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AI in Drug Discovery

Conference Overview

The biggest AI in Drug Discovery event will be coming back to London on the 8th – 10th March 2027, bringing together the highest number of big pharma speakers for focused discussions and networking. 

For the first time, the conference in 2027 has expanded to a three-day agenda reflecting the continued growth, maturity and impact of AI across the drug discovery landscape.

As the role of AI in drug discovery continues to evolve, this three-day conference provides a dedicated forum to explore how data-driven and computational approaches are shaping the future of R&D. The event will bring together senior leaders to share strategic insights, practical experiences and lessons learned as organisations look to embed AI more effectively across discovery programmes.

 

FEATURED SPEAKERS

Dr Martin Redhead

Dr Martin Redhead

Vice President Primary Pharmacology, Recursion
Dr Simone Fulle

Dr Simone Fulle

Head of Computer-Aided Drug Discovery, Basel, Novartis
Dr Tom Diethe

Dr Tom Diethe

Executive Director, Enterprise AI, AstraZeneca
Mr Laurent Gomez

Mr Laurent Gomez

Senior Vice President and Head of Discovery, Iambic Therapeutics
Mr Peter Clark

Mr Peter Clark

VP, Computational Drug Design, Novo Nordisk

Dr Aleksandra Karolak

Assistant Professor, Moffitt Cancer Center and Research Institute
Dr Aleksandra Karolak

Aleksandra Karolak is an Assistant Professor in the Machine Learning Department at Moffitt Cancer Center, where she leads the Molecular AI Lab. She holds secondary appointments in Drug Discovery and Gastrointestinal Oncology at Moffitt, in Chemical, Biological, and Materials Engineering at the University of South Florida. She is a computational scientist with expertise in computational chemistry, molecular modeling, and machine learning. Her research integrates AI, physics-based molecular simulations, and quantum optimization to accelerate drug discovery, from virtual screening and molecular dynamics to generative therapeutic design, while navigating the unique translational and operational complexities of drug discovery within an oncology center.

Dr Amendra Fernando

Principal Scientist, Pfizer Inc.
Dr Amendra Fernando

Dr. Amendra Fernando is a Principal Scientist in Biomedicine Design at Pfizer, specializing in structure-based protein design and predictive modeling for biologics. With a decade of experience across industry and academia, he combines AI/ML, physics-based simulations, and molecular dynamics to engineer next-generation therapeutics. Dr. Fernando is an inventor on three patents and has authored 14 peer-reviewed papers. Notably, his computational designs have successfully transitioned from the bench to clinical trials.

 

Dr Andrei Kamenski

Senior Data Scientist, Novo Nordisk R&D UK
Dr Andrei Kamenski

Dr Anthony Bradley

Assistant Professor, University of Liverpool, Co-Founder & Chief Scientific Officer, DaltonTx, University of Liverpool
Dr Anthony Bradley

Anthony Bradley is an Assistant Professor at the University of Liverpool, where he runs a research group developing machine learning methods for chemical synthesis and molecular design. He is also Co-Founder and Chief Scientific Officer of DaltonTx, building AI systems that plan, interpret and learn from experiments across the design–make–test–analyse cycle. He previously held computational chemistry and machine learning roles at Exscientia. His work focuses on small molecule and antibody discovery, and on turning the predictions available to a team into impactful decisions made inside discovery programme.

Dr Bjarki Johannesson

Director, Cell Painting & Predictive Safety, AstraZeneca
Dr Bjarki Johannesson

Dr Christoph Grebner

Senior Principal Scientist, Synthetic Molecular Design / Computational and AI Strategy – R&D, Sanofi
Dr Christoph Grebner

Dr Gerhard Hessler

Head of Synthetic Molecular Design, Sanofi
Dr Gerhard Hessler

Dr Graeme Robb

Director, Computational Chemistry, AstraZeneca
Dr Graeme Robb

Graeme Robb is Director of Computational Chemistry at AstraZeneca in Cambridge and has been at the leading edge of computer-aided drug design for over 20 years. Graeme has contributed to the invention of several clinical candidates and led multidisciplinary teams across oncology research and globally. Through his focus on enabling capabilities, he has delivered transformative and widely adopted design tools to the organisation and is now at the forefront of deploying AI tools to all AstraZeneca drug designers.

 

Dr Jack Glancy

Principal Computational Chemist, GSK
Dr Jack Glancy

Dr James Lumley

Senior Director & Head of Cheminformatics, GSK
Dr James Lumley

Dr James Lumley is Senior Director and head of Cheminformatics at GSK leading a team that develop and embed methods to accelerate small molecule candidate identification including generative design, ML and active learning, and agentic AI. He works alongside small molecule research, DMPK, toxicology, high-throughput experimentation and automation teams building an AI driven platform for small molecule design. James has over 25 years’ experience applying cheminformatics aid small-molecule drug discovery with a PhD in computational chemistry. His early career spanned 10 years across several biotechs, including as Head of Computational Chemistry at Arrow Therapeutics (a subsidiary of AstraZeneca) working in anti-infectives, before co-founding ReViral. He holds inventorship on multiple antiviral drug candidates, including AZD-7295 and Sisunatovir (RV521) later acquired by Pfizer as part of its $525M purchase of ReViral. James spent 2011-2020 at Eli Lilly leading global Cheminformatics support, delivering award winning platforms, methods and models recognised with multiple CIO awards and a BioIT World Innovative Practice Award. He joined GSK in 2020.

Dr Josephine Alba

Senior Expert, Data Science, Novartis
Dr Josephine Alba

Josephine Alba holds a degree in Pharmacy from the University of Rome La Sapienza and a PhD in Chemical Sciences from the same institution. During her doctoral research, she gained extensive experience in modeling TCR–pMHC complexes in membrane environments, performing all-atom molecular dynamics simulations to capture T-cell receptor conformational changes.

She subsequently completed a three-year postdoctoral fellowship at the University of Fribourg (Switzerland), where she expanded her expertise to coarse-grained simulations, studying the biophysical properties of membrane tubules and vesicles.

She currently works at Novartis, applying advanced computational techniques to investigate protein, siRNA and membrane behavior in drug discovery.

