the tech-enabled CRO that he co-founded in Oxford. Martin graduated as a medical doctor from the University of Freiburg in Germany, followed by his DPhil in Oncology as a Rhodes scholar at the University of Oxford. He has extensive research experience covering both clinical trials and preclinical drug discovery and is an active member of several leading cancer research organizations, including EACR, AACR, and ESTRO. In recognition of his research achievements, he has been elected as a member of the Young Academy of the German National Academy of Sciences and of Sigma Xi.

Dr Nadine Schneider

Lead, Generative Chemistry, Novartis Biomedical Research
Dr Nadine Schneider

Dr Nicola Richmond

VP of AI, BenevolentAI
Dr Nicola Richmond

Nicola Richmond is Vice President of AI, and is responsible for BenevolentAI’s AI strategy and ensuring the Company maintains its leading position in the AI-enabled drug discovery industry. Fundamentally, Nicola is driven by applying ML/AI technology to solve challenging problems in the drug discovery space to ultimately make a difference in patients’ lives.
Nicola has a PhD in pure mathematics and has worked at the intersection of AI and drug discovery for 22 years. During her post-doc, she developed the algorithm that lies at the heart of a commercial product (GALAHAD) that has been used throughout the worldwide pharmaceutical industry to elucidate 3D pharmacophores for virtual screening. Nicola joined GlaxoSmithKline (GSK) in 2004 where she made several key contributions. Her statistical approaches for actioning high-throughput screening data are in continual use across GSK’s early drug discovery portfolio, and her work on predicting the stable expression of therapeutic antibodies has reduced manufacturing cell line development timelines by 85%. Nicola also built and led the GSK.ai Fellowship Programme which has helped educate and inspire the next generation of brilliant minds who want to apply AI to drug discovery and impressively achieved a 50:50 ratio of women to men.
 

Dr Petrina Kamya

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

Dr Rabia Khan

Founder & CEO, Serna.bio
Dr Rabia Khan

<p>Rabia Khan, PhD (Immuno Genetics, MBA) is the founder and CEO of Serna Bio, a YC-backed biotech building the world's first map of the druggable transcriptome. She is a member of the Board of Trustees for the UK Dementia Research Institute, and an advisor to No Label Ventures, a fund investing in immigrant founders in the UK and EU.</p> <p>Prior to founding Serna Bio, Rabia was Managing Director, Discovery Sciences at Sensyne Health plc (now - Arctoris Data) where she established the scientific strategy, built the data science and discovery teams by recruiting and leading a team of 50 machine learning and clinical researchers, delivering on a number of significant pharma partnerships including Bayer, BMS, Roche and Alexion.</p> <p>She also held senior roles at BenevolentAI and Meta (acquired by Chan Zuckerberg BioHub). At Meta (previously Sciencescape), she was pivotal in the partnership with the Intelligence Advanced Research Projects Activity (IARPA) to acquire horizon-scanning technology that used NLP to research the biomedical corpus. At BenevolentAI, she helped shape the discovery strategy for a number of programs, led the Age-Related Macular Degeneration and Glioblastoma drug discovery programmes and served as the interface between the technical and biological teams under the mentorship of Prof. Jackie Hunter.</p> <p>Born and raised in Pakistan, Rabia has a passion for supporting diversity in technology, and supporting initiatives focused on improving access to care for Schizophrenia and Dementia. <br /> &nbsp;</p>

Dr Simone Fulle

Head of Protein Engineering, Novo Nordisk A/S
Dr Simone Fulle

Simone is heading the Protein Engineering department within the Computational Drug Design area at Novo Nordisk, which is focusing on de novo protein design and protein engineering, employing Molecular Modelling, Data Science, and Machine Learning approaches. Activities also include engaging with external collaborations in the field of AI-driven drug discovery and compound design.

Before joining Novo Nordisk, Simone was group leader at BioMed X in Heidelberg, where she developed together with her team computational methods that support the design of selective kinase inhibitors.

