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AI in Drug Discovery USA
21 October - 22 October 2024
AI in Drug Discovery USA

JUST ANNOUNCED: END OF DAY 1 NETWORKING DRINKS RECEPTION

This year, for the first time ever, AI in Drug Discovery USA is in association with RNA Therapeutics USA to create Drug  Discovery Week in Boston, taking place from October 21-24, 2024. This is an exciting opportunity for professionals in the pharmaceutical and biotech industries to unite and advance the future of drug discovery through AI and RNA innovation.

Special Offer: 50% Off Tickets

Purchase tickets for both events for you and a colleague and receive a 50% discount. Please note: This offer is for new bookings only and cannot be used in conjunction with other offers. For full terms and conditions, click here.

Why can’t you afford to miss the AI Drug Discovery USA conference?

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.

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 USA 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

Dr Bino John

Dr Bino John

Director, Computational Biology, Cancer Immunology and Immune Modulation, Boehringer Ingelheim
Dr Dmitriy Podolskiy

Dr Dmitriy Podolskiy

Associate Director and AI Drug Discovery, Astellas Pharma
Dr Petrina Kamya

Dr Petrina Kamya

President, Insilico Medicine Canada Inc and VP, Global Head of Platforms, Insilico Medicine
Dr Stephen MacKinnon

Dr Stephen MacKinnon

VP of Applied Machine Learning, Recursion Pharmaceuticals
Dr Vishnu Sresht

Dr Vishnu Sresht

Director, Computational Chemistry and Machine Learning, Genentech

Alfie Brennan

Chief Scientific Officer, Evariste
Alfie Brennan

Alfie Brennan, PhD, is the Chief Scientific Officer of Evariste, where he oversees the company's scientific strategy, the development of the company's pipeline of drug assets and its AI-enabled drug discovery platform, Frobenius. In his role, Alfie ensures that the insights gained during asset development are integrated back into the platform, enhancing the scientific knowledge base and accelerating innovation.

Alfie has a wealth of medicinal chemistry experience, having completed his MChem at Newcastle University, during which he worked on PI3Kdelta inhibitors in collaboration with GSK. He then pursued a PhD in Medicinal Chemistry under the mentorship of Professor Mike Waring and Dr. Celine Cano, with his research conducted in collaboration with Astex Pharmaceuticals. Following his PhD, Alfie spent two years at the Institute of Cancer Research in London, where he focused on the late-stage lead optimization of PPI inhibitors, now licensed by a US-based biotech.
 

Dante Pertusi

Director, Cheminformatics, GSK
Dante Pertusi

Dr Anshul Kanakia

Director of Machine Learning, AstraZeneca
Dr Anshul Kanakia

Dr Bino John

Director, Computational Biology, Cancer Immunology and Immune Modulation, Boehringer Ingelheim
Dr Bino John

Dr Christos A. Nicolaou

Senior Director, Digital Chemistry, Novo Nordisk
Dr Christos A. Nicolaou

Dr Dmitriy Podolskiy

Associate Director and AI Drug Discovery, Astellas Pharma
Dr Dmitriy Podolskiy

Dr. Podolskiy has previously worked at MIT and Harvard Medical School as Research Scientist on computational systems biology of canrcinogenesis and aging . He currently leads the Bioinformatics team at Astellas Institute for Regenerative Medicine and the cross-divisional AI drug discovery team at Astellas Inc

Dr Joel Karpiak

Head of Protein Design & Informatics, GSK
Dr Joel Karpiak

Dr Kinga Bercsenyi

Senior Vice President of Business Development, Arctoris
Dr Kinga Bercsenyi

Kinga Bercsenyi, PhD is the Senior Vice President of Business Development of Arctoris, a tech-enabled CRO based in Oxford.

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

Dr Martin Akerman

CTO & Co-Founder, Envisagenics
Dr Martin Akerman

Dr. Martin Akerman is the CTO and Co-founder of Envisagenics. He 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 Petrina Kamya

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

Rabia Khan, PhD, MBA is the founder and CEO of Serna Bio, a biotech building the world's first map of the druggable transcriptome to address novel inaccessible biology. The company is using their ML-enabled discovery engine to progress programs in Oncology and Rare disease.
Dr. Khan is a member of the Board of Trustees for the UK Dementia Research Institute, an advisor to No Label Ventures, a fund investing in immigrant founders in the UK and EU and to The First Thirty VC fund. She has also held advisory roles for Creative Destruction Labs.
Dr. Khan has more than 10 years experience building scientific teams and delivering commercial deals at the intersection of machine learning, biology and drug discovery. She was Managing Director, Discovery Sciences at Sensyne Health Plc (now - Arctorus Data) establishing the scientific strategy, building the machine learning and clinical teams by recruiting and leading a team of over 50 machine learning and clinical researchers, and delivering on several significant pharma partnerships including Bayer, BMS, Roche and Alexion.
Dr. Khan has also held senior roles at BenevolentAI and Meta (acquired by Chan Zuckerberg BioHub). At Meta, 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 several programs, led the Age-Related Macular Degeneration and Glioblastoma drug discovery programmes and served as the interface between the technical and biological teams.
Born and raised in Pakistan, Dr. Khan is passionate about supporting diversity in technology and initiatives focused on improving access to care and availability of medicine.