Dr Lewis Vidler

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

Dr Lewis Vidler is a Senior Director of Structure Based Drug Design at Eli Lilly. He achieved a first-class chemistry degree at the University of Oxford in 2009 and subsequently completed his PhD in Computational Medicinal Chemistry at the Institute of Cancer research in London in 2013. He joined Eli Lilly later that year and spent an initial duration of 7 years there, during which he progressed to Senior Research Scientist. Following shorter stints as a computational chemist at UCB and Amphista Therapeutics, he returned to Lilly in 2023, to further progress his career.

Dr Martin Redhead

Vice President Primary Pharmacology, Recursion
Dr Martin Redhead

Martin Redheadleads the Primary Pharmacology team at Recursion, where he leads efforts to integrate AI and computational approaches into drug discovery biology. With a career spanning academia and industry, Martin earned his PhD from the University of Nottingham before holding positions at Sygnature Discovery, UCB Pharma, and Exscientia. His work focuses on applying quantitative methods and artificial intelligence to accelerate the translation of biological insights into therapeutic candidates. At Recursion, Martin drives the development of innovative approaches that bridge computational biology, pharmacology, and AI-driven drug discovery platforms.

Dr Mona Kab Omir

Managing Director and Co-Founder, Xenos Bio
Dr Mona Kab Omir

Dr Mona Kab Omir is a serial entrepreneur with a PhD in Chemistry. As CTO and co-founder of Vatic Health, she delivered a patented, CE-marked COVID-19 diagnostic with a new assay format in nine months, and is an inventor of several technologies and trade secrets. She is Co-founder and Managing Director of Xenos Bio, where she developed a computationally efficient ab initio machine-learning platform that deterministically predicts peptide binders of any length from a protein sequence prompt alone. Validated in-house and with a customer, it targets a regime where annotated binder data is scarce, including intrinsically disordered proteins. She collaborates with Professor Giles Yeo on deorphanising a receptor for cardiometabolic disease.

 

Dr Noel O'Boyle

Chemical Biology Resources Team Leader, EMBL-EBI
Dr Noel O'Boyle

Dr Noel O’Boyle is the Chemical Biology Resources Team Leader at EMBL-EBI, where he leads the development of major open chemical biology resources including ChEMBL, ChEBI, SureChEMBL and UniChem. A computational chemist and cheminformatician by training, he has worked across academia, scientific software and the pharmaceutical industry, including roles at the Cambridge Crystallographic Data Centre, NextMove Software and Nxera Pharma. His interests span cheminformatics, molecular representation, open scientific software and computational drug discovery. His current work explores how AI can build upon high-quality curated chemical data, and help transform the way such data are curated, searched and used.

Dr Petrina Kamya

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

Petrina Kamya, PhD, serves as Global Head of AI Platforms (VP) and President of Insilico Medicine Canada. She is responsible for the strategy, development, and application of Pharma.AI, Insilico’s integrated generative AI platform that connects multi-omics target discovery (PandaOmics), generative chemistry for novel small molecules (Chemistry42), and multimodal clinical trial outcome prediction (inClinico).

Through Pharma.AI, Insilico has nominated more than 30 preclinical candidates since 2021 and advanced multiple programs into the clinic. Notable achievements include rentosertib (ISM001-055), the first AI-designed molecule against an AI-discovered target to demonstrate positive Phase 2 results in idiopathic pulmonary fibrosis and enter Phase 3 clinical trials, as well as a growing pipeline spanning oncology, fibrosis, immunology, and other areas of high unmet need. The platform is licensed by many of the top 20 global pharmaceutical companies and supports 10+ large-scale R&D collaborations.

Based in Montréal, Dr. Kamya leads Insilico’s Canadian AI R&D center and focuses on turning advanced machine learning into validated, clinically relevant drug candidates. She holds a PhD in theoretical chemistry and a BSc in Biochemistry from Concordia University.

Dr Prakash Rathi

Senior Director, Solutions Engineering, Roche Pharmaceuticals
Dr Prakash Rathi

Dr Rich Taylor

Director, Director of Computer-Aided Drug Design (CADD), UCB
Dr Rich Taylor

Richard D. Taylor is Director of Computer-Aided Drug Design (CADD) at UCB, based in the UK, leading teams in computational drug discovery for small molecules, macrocycles and antibodies. He specializes in applying AI, data-driven approaches and physics-based methods to accelerate molecular design and clinical candidate delivery. Richard began his industrial career at Astex Therapeutics after completing a PhD in Computational Chemistry at the University of Southampton. He is a Fellow of the Royal Society of Chemistry and is passionate about advancing drug discovery using digital approaches and fostering innovation through AI-enabled design and collaborative scientific initiatives.

Dr Simone Fulle

Head of Computer-Aided Drug Discovery, Basel, Novartis
Dr Simone Fulle

Dr Stefan Schiesser

Director of Medicinal Chemistry, AstraZeneca
Dr Stefan Schiesser

Dr Thierry Dorval

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

Thierry Dorval received a Ph.D. in Machine Learning from University Pierre & Marie Curie and then joined the institut Pasteur Korea in 2005 as a group leader specialized in High Content Screening applied to cellular differentiation and toxicity prediction.

In 2012 he joined AstraZeneca, where his activities were about developing and advising on quantitative data analysis solutions in support of high content phenotypic screening.

In 2015 he joined Servier, France, where he is currently leading the Data Sciences & Data Management research unit. He is in charge of optimizing early stages of drug discovery by taking advantage of cutting-edge computational approaches. This includes usage of Artificial Intelligence, knowledge graph for molecular entities selection, design & optimization.

Dr Thomas Licher

Global Head, Integrated Drug Discovery, Sanofi
Dr Thomas Licher

Dr Tom Diethe

Executive Director, Enterprise AI, AstraZeneca
Dr Tom Diethe

Dr Tom Diethe is an Executive Director in the Enterprise AI Unit at AstraZeneca, Cambridge UK. The mission of the unit is to devise innovative AI products and solutions at using cutting edge AI and Machine Learning technology that will make the drug discovery pipeline more efficient and aid in a better understanding of human biology and medicinal chemistry. Tom is a AI/ML leader with 20+ years of experience in a mix of industry in academic roles, including Amazon, Microsoft Research, the British Medical Journal Group, QinetiQ, UCL and the University of Bristol. As a researcher, as well as developing fundamental methods in ML, he specialised in healthcare applications of AI, including genomics, proteomics, digital health, and physiological measurement. He is co-author of the online first book “Model-Based Machine Learning.” Tom is also an Honorary Research Fellow at the University of Bristol, a Fellow of the Royal Statistical Society, and a member of ELLIS - the European Laboratory for Learning and Intelligent Systems.