Simone obtained her PhD from the Goethe-University in Frankfurt and did a PostDoc at a spin-out company from the Oxford University.

Dr Thierry Dorval

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

Thierry Dorval received a B.S. degree in theoretical physic and obtained a Ph.D. in image processing and artificial intelligence at Pierre & Marie Curie University, Paris, France. He then joined the Institut Pasteur Korea in 2005 first as researcher in biological image analysis then as a group leader specialized in High Content Screening applied to cellular differentiation as well as toxicity prediction. In 2012 he joined AstraZeneca, UK, where he was leading the Image and Data Analytics team. His activities were about developing and advising on quantitative image and data analysis solutions in support of high content phenotypic screens.


In 2015 he joined Servier, France, first as leader of the High Content Screening group within CentEX CPCB and then as Head of Data Science Lab, working on phenotypic approaches to improve drug discovery pipeline efficiency using high content and machine learning strategies.

 

Dr Yogesh Sabnis

Director, Lead Design, UCB Biopharma srl
Dr 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.

Karl Leswing

Executive Director, Machine Learning, Schrödinger
Karl Leswing

Karl Leswing is the Executive Director for Machine Learning at Schrödinger. In this role he oversees the research and execution of machine learning applications for Schrödinger’s digital chemistry platform.


In 2017 he was a visiting researcher at the Pande Lab working on using deep learning techniques for drug discovery. During that time he co-authored MoleculeNet, a benchmarking paper analyzing machine learning techniques for chemoinformatics.


Karl received his undergraduate degree from the University of Virginia, and a Master’s in machine learning from Georgia Tech.
 

Martin Buttenschoen

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

Martin Buttenschoen is a third-year PhD student at the University under the supervision of Professors Charlotte Deane, Garret Morris, and Michael Bronstein. His research primarily revolves around machine learning applications in de-novo drug design, specifically small molecule pose prediction, also known as small molecule docking.

Michael Edmund Beck

Distinguished Science Fellow, Bayer AG
Michael Edmund Beck

As scientist and science communicator with a wide range of achievements in academia and industry, I have held various managerial positions of increasing responsibility within Bayer AG, currently with the status of a distinguished science fellow. In addition, I am scientific advisor for a Max-Planck spin-off start-up, Faccts GmbH, as well as for a open-source initiative, OpenFold. I am co-inventor of a marketed agrochem-product and have continuously contributed to product (re-)registrations.
Besides science, I’d like to emphasize my record as a mentor of scientific talents across various scientific disciplines. My passion for teaching was awarded an honorary professorship @ TU Dortmund.

Philipe Moingeon

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

Philippe MOINGEON received his PhD in immunology from Paris XI University, and an MBA from Open University Business School (Newcastle, UK). He was formerly Resident of Paris Hospitals (1981-1986), Post-doctoral fellow (1987-1990) and Assistant Professor (1991-1994) at the Dana Farber Cancer Institute, Department of Pathology, Harvard Medical School (Boston, USA).

Philippe has extensive experience in the development of biologicals, with several positions held in the vaccine industry, including Head of the Cancer Vaccine program and General Secretary for Research and Development, at Aventis Pasteur (1994-2003), as well as Senior Vice President, Research and Pharmaceutical Development at Stallergenes (2003-2017). He joined Servier in September 2017, to lead the Therapeutic area immuno-inflammation. He is ‘Professeur associé’ of AI and drug sciences at Université Paris-Saclay.

Two vaccines registered in Europe, USA and Japan, 230 scientific publications in peer-reviewed journals, 23 patents/ patent applications.

Current scientific interests: artificial intelligence and computational modelling in support of drug development; applications to auto-immune, inflammatory and neurological diseases.
 

Philipp Lorenz

CTO, Basecamp Research
Philipp Lorenz

Phil Lorenz is the CTO at Basecamp Research where he leads the genomics, deep learning, and product development functions. With previous experience in the pharmaceutical industry, Phil holds a Ph.D. in genomics and machine learning as well as a master’s degree in biochemistry from the University of Oxford.

