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SMi is proud to announce the 2nd annual AI in Drug Discovery virtual conference taking place on 15th and 16th March 2021

With the global Artificial Intelligence (AI) in Drug Discovery Market size expected to exceed $3,900 million by 2027, this year’s agenda will encompass key drivers leading the way into a shorter, cheaper, and more successful R&D era. The conference will cover hot topics including; machine learning techniques for improving early drug discovery, effective prediction of ADMET properties, data quality, and innovative applications of AI for undruggable targets.


This year’s event will focus on 4 key themes: machine learning and automation for improved drug discovery pipelines; effective prediction of compound properties; data robustness and curation; innovative use of AI for rare and undruggable diseases. The agenda will highlight key case studies across these themes, uncovering key developments in pharma of data optimization and to aid therapeutic discovery. Real-world examples of AI in structure-based drug design and patient relevant data will be explored.
 

This two-day agenda offers you peer-to-peer networking with industry experts including directors and heads of informatics, data & AI, molecular design, and computational chemistry, to explore the latest developments in the industry, regulatory updates, and case studies from leading pharmaceutical and biotechnology companies.

AI in Drug Discovery will explore the latest industry case studies from leading pharma and biotech companies on machine learning and improved drug discovery pipelines. Understand how AI can be effectively implemented to enhance discovery R&D, delve into best practices in data quality, curation and validation, and examine novel uses of AI to unlock therapies that have evaded traditional drug discovery.
 

The SMi Virtual Conference Platform will deliver the following benefits:

  • Live and On Demand speaker content: Get access to the latest strategies and case studies from your market place online!
  • Network with all the event attendees: Connect, see who’s attending, chat and share contact details with all online delegates, speakers and sponsors
  • Exhibit a Virtual Booth: You can pack your customized booth full of documents, videos and even show who is manning the booth during the event and hosting meetings
  • Host & Join Meetings & Socials: Join preferred speaking sessions, host you own meetings and even a virtual Networking social, with in built Zoom functionality

 Key job titles include:

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

AbbVie; Astra Zeneca Plc; AstraZeneca; Aurelia Bioscience; Axol Bioscience Ltd; Bayer AG; Bayer HealthCare; Bayer Pharma AG; BB Consultants Ltd; BenevolentAI; Eli Lilly and Company Limited; ELS; Enplas Europe Ltd; Epizyme; ETH Zürich; Exscientia; F Hoffmann-La Roche; GHPC GmbH; Glaxo Smith Kline; GlaxoSmithKline; Goethe University; GSK; H Lundbeck A/S, Library & Info Ctr; Japan Tobacco; JSC R-PHARM; MD Biosciences Inc; MedImmune; National Institute Of Advanced Industrial Science And Technology; Novartis Pharmaceuticals; Promega UK Ltd; Sanofi-Aventis; Schrodinger ; Selcia Ltd; Sygnature Discovery; Technology Networks; ThermoFisher; TissUse GmbH; UCB BioPharma SPRL; UCB-Celltech; Vernalis Research Ltd;

Conference programme

8:30 Registration & Coffee

9:00 Chairman's Opening Remarks

Darren Green

Darren Green, Director of Molecular Design, GSK

9:10 Applications of data science and AI in drug discovery - an industry outlook

Darren Green

Darren Green, Director of Molecular Design, GSK

  • Challenges facing drug discovery pipelines in pharmaceutical R&D
  • Opportunities for data analysis and predictive modelling in drug design
  • Examples of progress
  • Emerging applications of AI for early drug discovery
  • 9:50 AI(d) in informing drug discovery

    Hugo Ceulemans

    Hugo Ceulemans, Scientific Director Discovery Data Sciences, Janssen Global Services, LLC
    View Bio

  • Microscopy images as a data source to drive drug discovery
  • Towards better combining chemistry and biology
  • MELLODDY: federated and privacy-preserving machine learning across pharma
  • 10:30 Morning Break Virtual Exhibition Hall Open

