HOW TO GET INVOLVED
Partner with Us
It's never too early to get involved! Based on your objectives, we can create bespoke packages designed specifically for you.
Prepare to engage with decision-makers from leading pharma and biotech companies and understand how they are adjusting existing processes to drive decisions off of AI insights. Opportunities predominantly lie in 3 main categories: Thought Leadership, Branding & Networking.
Register as a Delegate
As the first AI-designed candidates begin to reach human trials, those involved in drug discovery are right to be preparing for a future in which AI is an integral part of the process. Step in to interactive discussion and engaging presentations with leaders from pharma, biotech, AI-tech companies and academia and optimise your drug discovery.
Welcome to the AI Driven Drug Discovery Summit
Several high-profile partnerships between pharma and AI companies have materialized recently, but with commercial validation of approaches taking place, improving technologies will expedite this process. The inaugural AI Driven Drug Discovery Summit will highlight key approaches to implementing novel AI technologies to expanding datasets. With this landscape rapidly evolving, the need to determine where and how AI can most add value has never been stronger. By uniting leaders from pharma, biotech, AI-technology companies and academia, this forum allows you to create a clear strategic vision and ambition regarding the use of AI to optimize drug discovery.
Why you should attend?
- Engage with leading pharma and biotech companies as they discuss the current state of AI-assisted drug discovery and explain how they are adjusting existing processes to drive decisions off of AI insights
- Discuss how AI can be leveraged to support multi-omic data integration
- Understand how recent advances in AI/ML are improving the accuracy of therapeutic target identification
- Discover how AI is accelerating drug safety assessments and how researchers are augmenting toxicity predictions through in silico ADMET models
- Connect with industry leaders using AI/ML to aid drug repositioning efforts for rare disease and learn from SARS-CoV-2 drug repurposing efforts
- Upskill decision makers and users on AI methodologies and limitations to avoid black box mistrust
- Identify new AI-native partners and learn to better manage existing partnerships to further integrate AI processes across functions
PAST ATTENDEES INCLUDE
PREVIOUS SPEAKERS
Reza Olfati-Saber
Reza is an experienced data scientist and systems and control theorist with over 25 years of experience working on machine learning and its applications in life sciences, technology, and finance. He specializes in deep learning, natural language process (NLP), and image processing with broad applications in drug design, discovery, and development.
Reza received his S.M. and Ph.D. degrees from MIT and conducted his postdoctoral research at Caltech under the supervision of Richard Murray. He had a successful academic career as a professor of engineering at Dartmouth College before transitioning into the industry to pursue his entrepreneurial passions. He joined AbbVie R&D in 2014 to initiate in silico design of therapeutic antibodies for Oncology and Immunology. This provided him with an invaluable opportunity to work with some of the world’s leading scientists in drug design, protein engineering, bioinformatics, chemistry, biochemistry, computational biology, oncology, and immunology. He also served as the managing director and Chief AI Scientist of EY-Advanced Technology Lab with offices in Cambridge, MA and London, UK managing all scientific aspects of AI solutions for the financial industry and partnership with academic institutions.
He is currently the global head of AI and Deep Analytics (AIDA) at Sanofi R&D. He manages a diverse team of data scientists with a strong core in life sciences industry in the US, France, Germany, and China (starting 2021). The key research areas in AIDA involve in silico drug design, multiomics and single-cell seq. analysis, precision medicine, digital pathology and bioimaging, and digital health in immuno-oncology immunology, and rare and blood disorders.
Reza is the recipient of the prestigious PECASE award from President Obama at the White House in 2010 for his pioneering contributions to distributed information fusion and collective behavior of networks of agents (hundreds to millions of interacting entities). Today, these inventions form the foundations of federated learning, privacy-preserving computing, and autonomous transportation. Reza has a life-time citation of over 37,000.
He strongly believes that diversity of backgrounds, gender, and skills form the core strength of any successful team.
Alex Gaither
Alex is Vice President and head of Biology in US for Exscientia. Alex has a long career in drug discovery research work for 18 years at Novartis and the Novartis Institutes for Biomedical Research and 2.5 years at the LG Chem Life Sciences Innovation Center in Cambridge. During his tenure at Novartis he supported each step of the pharmaceutical pipeline including early research, target discovery, drug discovery, and early translational work. He has contributed to multiple projects that have entered clinical trials and eventually to commercialization for small molecules, biologics, and siRNA therapeutics. While the Vice president and head of the Translational Medicine department at the LG Chem Life Sciences Innovation Center he managed the internal R&D portfolio of LG Chem Life Sciences in Seoul and supported the strategic advancement of collaboration activities for the Innovation Center in Cambridge to help drive new target and platform technology into clinical development.