Quentin Perron

Co-Founder & CSO, Iktos
Quentin Perron

Quentin Perron is a medicinal chemist by training. He holds a PhD in organometallic chemistry from the University of Geneva. During his post-doc fellowship at UCLA he worked on the total synthesis of Brasillicardin A, a complex natural molecule known for having a potent immunosuppressive activity. After working as a medicinal chemist in CNS indications at Laboratoires Servier, he switched to data science and chemoinformatics at Quinten, a company specialized in data science services. In 2016, with his business partners Yann Gaston-Mathé and Nicolas Do Huu, he co-founded Iktos, a start-up company developing AI technologies for new drug design. He is now the CSO of the company.

 Why should you attend?

Get ready for the highly-anticipated return of the AI in Drug Discovery Conference this March 2024. In the interim since our last gathering in March 2023, the industry has achieved a ground breaking milestone – the first wholly AI-discovered and designed drug is now undergoing Phase 2 clinical trials.

Four compelling reasons as to why you can't miss out:

  • Gain insights from leading biotechs and big pharma on drug target selection
  • Explore neural networks with a focus on digital twins, new drug designs and in-silico modelling to expand your portfolio
  • Get the latest insights on the benefits of machine learning from molecule design to clinical trial selection, along with crucial patient-centric considerations.
  • Discuss best use and potential impact of Generative AI in light of the launch of Chat GPT

Join us as we navigate through the hype and unveil effective strategies to harness the AI revolution for achieving tangible and transformative outcomes in the realm of drug design and discovery.

Benefit from the insights of our diverse speakers, leveraging their expertise to propel the field forward into clinical trials and introduce transformative new drugs to the market.

Who should attend?

All companies wishing to get therapeutics to patients faster and thus extending healthy longevity for everyone.

Build your AI ecosystem combining internal and external resources and partners to discover new ways to treat diseases, design novel molecules, optimise pre-existing treatments, and reducing the cost & time of drug development.

Wish to share ideas, collaborate, and learn from successes and difficulties in both data science and drug discovery – fast-changing field that need interdisciplinary skills & experience.

Mature your company’s approach to AI and ML application to hurdle the major challenges and avoid the pitfalls that caused previous failures in the first wave of initiatives. Benchmark your pipeline against the latest understanding of strengths and weaknesses in leveraging these tools.

Gain insight into the true nature of the major challenges – away from algorithms and tools, towards data and technical leadership.

Join us in March!
 

sponsors

Conference agenda

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8:00

Registration & Coffee

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8:45

SAE Media Group Welcome

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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

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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    9:30

    End-to-End AI Integration in Drug Discovery: From Data Generation to Clinical Trials in the Insilico Pipeline

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

  • Enabling end-to-end AI integration: Successes of the Pharma.AI platform
  • The importance of supporting AI platforms with state-of-the-art laboratories for data generation
  • Overview of preclinical and clinical pipeline
  • Progressing AI-discovered and designed molecules to Human Trials: Successes and Challenges
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    10:00

    Latest Advancements in Machine Learning-Enhanced In Silico Design: Impact on A Pipeline of Drug Discovery Programs

    Karl Leswing, Executive Director, Machine Learning, Schrödinger

  • Case studies using active learning with FEP+ for large-scale in silico fragment screening for hit discovery
  • Applications of de novo design workflows for intelligent molecular core design
  • Leveraging experimental data for enhancing ADMET profiles in lead optimization using an interactive ML dashboard
     
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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

  • 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

     

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

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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

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

  • 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
     
  • Dr Grégori Gerebtzoff, Director, Pharmacokinetic Sciences (PKS), Novartis Biomedical Research

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    12:30

    Session Reserved for HPE

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    13:00

    Networking Lunch

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    14:00

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

    Dr Christophe Chabbert, Principal Scientist in Data & Analytics, 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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    14:30

    Pioneering Augmented Drug Design for Small Molecules

    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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    15:00