    11:00 In silico peptide and protein design

    Simone Fulle

    Simone Fulle, Head of Molecular modelling & Design, Novo Nordisk
    View Bio

  • Applications and opportunities for AI/ML in biologics design
  • Machine learning techniques for peptides
  • Challenges for the implementation of AI in pharmaceutical R&D
  • 11:40 Recent advances in machine learning for ADMETox prediction

    Igor  Tetko

    Igor Tetko, CEO, BigChem GmbH
    View Bio

  • Multitask learning, data integration
  • NLP, transformers, and other descriptor-less modelling
  • Interpretation of ADMETox models; explainable AI
  • Estimation of the accuracy of predictions and applicability domain
  • 12:20 Using ML/AI and data analytics tools to accelerate drug discovery by mitigating ADMET issues

    Barun Bhhatarai

    Barun Bhhatarai, Investigator, Novartis Institute of Biomedical Research, Novartis Institutes for BioMedical Research
    View Bio

  • How ML can be used to predict compound properties
  • Using ML/QSP tools to predict interactions between drug and target and enhance safety outcomes
  • How to use prediction data proactively to design enhanced therapeutics
  • Case study: use of ML/AI in ADMET prediction and impact on enhancing compound design
  • 13:00 Networking Break Virtual Exhibition Hall Open

    14:00 Bringing operational AI to pre-clinical research workflows

    Christophe  Chabbert

    Christophe Chabbert, Senior Bioinformatics Scientist, pRED, Roche
    View Bio

  • Machine learning and deep learning models are rapidly being adopted throughout the entire drug discovery value chain
  • Deploying business models in business processes is a critical step towards generating business value and transforming pharma research
  • Handling productive deployments and managing models throughout their life cycle requires dedicated and collaborative efforts
  • 14:40 AI-driven drug discovery platform for splicing driven diseases

    Maria Luisa Pineda

    Maria Luisa Pineda, CEO and Co-founder, Envisagenics
    View Bio

  • Introduction to Envisagenics' SpliceCore drug discovery platform, integrating machine learning, high performance computing, and RNA-splicing analytics to identify new therapeutic targets
  • Hybrid business model - ability to partner with biopharmaceutical companies and develop in-house therapeutic pipelines
  • scalable cloud computing that enables efficient and cost effective processing of RNA-sequencing datasets
  • 15:20 Afternoon Break Virtual Exhibition Hall Open

    15:50 AI for AID: artificial intelligence in support of new drug development for autoimmune diseases

    Philipe Moingeon

    Philipe Moingeon, Department head, Servier Pharmaceuticals
    View Bio

  • AI-based disease modelling from patient molecular profiling data
  • AI-educated target identification, drug discovery and repurposing
  • First steps towards in silico clinical studies
  • 16:30 Antibody design using synthetic biology and machine learning at BigHat Biosciences

    Mark DePristo

    Mark DePristo, CEO & CoFounder, BigHat BioScinces
    View Bio

  • An introduction to AI/ML in drug discovery
  • An overview of the outstanding challenges in antibody design and engineering
  • Integrating a high-speed wet lab and machine learning for antibody design and engineering
  • Case studies of creating antibodies on BigHat's platform
  • 17:10 Chairman’s Closing Remarks and Close of Day One

    8:30 Registration & Coffee

    9:00 Chairman's Opening Remarks

    9:10 AI in drug discovery - where do we currently stand, and what might be missing?

