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SMi’s 3rd Annual Conference
AI in Drug Discovery
14-15 March, 2022 | Conference

With the recent pandemic highlighting the need for rapid drug discovery, AI has become an area of increased interest. This is driven by the ability to discover drugs through the use of machine and deep learning. The current challenges within the drug discovery industry include the significant time consumption and expenses involved. This conference will discuss the solutions to these problems with presentations and updates from leading industry experts.

AI in drug discovery is leading the way into a shorter, cheaper and more successful R&D era where compound generation is automated, drug synthesis is predictable and undruggable diseases are finally being targeted.

AI in drug discovery is becoming an integral part of the research and development area of treating diseases with more companies incorporating ‘Big Data’ and data scientists within their R&D teams. Although the AI in drug discovery area has grown rapidly over the past few years, those in the industry acknowledge that there is a long way to go. Collaboration and partnerships are the key to driving this area forward.

Join us at SMi's 3rd annual AI in Drug Discovery conference and explore the latest industry updates in: the selection of targets using AI, decision making within drug discovery and closing the loop on AI in drug discovery. Don’t miss out on presentations from leaders within the field, giving insights into the latest industry advances and answering the big questions within AI in Drug Discovery.





  • Discover the main topics of research within industry, with talks on decision making, target selection and closing the loop
  • Engage with regulators about the guidance within machine learning and AI in Drug Discovery
  • Learn about the new breakthroughs within clinical trials and the treatment of disease
  • Explore the latest technologies in deep learning from leaders within the pharmaceutical industry
  • Discuss the impact of big data and how it applies to AI drug discovery within Pharma


Key job titles include:

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

Previous Attendees Include:


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 and Coffee

9:00 Chair's Opening Remarks

Darren Green

Darren Green, Director of Molecular Design, GSK

9:10 The application and development of AI methodology

Kim Branson

Kim Branson, SVP Global Head of AI and Machine Learning, GSK

  • The intersection of functional genomics and human genetics
  • Target discovery, causal machine learning and clinical applications
  • Translating AI into the real world
  • 9:50 Fragment screening campaigns and machine learning models

    Carl Poelking

    Carl Poelking, Research Associate, Astex Pharmaceuticals

  • Advantages of fragment-based drug discovery and the methodology behind it
  • Machine learning models, identifying the fragment drive binding to the target 
  • A general overview of Astex’s campaigns: screening and rational drug design
  • Precision medicine and the role of fragment targets in treating cancer
  • 10:30 Morning Coffee

    11:00 Prioritization of Disease-Mechanism Relationships for Target Discovery

    Gabi Griffin

    Gabi Griffin, Lead Bioinformatics Scientist, Benevolent AI

  • Mechanistic drug discovery approaches can lead to target hypotheses with clearer rationales that are more likely to succeed in clinical trials
  • Multimodality data, including millions of relationships extracted from the literature by natural language processing, are harmonized in the Benevolent Knowledge Graph to identify and prioritize mechanisms that are relevant and specific to a disease.
  • The Benevolent Knowledge graph and its associated machine learning algorithms and tools can surface this data to drug discovery scientists so they can make informed, data-driven decisions about which mechanisms to pursue.
  • 11:40 TBC: James Arnold

    12:20 Integration of AI in drug design: activity modelling and decision making

    Christian Tyrchan

    Christian Tyrchan, Associate Director Computational Chemistry, AstraZeneca

  • An update of how the research has moved forward during the pandemic
  • Activity modelling and its impact on drug design
  • The challenges of decision making within AI
  • Active learning for FAP (functional ambulation profile as a research case study
  • 13:00 Lunch

    14:00 NovaDesign, a Holistic Structure-based de novo Approach and Its Impact on Drug Discovery Projects

    Alexander Hillisch

    Alexander Hillisch, VP Head of Molecular Computational Design, Bayer AG

  • Components of the holistic workflow
  • Recent application examples of NovaDesign
  • Comparison to more traditional hit finding approaches
  • Future vision of holistic approaches
  • 14:40 Using AI Technology in Plant-Based Drug Discovery Research

    Andrea Small-Howard

    Andrea Small-Howard, Chief Science Officer & Board Member, GB sciences

  • Using AI technology to identify and predict the efficacy of combinations of novel active ingredients
  • An introduction to PhAROS (Phytomedical Analytics for Research Optimization at Scale)
  • The data analytics and machine learning capabilities of PhAROS
  • Specific examples of uses of PhAROS in drug discovery
  • Reducing the side effects commonly associated with traditional, single-molecule prescription medications
  • 15:20 AI in Drug Discovery – From Large Datasets to Biological Insights

    Sandor Szalma

    Sandor Szalma, Global Head, Computational Biology, Takeda

  • Large data sets and target identification
  • The biological aspect of target identification, biomarkers and patient selection
  • Machine learning: genomics and genetic signals
  • Finding insights for rare diseases using machine learning
  • 16:00 Afternoon Tea

    16:30 Applying deep neural networks in drug discovery

    Friedrich Rippmann

    Friedrich Rippmann, Director, Global Computational Chemistry & Biology, Merck

    Understanding how AI and machine learning is being applied across drug discovery 
    Predictive models and how they benefit drug discovery, including rational based drug design