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

    Dr Gerhard Hessler

    Dr Gerhard Hessler, Head of Synthetic Molecular Design, Sanofi

  • 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
     
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    15:30

    D2P2, a ML Platform to Facilitate Improved Small Molecule Design through Accurate Property Predictions

    Dr Yogesh Sabnis, Director, Lead Design, UCB Biopharma srl

  • Current challenges with discovery of small molecules
  • Data-Driven Predictive Platform, D2P2, for improved multi-property predictions and design optimisations
  • Unpacking new insights early and rapidly with Advanced Analytics
  • Lessons learned and personal reflections from UCBs effort in small molecule discovery
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    16:00

    Afternoon Tea

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    16:30

    Next-Generation Life Science AI Models & Gene Therapies fuelled by a Global Biodiversity Data Supply Chain from Five Continents

  • Basecamp Research has built partnerships with Nature Parks across 5 continents covering 60% of global biomes to establish a biological data supply chain. This data enters our knowledge graph, BaseGraph, of 6 billion relationships connecting 100s of millions of never-seen-before protein and genome sequences to their evolutionary context
  • With 5x greater sequence diversity captured in BaseGraph compared to public data, we leverage our data advantage for state-of-the-art beating deep learning models in functional annotation, structure prediction, and LLM-based protein design
  • The genomic context captured in BaseGraph enables us to develop next-generation gene writing technologies and offer our partners FTO for existing gene editing tools
  • Philipp Lorenz, CTO, Basecamp Research

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    17:00

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

    Dr 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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    17:30

    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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    18:00

    Chair's Closing Remarks

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

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    8:15

    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

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

    Dr Christina 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

    Bridging Artificial Intelligence and Quantum Chemistry

    Michael Edmund Beck, Distinguished Science Fellow, Bayer AG

  • What is Quantum Chemistry, and what is it good for in industry?
  • Synergism of first principles simulations and data driven AI
  • Combining ML and QM for Improved Property Prediction
  • clock

    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
  • clock

    10:30

    Advancing Drug Discovery Across Different Modalities with Physics-Based Modeling, AI and Machine Learning

    Dr Anne Goupil-Lamy

    Dr Anne Goupil-Lamy, Science Council Fellow and Principal Field Application Scientist, BIOVIA

  • Target characterisation: Combining physics-based modelling with Alphafold2/Openfold AI models
  • Small molecule therapeutics design: Seamless integration of virtual modelling and real lab data
  • Biotherapeutics design and optimization: Utilizing validated in silico techniques enhanced by AI and machine learning

     

  • clock

    11:00

    Morning Coffee

    clock

    11:30

    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
  • clock

    11:50

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

    Dr Rabia Khan, Founder & 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
  • clock

    12:10

    Data Generation Joint Q&A

  • Audience questions to focus session speakers
  • Speaker response to other focus session presentations
  • Reflections on data generation progress & challenges across the industry
  • Dr Rabia Khan, Founder & CEO, Serna.bio

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

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

    clock

    12:30

    Integrating AI and robotics for efficient drug design and discovery

    Quentin Perron, Co-Founder & CSO, Iktos

  • GenAI coupled with structural information: from hit discovery to lead optimization
  • AI driven retro-synthesis with robotic constraints inside
  • From virtual molecules to robots to streamline DMTA cycles
  • clock

    13:00

    Networking Lunch

    clock

    14:00

    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
  • clock

    14:30

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

    Dr Francois-Xavier Blaudin 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

    clock

    15:00

    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
  • clock

    15:30

    Knowing Why: Target Prediction with Explainable Large Language Models

    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

     

  • clock

    16:00

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

    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
  • clock

    16:30

    Afternoon Tea

    clock

    17:00

    AbLang: Building Language Models for Antibody Engineering

    Dr Iain Moal

    Dr Iain Moal, Scientific Leader, GSK Fellow, Computational Antibody Engineering, Medical Science & Technology, GSK Research & Development