    Andreas Bender

    Andreas Bender, Lecturer for Molecular Informatics, University of Cambridge
    View Bio

  • AI in drug discovery - available data and problem setting from discovery to safety/efficacy stages
  • Chemical and biological data, and the problem of labelling what we have
  • Some examples and case studies
  • How to move on from here: From ligand discovery to drug discovery
  • 9:50 AstraZeneca's 2 million genomes initiative and the relevance of FAIR data principles in pharmaceutical R&D

    ChuangKee Ong

    ChuangKee Ong, Associate Director, Information Architect Lead, AstraZeneca
    View Bio

  • The importance of transparent, robust data architecture in pharmaceutical R&D
  • The relevance of FAIR data principles as an enabler for new AI analytics to access data for machine learning and prediction
  • Cast study: AstraZeneca's 2 Million Genomes Initiative has utilised FAIR data principles to enhance productivity in their pharmaceutical R&D
  • Utilising data design to maximise value and deliver enhanced R&D
  • 10:30 Morning Break Virtual Exhibition Hall Open

    11:00 Natural Language Processing: an overview of NLP opportunities and challenges in pharma

    Peter Henstock

    Peter Henstock, Machine Learning & AI Lead, Pfizer Global Pharmaceuticals
    View Bio

  • Review some of the fundamental Natural Language Processing subproblems
  • Describe some interesting areas where pharma companies can leverage text mining
  • Discuss some of the challenges faced in some use cases
  • 11:40 Drug Discovery crawler - a new platform for AI and Big Data in drug discovery with the objective of accelerated starts in new drug design

    Ed Addison

    Ed Addison, Chairman and CEO, Cloud Pharmaceuticals
    View Bio

  • System “crawls” published databases, the literature and GWOS data to identify and discover novel targets
  • Runs a multitude of software programs to identify hits and leads for each from Simulation, AI, known databases and open source contributions
  • Stores target properties (on target effects, pathways, etc.) and lead properties (Pk, solubility, off target effects, toxicity score, etc.)
  • Considers traditional targeted therapeutics, but also poly pharmacy, novel design, repurposing, and modulated biology discovery paradigms
  • Covers small molecules, peptides, protein-protein interactions and large molecules
  • Drug safety of similar drugs
  • 12:20 Data-driven external innovation for a biotech company

    Lili Peng

    Lili Peng, Associate Director Scientific Ecosystem Analytics Lead, Biogen
    View Bio

  • Review which external models of R&D innovation are gaining recognition as a valuable strategy for pipeline-building and enabling creative business development engagements
  • Hear how Biogen’s External Innovation Unit uses various disparate data sources and descriptive and predictive analytics (AI/ML) capabilities to identify innovation opportunities
  • Discuss the challenges and lessons learned during the software implementation of an analytics platform
  • 13:00 Networking Break Virtual Exhibition Hall Open

    14:00 Challenges and opportunities in applying Ml/AI to drug target indication prioritisation

    Liling Warren

    Liling Warren, Director, Head of Translational Medicine Statistics, Teva Pharmaceuticals
    View Bio

  • Challenges and opportunities in integrating genetic, genomic, real world, clinical and biological analysis for drug target indication prioritisation
  • Working with ML/AI to create meaningful and actionable data interpretations
  • Drawing on traditional statistics to get better mileage from AI
  • 14:40 How can AI be exploited to identify the right biological targets?

    15:20 Afternoon Break Virtual Exhibition Hall Open

    15:50 Drug design with machine learning for challenging targets

    Noor Shaker

    Noor Shaker, Founder & CEO, Glamorous AI
    View Bio

  • Why do we need AI for drug discovery?
  • Recent advances in the use of AI for drug design and optimisation
  • Challenges in ML-driven drug design
  • Use cases for the design of novel small-molecules on a challenging target
  • 16:30 Advancing Drug Discovery with Machine learning and Polypharmacology to strengthen Patient outcomes

    Naheed Kurji

    Naheed Kurji, CEO, Cyclica
    View Bio

  • Bridging knowledge- and structure-based approaches to enable greater predictive performance and expedite drug discovery cycles
  • Advantages of going wide with a polypharmacology-enabled approach to de-risk off-target effects and design small molecules with greater precision
  • Ushering business model innovation to Create the Biotech Pipeline of the Future and the race to address unmet patient needs
  • 17:10 Chairman’s Closing Remarks and Close of Day Two