 

Dr Yogesh Sabnis

Global CADD, UCB
Dr Yogesh Sabnis

Mr Ilya Beketov

Lead AI and GenAI Architect, Bayer
Mr Ilya Beketov

Ilya Beketov is a Lead Architect AI specializing in Agentic AI, Enterprise AI Architecture, and AI Engineering. He helps organizations move from AI experimentation to production-scale intelligent systems by designing architectures that combine AI agents, business capabilities, data products, and digital platforms. His current focus includes Agentic AI frameworks, AI observability, responsible AI, architecture decision intelligence, AI-native engineering, and composable enterprise architectures. Ilya is passionate about enabling organizations to build trusted, scalable, and business-driven AI ecosystems that augment human decision-making and accelerate digital transformation

Mr Jonathan Mason

Senior Research Advisor, Global Biotech
Mr Jonathan Mason

Dr. Jonathan S Mason is a Senior Scientific Advisor (Design for Drug Discovery) to global biotech companies (UK/US/China/Japan), including Structure Tx & Centessa. He was previously a Senior Research Fellow at Sosei Heptares (Nxera) & also leading computational chemistry at Orexia Therapeutics (Centessa) for GPCR structure-based drug design. He has 4 decades of global pharmaceutical drug discovery experience and is an experienced leader and scientific expert for drug design technologies. He previously led teams involving CADD & structural biology/chemistry at Lundbeck (DK), Pfizer (UK, Executive Director), Bristol-Myers Squibb (US) & Rhône-Poulenc Rorer (now Sanofi, UK, France & USA). He completed his PhD at Queen Mary, U. London and become an early pioneer of the use of CADD approaches in drug discovery research.
He has always emphasised the key role of lipophilic hotspots as well as polar interactions, clearly now seen in many GPCR structures, and more recently enhanced CADD impact by using full binding site water networks and their energetics for potency, selectivity and kinetics, and by using FEP (Free Energy Perturbation) on GPCRs for the prediction of binding affinity and potency. He is a strong believer in the power of interactive 3D stereo visualisation for group as well as individual design sessions.
 

Mr Laurent Gomez

Senior Vice President and Head of Discovery, Iambic Therapeutics
Mr Laurent Gomez

Laurent Gomez is Senior Vice President and Head of Discovery at Iambic Therapeutics, where he leads multidisciplinary teams advancing novel therapeutics from early design through candidate nomination. He oversees discovery efforts that integrate physics-informed AI, high-throughput experimentation, and computational chemistry to accelerate small molecule drug discovery.
Dr. Gomez has more than 20 years of experience in the pharmaceutical and biotech industries, with a strong track record of advancing new chemical entities into clinical development across immunology, CNS, and oncology. He is known for building high-performing discovery organizations and implementing innovative approaches to translate early-stage research into promising therapeutic candidates.
 

Mr Le (Muller) Mu

Principal Machine Learning Engineer, MLOps lead, Computational Sciences Center of Excellence, gRED, Roche
Mr Le (Muller) Mu

Le (Muller) Mu brings over 15 years of cross-industry digitalization experience, blending his IT background with expertise in molecular biology to accelerate drug discovery within Roche Pharma Research (pRED & gRED). As a Principal Machine Learning Engineer and Lead of the cross-REDs MLOps Service team, he leads the operationalization of diverse machine learning and AI models to help deliver advanced treatments to patients faster, cheaper and better.

Mr Nicholas Runcie

DPhil student, University of Oxford – Oxford Protein Informatics Group
Mr Nicholas Runcie

Nicholas completed an integrated Master’s degree in Medicinal and Biological Chemistry at the University of Edinburgh. In his final year, he did an industrial placement at AstraZeneca, working on generative models for small-molecule drug discovery. He is now a DPhil student in the Oxford Protein Informatics Group, where he researches chemical reasoning with large language models and is building agentic systems for chemistry research.

Mr Paolo Marcatili

Associate Director, In Silico Biologics Discovery, Novo Nordisk
Mr Paolo Marcatili

Paolo Marcatili is Associate Director of In Silico Biologics Discovery at Novo Nordisk in Copenhagen, where he leads a team applying computational tools, structural bioinformatics, machine learning and generative AI to biologics discovery and the design–make–test–analyze cycle. He previously held academic positions at the Technical University of Denmark, including Associate Professor and Head of Studies. His work combines computational biology, protein engineering, developability, and immunogenicity assessment. Paolo has contributed to more than 20 highly cited bioinformatics tools and over 80 publications.

Mr Peter Clark

VP, Computational Drug Design, Novo Nordisk
Mr Peter Clark

Peter Clark, PhD, leads the Computational Drug Design team at Novo Nordisk. He and his team use advanced computational models and tools to accelerate the delivery of differentiated therapeutics, working from the earliest stages of research through clinical development and first in human trials for all therapeutic modalities and molecular formats. Before joining Novo Nordisk, Peter led the Computational Science & Engineering team at Johnson & Johnson, leading wet and dry lab scientists in creating new ways to discover and develop drugs, including antibodies, peptides, RNA, gene and cell therapies. His work has helped bring several new treatments to patients. Earlier in his career, Peter worked in academia as Director of Bioinformatics at the University of Pennsylvania and as a clinical fellow in molecular genetics at The Children’s Hospital of Philadelphia, where he helped develop diagnostic tests and gene therapies. He holds a PhD in Biomedical Engineering from Drexel University. Peter’s diverse expertise and leadership has led to over 60 scientific publications, several patents, and three biotech start-ups.
 

Mr Renan Andrade Pereira

Head of Data Science, Servier
Mr Renan Andrade Pereira

Renan Andrade Pereira is the Head of Data Science at Servier, a global pharmaceutical company, where he leads the implementation of AI strategies across research, development, marketing, industry and support functions. With over a decade of experience in data science and machine learning, Renan has driven innovative projects, including AI-powered drug discovery platforms and precision medicine solutions. A former entrepreneur and CTO in the biotech space, he is passionate about bridging cutting-edge technology with real-world therapeutic advancements.