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

    clock

    17:30

    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
  • clock

    17:50

    Generative AI Joint Q&A

    Dr Nadine Schneider

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

    Dr Nicola Richmond, VP of AI, BenevolentAI

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

    Dr Iain Moal

    Dr Iain Moal, Scientific Leader, GSK Fellow, Computational Antibody Engineering, Medical Science & Technology, GSK Research & Development

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

    clock

    18:10

    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

    Dr Simone Fulle, Head of Protein Engineering, Novo Nordisk A/S

    Dr Yogesh Sabnis, Director, Lead Design, UCB Biopharma srl

    clock

    18:40

    Chair's Closing Remarks

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


    President, Valence Labs and Vice President
    Recursion Pharmaceuticals
    Associate Director, Medicinal Chemistry
    AstraZeneca
    Science Council Fellow and Principal Field Application Scientist
    BIOVIA
    Senior Researcher, Computational Chemistry & Informatics
    Astex Pharmaceuticals
    Associate Director, Head of Computational Drug Design
    Merck KGaA
    Principal Scientist in Data & Analytics
    Roche Pharma Research and Early Development (pRED)
    Head of Cheminformatics & Data Science, Senior Felllow
    GSK
    Computational Scientist
    Servier Pharmaceuticals
    Head of Synthetic Molecular Design
    Sanofi
    Director, Pharmacokinetic Sciences (PKS)
    Novartis Biomedical Research
    Associate Director - Data Science, ML/AI, Computer Vision
    MSD Ireland
    Senior Director, Advanced Modeling & Display, Discovery Intelligence
    Astellas Pharma Inc.
    Scientific Leader, GSK Fellow, Computational Antibody Engineering, Medical Science & Technology
    GSK Research & Development
    Associate Director, Cheminformatics and Data Science
    GSK
    Global Head of Digital and and Biologics Strategy & innovation, Large Molecule Research
    Sanofi
    Co-Founder & CTO
    Envisagenics
    Co-Founder and CEO
    Arctoris
    Lead, Generative Chemistry
    Novartis Biomedical Research
    VP of AI
    BenevolentAI
    President, Insilico Medicine Canada Inc., and Global Head of AI Platforms, VP
    Insilico Medicine
    Founder & CEO
    Serna.bio
    Head of Protein Engineering
    Novo Nordisk A/S
    Head of Data Sciences & Data Management
    Servier Pharmaceuticals
    Director, Lead Design
    UCB Biopharma srl
    Executive Director, Machine Learning
    Schrödinger
    Research Student, Oxford Protein informatics Group
    University of Oxford
    Distinguished Science Fellow
    Bayer AG
    Professor, AI and Drug Sciences, Paris-Saclay University and Head of Immuno-Inflammation Portfolio
    Servier Pharmaceuticals
    CTO
    Basecamp Research
    Co-Founder & CSO
    Iktos

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    179-199 Holland Park Avenue, London, United Kingdom

    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.”

    A number of our clients have been approached by third party organisations offering to book hotel rooms. We would advise that you do not book through them as they are not representing the SMi Group. SMi Group books all hotel rooms directly. If you are approached by a third party organisation then please contact us before making any bookings. If you have already booked a hotel room using a third party organisation, we would highly recommend contacting the hotel you were booked into to ensure a booking has been made for you. We would also advise you to please check the terms and conditions of the booking carefully.
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    Sponsors


    Basecamp Research

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    Basecamp Research is a London based generative AI biotech company founded in 2020 and has raised $25 million to date. The team solves the hardest design challenges for partners in gene editing therapeutics & enzyme development through their state-of the-art-beating deep learning models which are fuelled by the data advantage derived from their partnerships with nature parks across 5 continents.


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    Iktos specializes in the development of artificial intelligence solutions applied to chemical research, more specifically medicinal chemistry and new drug design. Iktos is developing a proprietary and innovative solution based on deep learning generative models, which enables, using existing data, to design molecules that are optimized in silico to meet all the success criteria of a small molecule discovery project.


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    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

    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.”
    HOTEL BOOKING FORM

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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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