    Darren Green

    Darren Green, Director of Molecular Design, GSK

    +

    FEATURED SPEAKERS

    Andreas Bender

    Andreas Bender

    Lecturer for Molecular Informatics, University of Cambridge
    Barun Bhhatarai

    Barun Bhhatarai

    Investigator, Novartis Institute of Biomedical Research, Novartis Institutes for BioMedical Research
    Christophe  Chabbert

    Christophe Chabbert

    Senior Bioinformatics Scientist, pRED, Roche
    ChuangKee Ong

    ChuangKee Ong

    Associate Director, Information Architect Lead, AstraZeneca
    Ed Addison

    Ed Addison

    Chairman and CEO, Cloud Pharmaceuticals
    Hugo Ceulemans

    Hugo Ceulemans

    Scientific Director Discovery Data Sciences, Janssen Global Services, LLC
    Igor  Tetko

    Igor Tetko

    CEO, BigChem GmbH
    Lili Peng

    Lili Peng

    Associate Director Scientific Ecosystem Analytics Lead, Biogen
    Liling Warren

    Liling Warren

    Director, Head of Translational Medicine Statistics, Teva Pharmaceuticals
    Maria Luisa Pineda

    Maria Luisa Pineda

    CEO and Co-founder, Envisagenics
    Mark DePristo

    Mark DePristo

    CEO & CoFounder, BigHat BioScinces
    Naheed Kurji

    Naheed Kurji

    CEO, Cyclica
    Noor Shaker

    Noor Shaker

    Founder & CEO, Glamorous AI
    Peter Henstock

    Peter Henstock

    Machine Learning & AI Lead, Pfizer Global Pharmaceuticals
    Philipe Moingeon

    Philipe Moingeon

    Department head, Servier Pharmaceuticals
    Simone Fulle

    Simone Fulle

    Head of Molecular modelling & Design, Novo Nordisk

    Andreas Bender

    Lecturer for Molecular Informatics, University of Cambridge
    Andreas Bender

    Dr Andreas Bender is a Reader for Molecular Informatics with the Centre for Molecular Science Informatics at the Department of Chemistry of the University of Cambridge, as well as an Associate Director for Computational Safety within Clinical Pharmacology & Safety Sciences (CPSS) at AstraZeneca. In his work, Andreas is involved with the integration and analysis of chemical and biological data, aimed at understanding phenotypic compound action (such as cellular readouts, and also organism-level effects) on a mechanistic level, predicting molecular properties related to both compound effiacy and toxicity, as well as drug repurposing. He received his PhD from the University of Cambridge and worked in the Lead Discovery Informatics group at Novartis in Cambridge/MA as well as at Leiden University in the Netherlands before his current post.

    Barun Bhhatarai

    Investigator, Novartis Institute of Biomedical Research, Novartis Institutes for BioMedical Research
    Barun Bhhatarai

    Dr. Bhhatarai is an Investigator for Pharmacokinetic Sciences (PKS) department at Novartis Institute for Biomedical Research (NIBR) at Cambridge, MA. He has over 10 years of experience on using various ML and data analytics techniques to support small molecule drug discovery projects. At Novartis, he guides project team members from several therapeutic areas, to mitigate ADMET and PK/PD related issues during the lead optimization and drug candidate selection stages. Prior to that, Dr. Bhhatarai was Systems Toxicology Informatics Fellow in the Predictive Safety Assessment Center at The Dow Chemical Company in Midland, MI. Here, he was responsible for the development, assessment and implementation of novel in-silico alternative approaches aimed at predicting potential mammalian and environmental health effects. He was involved in various Systems Toxicology research projects. He also contributed actively in various workshops related to acute toxicity advocating Alternative Approaches for Identifying Acute Systemic Toxicity. Dr. Bhhatarai is a member of the American Society for Cellular and Computational Toxicology (ASCCT), the American Chemical Society (ACS), IQ consortium and the Society of Toxicology (SOT). He has authored 25+ peer-reviewed publications and several presentations.