Mr Tejus Venkatesh Reddy

Global PK/PD and Pharmacometrics Associate, Leeds University and Industry Placement Student at Eli Lilly
Mr Tejus Venkatesh Reddy

Tejus Venkatesh Reddy is a Biomedical Sciences undergraduate (BSc) at the University of Leeds with a growing focus on drug discovery and development. He has gained hands-on experience across the pharmaceutical industry through placements at Eli Lilly UK, Alchemab Therapeutics, and Manta Pharma, spanning patient safety, bioinformatics, regulatory affairs, and pharmacometrics. His AI-based PKPD modelling and simulation has produced a first-author manuscript and a supporting patent application, both now awaiting approval. An effective communicator and collaborator, Tejus enjoys tackling complex scientific problems and is committed to applying his skills to address unmet needs in healthcare and improve patient outcomes.

Mr Tim Hohm

Associate Director, Novo Nordisk
Mr Tim Hohm

Tim is a trained computational biologist with a decade of experience at the intersection of AI and digital in the life sciences combining experience from large pharma, biotech and soft-ware/tech companies with a BD and strategy background.

Ph.D Miriam Lopez-Ramos

Head of Data and AI Products for Research & CMC, Servier
Ph.D Miriam Lopez-Ramos

Professor Adam Brown

Professor of Biopharmaceutical Engineering, University of Sheffield
Professor Adam Brown

Adam Brown is a Professor of Biopharmaceutical Engineering at the University of Sheffield. His lab develops biological components to improve the manufacture and performance of mRNA, recombinant proteins, viral vectors and cell therapies. He recently spun out two companies from his lab, SynGenSys and Silvia Bio, focused on expression vector and cell engineering for biopharmaceutical production.

 

Professor David Brockwell

Professor of Biochemistry and Molecular Biology, University of Leeds
Professor David Brockwell

Professor Nicola Burgess-Brown

Professorial Research Fellow, Protein Sciences & COO Protein Sciences, SGC, University College London, School of Pharmacy, Pharma & Biological Chemistry
Professor Nicola Burgess-Brown

Professor Pranam Chatterjee

Assistant Professor, University of Pennsylvania
Professor Pranam Chatterjee

Professor Victor Guallar

ICREA Professor, Barcelona Supercomputing Center
Professor Victor Guallar

An ICREA Professor at BSC, Dr. Guallar completed his PhD in theoretical chemistry between UAB and UC Berkeley. After a postdoct at Columbia University, he was appointed assistant professor at Washington University School of Medicine, before moving to BSC in 2006. His laboratory has completed 26 PhDs, developing important contributions in computational biophysics, such as the intermolecular modelling software PELE, and biochemistry, including the introduction of the first PluriZyme (enzyme with multiple actives sites).

Prof. Guallar has been awarded several important research projects, including a prestigious advanced ERC grant. He also cofounded BSC’s first spin off, Nostrum Biodiscovery.

Why attend the AI in Drug Discovery Conference?

  • Strong small molecule focus: Dedicated to small molecule drug discovery, with a broad mix of strategic, scientific and technical perspectives.
  • Balanced programme: Combines high-level industry outlooks with real-world case studies and technical presentations.
  • Value for everyone: Relevant to both decision-makers and hands-on practitioners working at the forefront of AI-enabled discovery.
  • Expanded three-day format: More scope for in-depth discussion, knowledge sharing and cross-functional exchange across the AI in drug discovery ecosystem.
  • Curated content: A carefully chosen blend of thought leadership, practical insight and forward-looking discussion as the field evolves at pace.
  • Extensive networking: Three days of opportunities to connect with peers from leading pharmaceutical companies and innovative biotechs.
  • Senior-level representation: Meet senior figures across informatics, data and AI, molecular design and computational sciences.
  • Connect with industry shapers: An unrivalled opportunity to meet those shaping the future of small molecule drug discovery.

Secure Your Place

 

 Why AI is Transforming the Future of Drug Discovery:

  • Accelerate Discovery - Identify promising targets and candidates faster.
  • Design Better Molecules – Optimise potency, safety and developability earlier.
  • Unlock Complex Data – Turn vast, multimodal datasets into actionable insight.
  • Automate Experimentation – Connect AI with lab-in-the-loop discovery.

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Our approach sets us apart from other events. We're committed to providing genuine insight and meaningful learning experiences. Our discussions are led by expertly selected participants, ensuring comprehensive coverage of the latest developments from all sectors of the industry.

This event is your gateway to interacting with key stakeholders spanning big pharma, informatics, data and AI, molecular design and computational sciences, offering a comprehensive exploration of the sector's future. Immerse yourself in unparalleled networking opportunities at this conference.

By sponsoring, you’ll position your brand for success. Don't miss out on the opportunity to elevate your visibility, credibility, and business prospects - consider sponsorship today!

To discuss your involvement as a sponsor contact, Emma Foundation, Sponsorship Sales Manager at Emma.Fountain@saemediagroup.com or call +44 (0) 20 7827 6132.

 

sponsors

Conference agenda

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7:30

Registration & Coffee

Focus Day: AI for Biologics, Peptides & Protein Design
Building the Next Generation of AI-Powered Therapeutics


As AI moves beyond small molecule discovery, biologics, peptides and protein therapeutics are emerging as one of its most exciting frontiers. This dedicated Focus Day brings together pharmaceutical, biotech and academic leaders to explore how AI is transforming the design, optimisation and development of next-generation therapeutics.

Discover how generative AI, protein and peptide language models, predictive modelling, reinforcement learning and physics-based approaches are being used to navigate vast sequence spaces and design candidates with improved potency, selectivity, stability and developability.

The programme follows the journey from AI-driven peptide and antibody design through developability, manufacturing and translation, examining how computational innovation can be converted into experimentally viable and manufacturable medicines.

Looking ahead, executive discussions will tackle scaling AI across biologics R&D, preparing scientists for the AI-native laboratory, and the convergence of foundation models, robotics and autonomous experimentation that could define the future of therapeutic discovery.