Dr Stephen MacKinnon

VP of Applied Machine Learning, Recursion Pharmaceuticals
Dr Stephen MacKinnon

Stephen MacKinnon completed his PhD in Biochemistry at the University of Toronto, with research emphasis on structural bioinformatics.

Stephen led the research and development of predictive technologies for Cyclica, a Toronto-based startup, until their acquisition by Recursion in May 2023.

Stephen is currently VP of Digital Chemistry at Recursion, leading the continuous development and application of machine learning for drug discovery.

Dr Thierry Dorval

Head of Data Sciences & Data Management, Servier
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 Vishnu Sresht

Director, Computational Chemistry and Machine Learning, Genentech
Dr Vishnu Sresht

After a PhD in computational chemistry at MIT, Vishnu worked on developing computational chemistry and machine learning methods for small molecule discovery and development at Pfizer before joining Genentech. He currently leads a group developing methods to integrate machine learning methods alongside physics-based and cheminformatic approaches into target validation, hit discovery, and the DMTA cycle for small molecule discovery.

Karl Leswing

Executive Director, Machine Learning, Schrodinger
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.
 

Khader Shameer

Executive Director and Global Head of AI-Driven Drug Discovery, Sanofi
Khader Shameer

Le Mu

PM and Tech Lead for Roche pRED-MLOps, Senior Scientist / Solution Architect, Roche Innovation Center
Le Mu

Matthew Welborn

Executive Director, Machine Learning, Iambic Therapeutics
Matthew Welborn

Dr. Matt Welborn leads Machine Learning at Iambic Therapeutics, a position he has held since 2020. He earned his PhD in Physical Chemistry from MIT and subsequently completed a postdoctoral fellowship at Caltech, focusing on the application of deep learning to quantum chemistry. Prior to his current role, Dr. Welborn served as a software scientist at the Molecular Sciences Software Institute.

Raya Stoyanova

Data Scientist, MLOps, Roche
Raya Stoyanova

I have a background in machine learning and data science within the pharmaceutical industry, with extensive experience across various domains, primarily focusing on small molecules. Recently, I transitioned into the MLOps domain at Roche, where I am concentrating on developing a robust platform that enables data scientists to onboard their models as efficiently as possible. My work involves streamlining the deployment process and ensuring scalability. This platform aims to enhance the accessibility and operationalization of machine learning models, ultimately driving innovation and efficiency in pharmaceutical research and development.

Senior Representative

, BenevolentAI
Senior Representative

Why should you attend?

Get ready for the highly requested AI in Drug Discovery Conference this October 2024 in the United States. In the interim since our last gathering in March 2024 in the UK, the industry has achieved a groundbreaking 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

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 applications 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 October 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.

sponsors

Conference agenda

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8:00

Registration & Coffee

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9:00

Chairs' Opening Remarks

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

Dr Christos A. Nicolaou

Dr Christos A. Nicolaou, Senior Director, Digital Chemistry, Novo Nordisk

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9:10

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

Dr Anshul Kanakia

Dr Anshul Kanakia, Director of Machine Learning, AstraZeneca

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9:40

Exploiting Emerging AI Techniques in Biologics to Support Hit Identification and Lead Optimisation

Dr Dmitriy Podolskiy, Associate Director and AI Drug Discovery, Astellas Pharma

  • Structure-based generative AI for biologics
  • Sequence-based generative AI for biologics
  • ML models for binding properties of peptides
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    10:10

    Integrating Multi-Modal AI/ML Models into the Drug Discovery Pipeline

    Dante Pertusi

    Dante Pertusi, Director, Cheminformatics, GSK

  • Improving the Robustness of AI/ML Predictions & Results 
  • Incorporating Multi-Modal Models into Existing Frameworks
  • Reflections on the GSK Experience of Augmenting Cheminformatics with AI/ML tools

     

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    10:40

    Morning Break

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    11:10

    Latest advancements in machine learning-enhanced in silico design: Impact on a pipeline of drug discovery programs