    Christophe Chabbert

    Senior Bioinformatics Scientist, pRED, Roche
    Christophe  Chabbert

    After graduating from an engineering school, Mines Paristech in Paris, Christophe received a PhD in molecular biology and bioinformatics from the University of Heidelberg/EMBL. Passionate about applying advanced analytics and machine learning to answer complex research questions in Oncology, Christophe worked in several large research and pharmaceutical organizations such as AstraZeneca and ETH Zurich. He has been leading collaborative projects around data integration and AI Ops in Roche over the past 2.5 years.

    ChuangKee Ong

    Associate Director, Information Architect Lead, AstraZeneca
    ChuangKee Ong

    ChuangKee (CK) is a proven research and informatics leader in the life science and pharmaceutical industry with solid technical leadership and excellent track records in informatics projects delivery. In his current role as Associate Director, Early Science Information Architecture (Bioinformatics) AstraZeneca UK Ltd, he provides bioinformatics domain leadership to drive the company’s data strategy in target identification and pre-clinical space to achieve FAIRification of research data. He is also responsible for informatics solutions delivery, focusing on the 2 million genomes initiative. Previously he worked on high impact projects such as Ensembl and OpenTargets. Most recently, Data Integration Manager at OpenTargets, a pre-competitive collaboration between EBI, GSK, Sanger Institute, Takeda et al. There, he is responsible for data provision across different EBI teams, that supply various datatypes from genomic, clinical trial, literature, multi-omics, etc. He also led the data integration efforts through common disease ontology. Other roles he has held previously are Principal Scientist, VP at Sime Darby Technology Center, Senior Associate Scientist at Eli Lilly
    and Bioinformatics Scientist at Medical Research Council in Edinburgh, Scotland.
     

    Darren Green

    Director of Molecular Design, GSK
    Darren Green

    Ed Addison

    Chairman and CEO, Cloud Pharmaceuticals
    Ed Addison

    Ed Addison, is Chairman and CEO of Cloud Pharmaceuticals, a Research Triangle Park company from Duke University that applies AI to drug design and discovery, achieving unprecedented results. Cloud Pharmaceuticals has designed new drug candidates for cancer, metabolic diseases and other indications. With over 30 years experience, Ed is on the advisory board and/or board of directors of a number of companies deploying exponential technologies in life sciences, and artificial intelligence, including Drug Logic, Parallel Profile, New Life Cures, and Quantum Cures Foundation.

    Hugo Ceulemans

    Scientific Director Discovery Data Sciences, Janssen Global Services, LLC
    Hugo Ceulemans

    As Scientific Director Discovery Data Sciences Hugo currently heads a multidisciplinary team that supports drug discovery with machine learning approaches. This team advises on impactful data generation and combines new with existing data to formalize and improve biological and chemical criteria for molecule selection design, and propose attractive and efficiently synthesizable molecules to make and test. Hugo holds the degrees of MD, MSc in Bioinformatics and PhD in Molecular Biology, and prior to joining Janssen in 2008, he completed postdoctoral fellowships in molecular and computational biology at the University of Leuven and in structural bioinformatics at the European Molecular Biology Laboratories in Heidelberg.

    Igor Tetko

    CEO, BigChem GmbH
    Igor  Tetko

    Dr. Tetko is CEO of BIGCHEM GmbH, associative editor of ChemResTox as well as coordinator of the Marie Sklodowska-Curie Innovative Training Network European Industrial Doctorate Horizon2020 project “Advanced machine learning for Innovative Drug Discovery” http://ai-dd.eu. He received MSc degree from Moscow Institute of Physics and Technology, PhD in chemistry from the Ukrainian Academy of Sciences as well as habilitation in cheminformatics from the University of Strasbourg. His research interests include machine learning, neural networks, chemoinformatics, application of machine learning to predict physico-chemical properties and toxicity of molecules, drug-design and Big Data analysis.