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8:20

Chair's Opening Remarks

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8:30

Enhancing VHH Engineering and Discovery through Multi-Property

Dr Andrei Kamenski

Dr Andrei Kamenski, Senior Data Scientist, Novo Nordisk R&D UK

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8:55

Multi-Objective Generation of Therapeutically-Ready Peptide Modulators

Professor Pranam Chatterjee

Professor Pranam Chatterjee, Assistant Professor, University of Pennsylvania

  • An overview of novel sequence-based algorithms for peptide
  • binder design
  • Introducing multi-objective guidance of peptide design with
  • therapeutic property predictors Demonstrating experimental use-cases and deployment of
  • modulators in disease models
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    9:20

    Session Reserved for Focus Day Strategic Partner

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    9:50

    Morning Coffee & Networking

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    10:20

    Disruptive AI and Physics for Antibody Design ; Past, Present and Future

    Dr Rich Taylor, Director, CADD, UCB

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    10:45

    Finding Order in Disorder: Physics-Informed Peptide Discovery from Squence Alone

    Dr Mona Kab Omir, Managing Director and Co-Founder, Xenos Bio

  • Addressing the peptide discovery gap by exploring why so few peptide drugs have emerged from traditional screening campaigns despite their therapeutic advantages
  • Overcoming the limitations of conventional screening by moving beyond high-volume approaches that favour strong binders, overlook dynamic interactions and provide limited mechanistic insight
  • Breaking through the combinatorial challenge of discovering longer peptides, where traditional screening becomes increasingly time-consuming, expensive and impractical beyond approximately 12 amino acids
  • Predicting protein–peptide interactions without structural input using a physics-informed, sequence-driven approach to identify interactions that conventional screening may miss
  • Demonstrating structure-free peptide discovery in practice through experimental examples and existing datasets, while exploring the implications for faster, more targeted future discovery
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    11:10

    Session Reserved for Gold Sponsor

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    11:40

    An Integrated Evolutionary and Reinforcement Learning Framework for Multi-Objective Antibody Developability Optimization

    Dr Amendra Fernando, Principal Scientist, Pfizer Inc.

  • Simultaneously optimising multiple antibody developability properties, including immunogenicity, chemical stability, non-specificity, viscosity and sequence liabilities
  • Combining multi-objective evolutionary algorithms with reinforcement learning to identify optimal trade-offs and generate improved antibody variants
  • Incorporating prediction uncertainty and confidence to guide mutation selection and improve the reliability of computational design
  • Compressing months of iterative experimental screening into a single computational optimisation run followed by targeted validation
  • Demonstrating improved therapeutic antibody candidates with fewer immunogenic hotspots and high-risk chemical liabilities while maintaining overall developability

     

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    12:05

    Session Reserved for Gold Sponsor

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    12:35

    Lunch & Networking

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    13:35

    Session Reserved for Silver Sponsor - Chemical Computing Group

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    14:05

    Ensuring Manufacture of Next Generation Biopharmaceuticals by Developability (EMBeDs)

    Professor David Brockwell

    Professor David Brockwell, Professor of Biochemistry and Molecular Biology, University of Leeds

  • An overview of a wide range of developability assays (17 methods, 27 variables), coupled with long term (5°C) and accelerated (25°C and 45 °C) stability studies on 44 next generation mAb scaffolds.
  • Using this large dataset to obtain insight into key developability assays for next generation formats and the relationship between different assays
  • How the outputs of these assays can be used to predict biophysical properties that are difficult to perform or expensive in terms of material or time
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    14:30

    From AI-Designed Protein to Production: Applying AI/ML to the Manufacture of Complex Biologics

    Professor Adam Brown, Professor of Biopharmaceutical Engineering, University of Sheffield

  • Bridging the gap between AI-enabled protein design and successful production
  • Applying AI/ML to vector, cell and media engineering to improve production of complex synthetic products
  • Overcoming manufacturability challenges as increasingly complex AI-designed proteins progress through development
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    14:55

    Session Reserved for Silver Sponsor

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    15:25

    Afternoon Coffee & Networking

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    15:55

    Executive Round Table #1: Scaling AI Across Biologics Discovery

    Professor Pranam Chatterjee

    Professor Pranam Chatterjee, Assistant Professor, University of Pennsylvania

  • Scaling AI platforms across multiple therapeutic modalities and global research teams
  • Creating collaborative ecosystems between computational scientists, biologists and laboratory researchers
  • Measuring scientific and commercial value beyond successful proof-of-concept projects
  • Balancing innovation, governance and investment as AI adoption accelerates
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    15:55

    Executive Roundtable #2: Preparing Scientists for the AI-Native Laboratory

    Dr Stefan Schiesser

    Dr Stefan Schiesser, Director of Medicinal Chemistry, AstraZeneca

  • Redefining scientific roles as AI becomes embedded across discovery workflows
  • Developing the skills needed to work alongside intelligent research systems
  • Building trust, collaboration and adoption across multidisciplinary R&D teams
  • Creating organisational cultures that enable AI-driven scientific innovation
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    16:40

    Closing Panel Discussion: The Future of AI-Driven Therapeutics – What Will Drug Discovery Look Like by 2035?

  • Exploring how autonomous laboratories and scientific agents will reshape biologics discovery
  • Examining the convergence of foundation models, robotics and digital experimentation
  • Identifying the capabilities organisations must invest in today to remain competitive in an AI-first pharmaceutical landscape
  • Dr Tom Diethe, Executive Director, Enterprise AI, AstraZeneca

    Professor Pranam Chatterjee

    Professor Pranam Chatterjee, Assistant Professor, University of Pennsylvania

    Professor Adam Brown, Professor of Biopharmaceutical Engineering, University of Sheffield

    Dr Gerhard Hessler

    Dr Gerhard Hessler, Head of Synthetic Molecular Design, Sanofi

    Dr Graeme Robb, Director, Computational Chemistry, AstraZeneca

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    17:20

    Chairman’s Close & End of Focus Day

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    7:30

    Registration & Coffee

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    8:20

    Chair's Opening Remarks

    Mr Peter Clark, VP, Computational Drug Design, Novo Nordisk

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    8:30

    Opening Keynote: From AI Discovery Tools to AI-Powered Drug Creation Platforms

    Dr Tom Diethe, Executive Director, Enterprise AI, AstraZeneca

  • Case study examples of leading pharmaceutical companies successfully embedding AI across the entire discovery pipeline
  • Analysing the shift from isolated AI solutions to integrated, enterprise-wide discovery platforms
  • How AI operating models are reducing timelines, increasing productivity and improving portfolio decisions
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    8:55

