Abhinav Kumar

Abhinav heads the Reaxys Data & Predictive Technologies development team with a focus on bringing innovative products to market to help accelerate drug discovery. The team has developed multiple data and AI products to expedite small molecule discovery. They have launched Reaxys Predictive Retrosynthesis, Synthetic Accessibility, Machine Learning Optimised Reactions Datasets, New Reaxys Datasets API and enhanced traditional ontology driven keyword-based searching with vector search.

Prior to this role, Abhinav led multiple consulting projects at Deloitte, PwC and Evalueserve, advising the board and senior leadership teams of leading UK and global life sciences firms on their drug development and business transformation strategies. Abhinav earned a doctorate in Pharmaceutical Science from King’s College London.

Andrei Kamenski

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

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 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

Dr Christoph Grebner

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 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 Stefan Schiesser

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.

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

Jack Glancy

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.
 

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.

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.

Miriam Lopez-Ramos

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.

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.
 

Prakash Rathi

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 Nicola Burgess-Brown

Professor Pranam Chatterjee

Professor Victor Guallar

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.

Simone Fulle

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.

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.

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.