    Lili Peng

    Associate Director Scientific Ecosystem Analytics Lead, Biogen
    Lili Peng

    Dr. Lili Peng is currently the Associated Director of Analytics in Biogen’s External Innovation unit, where she is applying science, data, informatics, and technology to facilitate data-driven decision-making towards expanding Biogen's drug portfolio. She was formerly a strategy and technology consultant, Booz Allen Hamilton, where she executed data science and software implementation projects at the FDA and NIH. Prior to that, Dr. Peng worked as an informatics scientist at AstraZeneca providing analytics solutions in drug discovery and development. Dr. Peng completed her postdoctoral training in computational chemistry and bioinformatics, respectively, at University of California, San Francisco and Stanford University. She completed her Ph.D. thesis in theoretical chemistry from the University of California, San Diego. She graduated Tau Beta Pi from the Massachusetts Institute of Technology with a bachelor’s in chemical engineering.

    Liling Warren

    Director, Head of Translational Medicine Statistics, Teva Pharmaceuticals
    Liling Warren

    Liling Warren is Head, Director of Translational Medicine Statistics at Teva Pharmaceuticals. She is responsible for building and applying computational biology, ML/AI, statistical genetics, biomarker analytics to support drug discovery and development projects at Teva. She served as a member of the scientific committee in GRC (Genomic Research Consortium) to evaluate and assess drug discovery and drug repurposing opportunities using Real World Data (RWD)-based GWAS and PheWAS studies. Before joining Teva, she was Director of Statistics at Roivant, Head of US biostatistics at Acclarogen and led several large-scale pharmacogenetic and human genetics projects at GSK. She received her Ph.D. in Bioinformatics and MS in Statistics from North Carolina State University.

    Maria Luisa Pineda

    CEO and Co-founder, Envisagenics
    Maria Luisa Pineda

    Maria Luisa Pineda, PhD, is the CEO and co-founder Envisagenics. Maria Luisa began her career as a life science venture capital investor before starting the company. Under her leadership, Envisagenics has received nearly $3.5M in non-dilutive SBIR grants and revenue, raised $7.5M from investors, and won several prestigious competitions for excellence in artificial intelligence, including the JLABS Artificial Intelligence for Drug Discovery QuickFire Challenge. Maria Luisa secured the company’s first commercial partnership with The Lung Cancer Initiative at Johnson & Johnson that was announced in April, 2020.

    Mark DePristo

    CEO & CoFounder, BigHat BioScinces
    Mark DePristo

    Mark DePristo is Founder and CEO of BigHat Biosciences, an early-stage Bay Area startup reimagining antibody discovery and engineering with synthetic biology and machine learning to create better antibodies faster and undertake novel designs far beyond what's possible today. Mark founded and led the Genomics team in Google Brain, was Vice President of Informatics at SynapDx, and co-director of Medical and Population Genetics at the Broad Institute. He has a BA in Computer Science and Math from Northwestern University, a PhD in Biochemistry from the University of Cambridge as a Marshall Scholar, and was a Damon Runyon Fellow at Harvard University in evolutionary biology. Dr. DePristo's academic articles are widely published and have received more than 73,000 citations.

     

    Naheed Kurji

    CEO, Cyclica
    Naheed Kurji

    Naheed Kurji is a Co-founder, and the President and CEO of Cyclica, a Toronto-based venture-backed biotechnology company that leverages its integrated AI-augmented drug discovery platform to advance the discovery of new and better medicines. He is passionate about building AI-augmented technologies that enable researchers to make more strategic and informed decisions in healthcare and the life sciences. Naheed is also a co-founder of The Alliance for Artificial Intelligence in Healthcare (AAIH), of which he is 1 of 4 appointed Executive Officers. He was named by Technology Innovators as one of the top 50 Healthcare Technology CEOs of 2019.