    Pharmaceutical Superintelligence in Practice: Derisking Discovery from Target to Candidate

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

  • Pharmaceutical Superintelligence (PSI) combines Chemical and Biology/Clinical Superintelligence to support decisions from target to candidate
  • MMAI Gym for Science trains specialist models using multimodal scientific data, fine-tuning, reward models and reinforcement learning
  • Insilico uses its internal pipeline and 30+ nominated PCCs to benchmark and continuously improve models across biology, design, ADMET and synthesis
  • The result is a closed-loop AI platform that continuously learns from real-world drug discovery decisions to derisk candidate development
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    9:20

    Session Reserved for Lead Sponsor

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    9:50

    An AI-Driven, Hypothesis-Led Framework for Early-Stage Drug Discovery

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

  • In Silico Foundation: Utilizing Knowledge Graph, active learning and properties prediction to build vast initial compound libraries
  • Physical Integration: Building hypothesis-driven physical libraries through curated acquisition and synthesis
  • Biological Feedback: Implementing high-quality in vitro testing and cellular profiling to create a continuous “test-and-learn” feedback loop for AI model refinement
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    10:15

    Morning Coffee & Networking

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    10:45

    AI, Multi-OMICs and NAMs: Accelerating Drug Discovery Through Predictive Toxicity

    Dr Bjarki Johannesson

    Dr Bjarki Johannesson, Director, Cell Painting & Predictive Safety, AstraZeneca

  • Introducing a new AI-driven platform for high-throughput safety screening in drug discovery.
  • Integrating Cell Painting, multi-OMICs and New Approach Methodologies (NAMs) to predict toxicity earlier and with greater confidence.
  • Translating complex biological data into scalable, decision-ready safety insights that reduce late-stage attrition and accelerate the development of safer medicines.
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    11:10

    Session Reserved for Gold Sponsor - Schrödinger GmbH

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    11:40

    Panel Discussion: Operationalising AI Across Global R&D Organisations

  • Moving beyond successful pilots to enterprise-wide deployment
  • Building AI operating models that scale across therapeutic areas and research functions
  • Aligning data science, biology and chemistry teams around shared AI platforms
  • Measuring business impact beyond model performance
  • Mr Tim Hohm, Associate Director, Novo Nordisk

    Mr Renan Andrade Pereira, Head of Data Science, Servier

    Dr Anthony Bradley, Assistant Professor, University of Liverpool, Co-Founder & Chief Scientific Officer, DaltonTx, University of Liverpool

    Dr James Lumley, Senior Director & Head of Cheminformatics, GSK

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    12:20

    Session Reserved for Gold Sponsor

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    12:50

    Lunch & Networking

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    13:50

    Session Reserved for Silver Sponsor - Cresset Biomolecular Discovery

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    14:20

    Better Data, Better Discovery: Building the Experimental Foundations for Machine Learning in Drug Discovery

    Professor Nicola Burgess-Brown

    Professor Nicola Burgess-Brown, Professorial Research Fellow, Protein Sciences & COO Protein Sciences, SGC, University College London, School of Pharmacy, Pharma & Biological Chemistry

  • Exploring why the performance of machine learning models is fundamentally dependent on the quality, quantity and diversity of experimental data available
  • Identifying critical data gaps in protein and ligand research that limit the impact of ML in drug discovery
  • Developing strategies to increase the generation and collection of experimentally useful datasets
  • Examining how better data can improve ML-enabled approaches to making proteins and ligands and accelerate discovery
  • Moving the focus beyond model development towards building the data foundations needed for more effective AI-driven drug discovery
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    14:45

    Impactful Use Cases and Adoption of ML/AI in Drug Discovery

    Dr Simone Fulle

    Dr Simone Fulle, Head of Computer-Aided Drug Discovery, Basel, Novartis

  • Integration of physics-based modeling and machine learning into early drug discovery workflows
  • Internal examples from challenging targets across the DMTA cycle
  • Inhouse work on generative and predictive modeling for small molecules, plus current AI and ML trends
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    15:10

    Session Reserved for Silver Sponsor

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    15:40

    Afternoon Coffee & Networking

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    16:10

    Why AI/ML cannot yet do Drug Discovery - Putting the I into AI/ML

    Mr Jonathan Mason, Senior Research Advisor, Global Biotech

  • The severe limitations of models predicting bioactivity and ADME trained on public data sources
  • How water networks and lipophilic hotspots drive drug design and can put the I into Ai
  • New approaches using other causative descriptors
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    16:35

    Active Learning Processes in Biomolecular Modelling

    Professor Victor Guallar, ICREA Professor, Barcelona Supercomputing Center

  • The complementary role of AI and molecular modelling (MM), combining AI’s ability to explore chemical and biological space with physics-based modelling to overcome applicability-domain limitations
  • Iterative AI–MM feedback loops to improve modelling performance while reducing false positives and computational burden, and improving explainability with physics driven insights
  • Recent applications to challenging protein–protein interactions, including biologic design and targeted protein degradation
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    17:00

    In Silico Approaches for SiRNA-AOC Optimisation

    Dr Josephine Alba, Senior Expert, Data Science, Novartis

  • Therapeutic opportunities and challenges of siRNA and siRNA–antibody oligonucleotide conjugates (AOCs)
  • Current state of computational modelling for siRNA, including chemically modified nucleotides
  • Limitations of existing software and force fields for accurate siRNA modelling
  • Development and implementation of force-field parameters for siRNA-AOC systems
  • Integrating physics-based modelling and data-driven approaches for AOC-siRNA optimisation
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    17:25

    Navigating AI and Computational Chemistry for Drug Discovery and Translation in an Oncology Centre

    Dr Aleksandra Karolak, Assistant Professor, Moffitt Cancer Center and Research Institute