    Karl Leswing, Executive Director, Machine Learning, Schrodinger

  • Using active learning with FEP+ for large-scale in silico fragment screens in hit discovery
  • Applying 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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    11:40

    Knowledge Graphs to Maximise Success Rate of Early Stage Drug Discovery

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

  • Current weaknesses in approaches to small molecule screening
  • Using knowledge graphs to address those weaknesses 
  • Anticipating future progress
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    12:10

    Networking Lunch

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    13:10

    Implementing Production AI: Redrawing the DMTA Drug Discovery Cycle

    Dr Christos A. Nicolaou

    Dr Christos A. Nicolaou, Senior Director, Digital Chemistry, Novo Nordisk

  • Why does advances in AI methodologies means we should reimagine the DMTA cycle? 
  • What should an optimised, comprehensive, closed-loop DMTA cycle look like? 
  • What are the limits of AI utility to this transformation? What are the current technological gaps? How do we accommodate gaps and limits to augment drug discovery processes?
  • Unpacking recent successes in putting the theory into practice at Novo Nordisk
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    13:40

    Merging Minds and Machines in the DMTA Cycle

    Dr Vishnu Sresht, Director, Computational Chemistry and Machine Learning, Genentech

  • Leveraging the minds: approaches for drawing on internal medchem expertise
  • Leveraging the machines: automated approaches for drawing insights from internal data
  • Our approaches for seamlessly merging computation into a lab-in-the-loop paradigm for small molecule optimizatio
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    14:10

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

    Dr Martin Akerman, CTO & Co-Founder, Envisagenics

  • Envisagenics presents SpliceCore, an AI-powered target discovery platform with a focus on the identification of novel, highly tumor-specific, and safe epitopes for immunotherapeutic development.
  • Explore the cutting-edge technology behind SpliceCore and delve into its scientific 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 showcase the predictive capabilities of the SpliceCore platform and its ability to find novel tumor-specific epitopes.
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    14:40

    Afternoon Break

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    15:10

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

    Dr Rabia Khan, Founder/CEO, Serna.bio

  • Why RNA? Limits of protein-focused drug discovery
  • Building an appropriate & accurate dataset to inform valid AI mapping
  • ML methodologies applied to build the platform
  • Lessons drawn from ML: the StaR rules
  • Leveraging the new RNA world for drug discovery – unlocking classically undruggable proteins and unexplored potential therapeutic targets
     
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    15:40

    Panel Discussion: Strategising the Next Stage of AI Integration into Drug Discovery and the Pharmaceutical Industry

  • 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 Christos A. Nicolaou

    Dr Christos A. Nicolaou, Senior Director, Digital Chemistry, Novo Nordisk

    Dr Stephen MacKinnon, VP of Applied Machine Learning, Recursion Pharmaceuticals

    Dr Martin Akerman, CTO & Co-Founder, Envisagenics

    Alfie Brennan, Chief Scientific Officer, Evariste

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    16:20

    Chairs' Closing Remarks

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    8:30

    Registration & Coffee

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    9:00

    Chairs' Opening Remarks

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

    Dr Christos A. Nicolaou

    Dr Christos A. Nicolaou, Senior Director, Digital Chemistry, Novo Nordisk

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    9:10

    Leveraging the Era of Artificial Intelligence to Remould Drug Discovery into a Disease-Centric Ecosystem

    Khader Shameer

    Khader Shameer, Executive Director and Global Head of AI-Driven Drug Discovery, Sanofi

  • Discussing recent changes in Drug Discovery - AI-driven evolution or revolution?
  • Acknowledging the challenges and considerations posed by recent developments & future potential progress
  • The opportunity of sophisticated data types and advanced analytical approaches to improve precision medicine with translational bioinformatics
  • Leading technological and conceptual change at Sanofi to grasp the opportunity and create a comprehensive disease research data eco-system in practice
     
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    10:30

    Morning Break

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    11:00

    Re-Engineering the Drug Discovery Pipeline to Put AI Integration at the Heart and Maximise Gains in Efficacy, Safety, and Cost

    Dr Petrina Kamya, President, Insilico Medicine Canada Inc and VP, Global Head of Platforms, 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
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    11:40

    Industrialising Drug Design through an ML-First Mindset

    Dr Stephen MacKinnon, VP of Applied Machine Learning, Recursion Pharmaceuticals

  • What does it mean to have an ‘ML-First Mindset’?
  • Moving ML beyond small silos and virtual bubbles to scaled, generalised company-wide processes – reflections from experience in application
  • The Future Ideal: Combining AI with other advanced tools to minimise manual action until clinical dosing
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    12:20