    How AI and machine learning can alleviate some of the bottle necks within discovering medicines 
    The future advances within this field that will allow AI to be a critical part of drug discovery

    17:10 Standardising the oversight of AI- and ML-based medical devices, and the terminology associated

    17:50 The ethical stance behind artificial intelligence for drug delivery

    18:30 Chair's Closing Remarks and Close of Day One

    Darren Green

    Darren Green, Director of Molecular Design, GSK

    8:30 Registration and Coffee

    9:00 Chair's Opening Remarks

    Darren Green

    Darren Green, Director of Molecular Design, GSK

    9:10 Accelerating Drug Discovery using AI-enabled ML across Target Identification to Clinical Candidate Nomination

    Irene Choi

    Irene Choi, Senior Director, Verge Genomics

  • Leverage AI-ML to identify a “disease signature” and novel targets
  • Integrate AI-ML in selection of relevant preclinical/ translational models
  • Focus on AL-ML enabled translational readouts which demonstrate PoC in advancing disease modulating therapies to clinic
  • 9:50 Artificial intelligence in drug discovery: recent advances and future perspectives

    Mathew Divine

    Mathew Divine, Senior Data Scientist, Boehringer Ingelheim GmbH & Co. KG

  • The current status of AI in chemoinformatics
  • Quantitative structure-activity/property relationship and structure-based modelling
  • De novo molecular design, and chemical synthesis prediction.
  • Advantages and limitations of current deep learning applications and next-generation AI for drug discovery.


  • 10:30 Morning Coffee

    11:00 AI-powered, patient inspired treatments for rare diseases

    Neil Thompson

    Neil Thompson, Chief Scientific Officer, Healx Limited

  • Using natural language processing (NLP) to extract disease knowledge from published sources
  • A hypothesis free approach, what is means and how it works
  • The rare disease-focused knowledge graph and what is shows
  • Rare Treatment Accelerator (RTA), what it is.
  • 11:40 Cross species meta-analysis of cancer associated fibroblasts heterogeneity via single cell RNA-seq and spatial transcriptomics data sets

    Varenka Angelica Rodriguez DiBlasi

    Varenka Angelica Rodriguez DiBlasi, Senior Scientist -Lab Head Exploratory Bioinformatics Group, Boehringer Ingelheim Pharmaceuticals, Inc.

  • Meta-analysis of CAF Heterogeneity in human tumours of PDAC and CRC
  • PDAC mouse models that recapitulate human CAF heterogeneity and their implications in immune-oncology drug discovery
  • Maximizing pathology informatics with AI approaches
  • 12:20 Integrating multi-omic data to lead drug discovery

    Ariella Cohain

    Ariella Cohain, Head of Computational Biology, MultiOmic Health

  • The importance of taking a holistic view towards data and drug discovery
  • Approaches on how to integrate complex data for complex diseases
  • Integration of data-driven research with wise experimental target validation approaches
  • 13:00 Lunch

    14:00 Patient-first drug development through AI-supported functional precision medicine platform

    Gregory Vladimer

    Gregory Vladimer, VP Translational Research, Exscientia

  • Functional precision medicine platforms enables patient-first drug discovery and development using primary human tissues.
  • High content imaging and custom AI-derived image analysis drives the scalable quantification of drug effects at the single cell level from primary patient material/
  • Platform technology has proven translational and clinical impact through the first ever prospective interventional trial prioritising therapy for late-stage patients with blood cancer (EXALT-I; NCT03096821)
  • 14:40 Panel Discussion: The use of AI in Drug Discovery for the treatment of rare diseases

  • How rare disease clinical research can benefit from the use of AI technologies
  • The importance of accelerating diagnosis through patient activation
  • AI-assisted profiling and patient activation strategies 
  • A future outlook on moving from computer science to medicine
  • Maria Luisa Pineda

    Maria Luisa Pineda, CEO and Co-Founder, Envisagenics

    Neil Thompson

    Neil Thompson, Chief Scientific Officer, Healx Limited

    15:20 Afternoon Tea

    15:50 Natural language processing from clinical trials to patent mining

    Peter Henstock

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

  • Extended search and export capability for clinicaltrials.gov
  • Combining drug labels and patent claim extraction to interpret legal strategies 
  • Examples of additional use cases within pharma
  • The future and possibilities of Natural Language processing
  • 16:30 How AI enhances every stage of Clinical Research

    Bhupathy Alagiriswamy

    Bhupathy Alagiriswamy, Clinical Trial Manager, Boehringer Ingelheim

  • Challenges in the current and traditional clinical trial process
  • Overview of AI and its applications
  • AI in Clinical trial – pros and cons
  • Case studies
  • Regulatory guidance and ethics within AI
  • 16:40 Towards in silico clinical studies to predict drug efficacy

    Philipe Moingeon

    Philipe Moingeon, Head of therapeutic area immuno-inflammation, Servier Pharmaceuticals

  • Assessing the complexity of chronic diseases by combining comprehensive molecular profiling of patients with AI-modelling
  • Using disease models to support various steps of drug development
  • How far are we from performing virtual clinical studies to evaluate drug efficacy?
  • 17:20 Chair's Closing Remarks and Close of Day Two

    Darren Green

    Darren Green, Director of Molecular Design, GSK



    Venue To Be Confirmed

    London, United Kingdom

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


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