  • AI-Enabled Hit Discovery and Drug Repurposing: exploring virtual screening, multimodal molecular data, pharmacophore approaches, predictive AI, drug repurposing, and AI-enabled hit identification
  • An overview of molecular docking, molecular dynamics, free-energy methods, quantum computing, protein dynamics, and mechanistic prioritisation of candidate molecules
  • Generative AI for Molecular Design and Optimisation: generative models, reinforcement learning, scaffold optimisation, multi-objective design, ADME and drug-likeness, and AI-guided medicinal chemistry
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    17:50

    AI Automation of QTc-Concentration Analysis

    Mr Tejus Venkatesh Reddy, Global PK/PD and Pharmacometrics Associate, Leeds University and Industry Placement Student at Eli Lilly

  • The AI-led workflow incorporates templates for the analysis plan and report, dataset standards, and modelling scripts covering data formatting, modelling, prediction, and output generation
  • This AI-led workflow has the potential to improve the efficiency of this standard modelling assessment, resulting in significant time and cost savings
  • The approach can also be applied across all modalities
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    18:15

    Chair’s Closing Remarks and Close of Day One

    Mr Peter Clark, VP, Computational Drug Design, Novo Nordisk

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    7:30

    Registration & Coffee

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    8:20

    Chair's Opening Remarks

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

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    8:30

    Generating Molecules Like a Medicinal Chemist

    Dr Noel O'Boyle, Chemical Biology Resources Team Leader, EMBL-EBI

  • The ChEMBL database contains a large and diverse set of chemical information from many medicinal chemistry projects across academia and pharma
  • Pairs of molecules from the same medicinal chemistry assay can be regarded as similar in the context of a particular medicinal chemistry project
  • By training on these pairs, we have developed ANNalog, a Seq2Seq Transformer model that generates medchem-similar molecules given a query molecule

     

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    8:55

    From Language Models to Laboratory Insights: Building Agentic Systems for Chemistry Research

    Mr Nicholas Runcie, DPhil student, University of Oxford – Oxford Protein Informatics Group

  • Exploring how large language models can support chemistry and drug discovery research beyond general-purpose applications
  • Applying LLMs to interpret and reason across complex chemical and scientific information
  • Using language models to accelerate research workflows, hypothesis generation and scientific decision-making
  • Examining the opportunities and current limitations of LLMs as tools for practising chemists and drug discovery scientists
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    9:20

    Session Reserved for Gold Sponsor

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    9:50

    Judgment, Not Synthesis: If an LLM Becomes a Chemist, What's Lost and What's Left?

    Dr James Lumley, Senior Director & Head of Cheminformatics, GSK

  • Developing and embedding actionable AI for small molecule design
  • Rooting Generative Design in predictive synthesis and automation
  • Agents for program success
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    10:15

    Morning Coffee & Networking

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    10:45

    Session Reserved for Gold Sponsor

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    11:15

    Scaling Lab-in-the-Loop Discovery: The Journey Toward Self-Service MLOps and Agentic

    Mr Le (Muller) Mu, Principal Machine Learning Engineer, MLOps lead, Computational Sciences Center of Excellence, gRED, Roche

  • Evolution of Lab-in-the-Loop MLOps: Tracing our multi-year progression at Roche Pharma Research —from bridging initial engineering gaps to serving over 170 predictors and 150M+ monthly predictions across automated, real-time wet-lab feedback loops.
  • The Multi-Faceted Path to Self-Service: Demonstrating how we transitioned from a
  • hand-holding model to full scientist autonomy through Bring-Your-Own-Container (BYOC) patterns, using AI onboarding agents as a key enabler to remove infrastructure friction.
  • Scaling Business Volume with a Lean Operational Footprint: Sharing architectural and governance strategies that enabled a 10x adoption surge across R&D while keeping core system overhead low and maintainable.
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    11:40

    Session Reserved for Gold Sponsor

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    12:10

    From Design to Decision: Integrating AI-Driven Molecular Design into the Drug Discovery Cycle

  • Accelerating the DMTA cycle through AI-based molecular design and automated execution platforms
  • Generative AI design approaches boost compound optimization
  • Language models meet chemistry: Practical applications of LLMs across ideation, and molecular design
  • Dr Christoph Grebner

    Dr Christoph Grebner, Senior Principal Scientist, Synthetic Molecular Design / Computational and AI Strategy – R&D, Sanofi

    Dr Gerhard Hessler

    Dr Gerhard Hessler, Head of Synthetic Molecular Design, Sanofi

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    12:35

    Networking Lunch

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    13:35

    Why We Automated our DMTL Loops, and Why You Should Too

    Dr Martin Redhead, Vice President Primary Pharmacology, Recursion

  • Drug discovery has a high failure rate relative to the size of investment
  • This high cost of failure has hampered innovation; strategies to reduce the cost of failure to encourage working in novel areas are required
  • An insight into Recursion’s fully automated lab, which is capable of running design, make, test, learn loops for small molecule projects autonomously
  • An overview of computationally planned synthesis carried out on robots, and biological testing which requires zero human input
  • Removing the overhead of screening and iteration allows scientists to study their systems in more detail, derisking taking on novel biology
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    14:00

    Session Reserved for Silver Sponsor

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    14:30

    Automating the Medicinal Chemistry DMTA Cycle with Kernel Lilly

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

  • Automating key analysis, design and retrosynthesis activities across the medicinal chemistry DMTA cycle
  • Rebuilding an earlier virtual assistant as a scalable Python-based platform using agentic coding
  • Automatically detecting new experimental data, applying cheminformatics analyses and delivering decision-ready reports to scientists
  • Triggering downstream activities including new molecule designs and additional assay submissions
  • Lessons from deploying workflow automation to medicinal chemists and identifying where human scientists remain essential
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    14:55

    AI-Guided Discovery of a Clinical-Stage, Next-Generation HER2 Inhibitor

    Mr Laurent Gomez, Senior Vice President and Head of Discovery, Iambic Therapeutics

  • How AI-enabled molecular design accelerated the identification of a highly selective, brain-penetrant HER2 type II inhibitor
  • Exploring how AI, high-throughput screening and iterative optimisation improved selectivity, mutant coverage and in vivo efficacy
  • Learning how AI-guided discovery advanced a potential best-in-class candidate from design to Phase 1b clinical evaluation with favourable pharmacokinetic and safety profiles
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    15:20

    Afternoon Coffee & Networking

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    15:50

    Learning from Structure and Activity: AI-Enabled Molecular Design at GSK

    Dr Jack Glancy

    Dr Jack Glancy, Principal Computational Chemist, GSK

  • Co-folding and physics-based pose prediction
  • An insight into GSK dataset and its similarity to external structures
  • Synthetic data augmentation with FEP – active learning

     

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    16:15

    Panel Discussion: Creating the AI-Native Pharmaceutical Organisation

  • Defining the capabilities required for AI-first drug discovery
  • Preparing scientists for human-AI collaboration across research teams
  • Investing in platforms, infrastructure and talent for long-term competitive advantage
  • What separates AI leaders from organisations still struggling to scale?