    How Iambic used AI and laboratory automation to move from program launch to IND in two years, discovering IAM1363, a pan-mutant, brain-penetrant HER2

    Matthew Welborn, Executive Director, Machine Learning, Iambic Therapeutics

  • I'll describe the main elements of our AI-driven experimental platform (including models like NeuralPLexer for structure prediction, and plate-based experimentation for closed-loop exploration and data generation)
  • As an example of how these technologies have been effectively brought to bear in discovery projects at Iambic, I'll talk through our lead program, which went from launch to IND in 24 months, and is now being studied in a Phase 1 clinical trial
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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

  • 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
  • Raya Stoyanova, Data Scientist, MLOps, Roche

    Le Mu

    Le Mu, PM and Tech Lead for Roche pRED-MLOps, Senior Scientist / Solution Architect, Roche Innovation Center

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    14:40

    Realising the Full Potential of Lab Automation as an Enabling Technology for AI-driven Drug Discovery beyond High-Throughput Screening

    Dr Kinga Bercsenyi , Senior Vice President of Business Development, Arctoris

  • Underlining the foundational importance deep, quality data for successful AI/ML applications
  • Challenging the classic narrow association of laboratory automatic with high-thoughput screening applications for hit ID
  • Demonstrating the unique synergy of lab automation and AI/ML methods through case studies and latest trends across the Drug Discovery pipeline
     
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    15:20

    Building Language Models for Antibody Engineering

    Dr Joel Karpiak

    Dr Joel Karpiak, Head of Protein Design & Informatics, GSK

  • Advantages of specific models over generic LLMs
  • Specific utilities for antibody engineering
  • Overview of the next-generation models
  • Ensuring accurate output
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    16:00

    Afternoon Break

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    16:30

    Knowing Why: Target Prediction with Explainable Large Language Models

    Senior Representative

    Senior Representative, , BenevolentAI

  • Understanding the ‘why’ in science often holds greater significance than comprehending the ‘what’ – especially for bench scientists
  • Because the Large Language Model is a complex and notoriously black box, it poses a particular challenge when utilised by bench scientists in discovery
  • BenevolentAI’s ethos is centred around ensuring the scientist is always equipped to make full use of those targets facilitated by LLMs by making the why accessible
  • Our journey demonstrates how keeping the ‘why’ central enables scientists to harness the full power of LLMs for therapeutic target identification
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    17:10

    Closing Fireside Chat: Reflecting on Two Years of ChatGPT-Driven LLM Hype

  • A new era for drug discovery technologies?
  • Assessing the potential: A limited but useful tool?
  • A pivotal development in the space or a distraction from beneficial forms of generative AI?
  • Dr Petrina Kamya, President, Insilico Medicine Canada Inc and VP, Global Head of Platforms, Insilico Medicine

    Dr Joel Karpiak

    Dr Joel Karpiak, Head of Protein Design & Informatics, GSK

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

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    17:50

    Chairs' Closing Remarks and End of Conference


    Chief Scientific Officer
    Evariste
    Director, Cheminformatics
    GSK
    Director of Machine Learning
    AstraZeneca
    Director, Computational Biology, Cancer Immunology and Immune Modulation
    Boehringer Ingelheim
    Senior Director, Digital Chemistry
    Novo Nordisk
    Associate Director and AI Drug Discovery
    Astellas Pharma
    Head of Protein Design & Informatics
    GSK
    Senior Vice President of Business Development
    Arctoris
    CTO & Co-Founder
    Envisagenics
    President, Insilico Medicine Canada Inc and VP, Global Head of Platforms
    Insilico Medicine
    Founder/CEO
    Serna.bio
    VP of Applied Machine Learning
    Recursion Pharmaceuticals
    Head of Data Sciences & Data Management
    Servier
    Director, Computational Chemistry and Machine Learning
    Genentech
    Executive Director, Machine Learning
    Schrodinger
    Executive Director and Global Head of AI-Driven Drug Discovery
    Sanofi
    PM and Tech Lead for Roche pRED-MLOps, Senior Scientist / Solution Architect
    Roche Innovation Center
    Executive Director, Machine Learning
    Iambic Therapeutics
    Data Scientist, MLOps
    Roche
    BenevolentAI

    Sponsors

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    As part of Drug Discovery Week, SAE Group Media is delighted to confirm that AI USA is now in association with RNA Therapeutics USA. This means that when you secure a ticket to both events, you will be given a 50%* discount across both tickets. Bring a colleague who works within RNA Therapeutics advancements, and you will both benefit from this exclusive offer – confirm your attendance to both events below!

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    RNA Therapeutics USA

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    AI in Drug Discovery UK Audience Breakdown

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    WHAT IS CPD?

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