    Noor Shaker

    Founder & CEO, Glamorous AI
    Noor Shaker

    Noor is a serial biotech entrepreneur with a track record achievements in AI having held an Assistant Professorship from Aalborg University. Noor has published >50 papers cited >2000 times and is an inventor on a handful of patents. She is passionate about science and on a mission to cure cancer with the power of data and AI. Her work at GlamorousAI pushes the boundaries to what is possible with AI to cure challenging diseases. She is MIT innovator under 35 and in BBC 100 women for 2019.

     

    Peter Henstock

    Machine Learning & AI Lead, Pfizer Global Pharmaceuticals
    Peter Henstock

    Peter Henstock is a member of Pfizer’s Quantitative Data Sciences group and serves as the Machine Learning & AI Lead. His work has mostly been at the intersection of statistics, software engineering, visualization and AI to solve problems in drug discovery. Peter holds a PhD in Artificial Intelligence from Purdue University and 6 Master’s degrees. He teaches graduate AI, Software Engineering, and Computer Vision courses at Harvard.

     

    Philipe Moingeon

    Department head, Servier Pharmaceuticals
    Philipe Moingeon

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

    He acquired expertise in the development of biologicals, with several positions held in the vaccine industry, such as Director for 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 S.A (2003-2017).

    He joined Servier Pharmaceuticals in the Paris area in 2017, to head the therapeutic area immuno-inflammation. His current research focuses on the application of artificial intelligence in support of new drug development against auto-immune diseases.
     

    Simone Fulle

    Head of Molecular modelling & Design, Novo Nordisk
    Simone Fulle

    Simone is heading the ’Molecular Modelling & Design’ department at Novo Nordisk, which is focusing on in silico driven design and engineering of biologics by 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 the BioMed X Innovation centre in Heidelberg, where she developed together with her team computational methods that support the rational design of selective kinase inhibitors. Simone obtained her PhD in the field of Computational Chemistry from the Goethe-University, Frankfurt/Main, and did a PostDoc in the course of a Marie Curie program at a spin-out company from the University of Oxford.
     

    Exhibitors

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    Speaker Interview - Mark Depristo

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    AI Drug Discovery 2021 Brochure

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    2021 Conference Programme

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    2020 Past Presentation from Sanofi - speaker 2

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    2020 Past Presentation from Schrödinger

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    2020 Past Presentation from Sanofi - speaker 1

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    2020 Past Presentation from Blue Burgundy

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    2020 Past Presentation from Biogen

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    2020 Past Presentation from Bayer

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    2020 Past Presentation from AstraZeneca

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    Exhibitors


    OpenEye Scientific Software

    Exhibitors
    http://www.eyesopen.com/

    OpenEye has built a solid reputation as a scientific leader in the field of molecular design based on two decades of delivering useful applications and programming toolkits. Our scientific approach has focussed on the power of molecular 3D structure to inform and guide, in particular via the concept of shape similarity. We have changed industry perception of what is possible with the speed, robustness and scalability of our tools and have recently built these into a ground-up, cloud-native platform, Orion. Combining unlimited computation and storage with powerful tools for data sharing, visualization and analysis in an open development platform, Orion offers unprecedented capabilities for drug discovery and optimization.

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    MedChemNet provides a unique and unparalleled platform for the medicinal chemistry community to share insights, discuss the latest research, and help move the field forward. MedChemNet covers all aspects of the drug discovery pipeline, from target identification and validation, through computer aided drug design (CADD), synthesis, screening and other biophysical techniques, to development of novel lead compounds and pre-clinical in vivo proof of concept. We also cover the design of synthetic drug delivery carriers and ADME/toxicology studies, as well as intellectual property and economic related issues. Website: Welcome to Pharma News HQ, the journal dedicated to the pharmaceutical and biopharmaceutical contract services. With regular sections on contract manufacturing, contract research, contract packaging, formulation/development services, contract analytical testing, APIs, stability testing, clinical research and other areas, we bring the most complete coverage of trends and issues in the industry. Pharmanewshq.com is the only website focusing specifically on issues relevant to pharmaceutical professionals working with technology, be it development, engineering, IT or production.


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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, SMI 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@smi-online.co.uk

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