     

  • Dr Simone Fulle

    Dr Simone Fulle, Head of Computer-Aided Drug Discovery, Basel, Novartis

    Dr Prakash Rathi

    Dr Prakash Rathi, Senior Director, Solutions Engineering, Roche Pharmaceuticals

    Dr Yogesh Sabnis

    Dr Yogesh Sabnis, Global CADD, UCB

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    16:55

    Laying the Foundations for Data and AI Driven Drug Discovery: From Lab Digitalisation to AI Augmented Scientists

    Ph.D Miriam Lopez-Ramos

    Ph.D Miriam Lopez-Ramos, Head of Data and AI Products for Research & CMC, Servier

  • Digitalising labs and data at the source to ensure quality, FAIR, and traceable data
  • Building scalable data and AI foundations to support ML and scientific compute: some end-to-end use cases supporting drug discovery
  • Democratising AI for scientists and enable adoption at scale through accessible tools and sandboxes, supported by governance, training, and change management

     

  • clock

    17:20

    Scaling AI Agents Across Pharma R&D with a Governed Multi-Model Platform

    Mr Renan Andrade Pereira, Head of Data Science, Servier

  • Building a vendor-agnostic operating model to safely test, benchmark, and deploy agentic AI across global R&D teams
  • Elevating standard coding assistants into specialized scientific agents by combining internal domain expertise with standardized configurations
  • Embedding governance, security, and cost control directly into R&D workflows while driving enterprise adoption
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    17:45

    Chair’s Closing Remarks and Close of Conference

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

    Confirmed Speakers Include:

    Assistant Professor
    Moffitt Cancer Center and Research Institute
    Principal Scientist
    Pfizer Inc.
    Senior Data Scientist
    Novo Nordisk R&D UK
    Assistant Professor, University of Liverpool, Co-Founder & Chief Scientific Officer, DaltonTx
    University of Liverpool
    Director, Cell Painting & Predictive Safety
    AstraZeneca
    Senior Principal Scientist, Synthetic Molecular Design / Computational and AI Strategy – R&D
    Sanofi
    Head of Synthetic Molecular Design
    Sanofi
    Director, Computational Chemistry
    AstraZeneca
    Principal Computational Chemist
    GSK
    Senior Director & Head of Cheminformatics
    GSK
    Senior Expert, Data Science
    Novartis
    Senior Director - Structure Based Drug Design
    Eli Lilly and Company
    Vice President Primary Pharmacology
    Recursion
    Managing Director and Co-Founder
    Xenos Bio
    Chemical Biology Resources Team Leader
    EMBL-EBI
    President, Insilico Medicine Canada and VP, Global Head of AI Platforms
    Insilico Medicine
    Senior Director, Solutions Engineering
    Roche Pharmaceuticals
    Director, Director of Computer-Aided Drug Design (CADD)
    UCB
    Head of Computer-Aided Drug Discovery, Basel
    Novartis
    Director of Medicinal Chemistry
    AstraZeneca
    Head of Data Sciences and Data Management
    Servier
    Global Head, Integrated Drug Discovery
    Sanofi
    Executive Director, Enterprise AI
    AstraZeneca
    Global CADD
    UCB
    Lead AI and GenAI Architect
    Bayer
    Senior Research Advisor
    Global Biotech
    Senior Vice President and Head of Discovery
    Iambic Therapeutics
    Principal Machine Learning Engineer, MLOps lead
    Computational Sciences Center of Excellence, gRED, Roche
    DPhil student
    University of Oxford – Oxford Protein Informatics Group
    Associate Director, In Silico Biologics Discovery
    Novo Nordisk
    VP, Computational Drug Design
    Novo Nordisk
    Head of Data Science
    Servier
    Global PK/PD and Pharmacometrics Associate
    Leeds University and Industry Placement Student at Eli Lilly
    Associate Director
    Novo Nordisk
    Head of Data and AI Products for Research & CMC
    Servier
    Professor of Biopharmaceutical Engineering
    University of Sheffield
    Professor of Biochemistry and Molecular Biology
    University of Leeds
    Professorial Research Fellow, Protein Sciences & COO Protein Sciences, SGC
    University College London, School of Pharmacy, Pharma & Biological Chemistry
    Assistant Professor
    University of Pennsylvania
    ICREA Professor
    Barcelona Supercomputing Center

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    Schrödinger

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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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    Chemical Computing Group

    Silver Sponsors
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    Chemical Computing Group (CCG) is a global leader in computer-aided molecular design software for pharmaceutical, biotechnology, crop science and academic organizations worldwide. Its main software platform, the Molecular Operating Environment (MOE), is used by computational chemists, medicinal chemists, and biologists throughout the world. CCG has a strong reputation for collaborative scientific support, providing organizations with expert collaboration across North America, Europe, and Asia. Founded in 1994, CCG is headquartered in Montreal, Canada.


    Cresset

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

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    IBI - International Biopharmaceutical Industry, provides the biopharmaceutical industry with comprehensive coverage of key scientific, technology, regulatory and business topics. The editorial mix of peer-reviewed papers, practical advice on managing bioprocessing and technology, regulatory and business columns, and expert commentary provides comprehensive coverage of upstream and downstream processing, manufacturing operations, regulations, formulation, scale-up/technology transfer, drug delivery, analytical testing and more. The insight and analysis covers biologic – based therapies including We will report on emerging trends, strategies and best practices in the key areas.


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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.

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    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.

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    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.

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