Shameer Khader
Dr. Shameer Khader is a senior director of advanced analytics, data science, and bioinformatics at AstraZeneca, a global, science-led biopharmaceuticals company. At AstraZeneca, he leads a global team of data scientists, biomedical scientists, statisticians, and software engineers. He oversees a portfolio of projects in oncology and biopharma to accelerate drug discovery and development using biomedical and clinical big data using machine intelligence approaches. Before this role, he was a Program Director of Machine Learning & Data Science at Northwell Health, New York, USA. He built the first data science team, developed the strategy and lead operational data science activities for one of the largest health systems in the country with 12 billion in revenue.
Peter Henstock
Peter Henstock is the Machine Learning & AI Lead at Pfizer and based in greater Boston. His work has focused at the intersection of AI, visualization, statistics and software engineering applied mostly to drug discovery but more recently to clinical trials. Peter holds a PhD in Artificial Intelligence from Purdue University along with 6 Master’s degrees. He was recognized as being among the top 12 leaders in AI and Pharma globally by the Deep Knowledge Analytics group. He also currently teaches graduate AI, and Software Engineering courses at Harvard.
Kam Chana
Dr Chana is Director of the Computational Platforms group supporting research and development at Merck Research Labs. His group consists of High Performance Computing, Applied Computing, and Site Reliability Engineering teams providing capabilities and platforms for engineering software, scientific algorithms, and infrastructure supporting massively parallel and distributed compute. In addition his group supports capabilities for deployment engineering, instrumentation and monitoring of software, infrastructure and lab systems. His team utilizes and supports algorithms and platforms for cutting edge Deep Learning and AI, and is leading an initiative to develop Quantum Computing for life science R&D within Merck. Dr Chana holds a Ph.D. in Theoretical Condensed Matter Physics and has 20+ years’ experience in scientific computing for the life sciences industry.
Monica Wang
Monica currently leads the Biologics and Novel Modalities Discovery in Takeda Scientific Informatics. She comes with multidiscipline education with Ph.D. in Biochemistry and MS in Software Engineering. She has 8+ years of experience in academic research and 15+ years of experience in Scientific Informatics in the biotechnology and pharmaceutical industries. She is good at strategic planning with proven successful track records of managing complicated global enterprise informatics projects. She has delivered many informatics projects/programs within time and budget for many departments (Molecular Pathology, Protein Science, Biotherapeutics, Translational Medicine and Legal IP et al). She is technically and scientifically proficient in Bioinformatics, Cheminformatics, Functional Genomics, and Pharmacogenomics. Her team has designed and implemented many global enterprise informatics solutions to support biologics research, biomarker discovery, translational research and personalized medicine. Her recent focus is concentrated on building a state-of-art Global Biologics Platform utilizes in silico AI technologies to support the global biologics R&D research in Oncology, GI, CNS and Rare Diseases across Takeda.
Tudor Oprea
As Vice President, Translational Informatics at Roivant Discovery, Tudor leads translational informatics efforts ranging from targets through drug discovery as we advance our pipeline of medicines to the clinic. Tudor joined Roivant Discovery from the University of New Mexico. Tudor is a digital drug hunter with three decades of experience in machine learning and knowledge management applied to target and drug discovery.
At the University of New Mexico, Tudor served as director for screening informatics and, ultimately as chief of translational informatics where he received over $24 million in extramural funding for a broad range of projects. Prior to the University of New Mexico, Tudor worked at AstraZeneca where he conceptually developed the “lead-like approach” for combinatorial chemistry and fragment-based drug discovery. Tudor co-invented the first G-protein coupled estrogen receptor agonist, an orphan drug designated compound for uveal melanoma. He has developed machine learning models since 1989, first in cheminformatics and quantitative structure-activity relationship (QSAR) and later in disease and target biology. Tudor has co-authored over 270 publications, edited two books on informatics in drug discovery, has been awarded 10 U.S. patents and has been cited nearly 23,000 times.
Tudor received his M.D. in general medicine (1990) and Ph.D. in molecular physiology (1992) from the University of Medicine and Pharmacy in Timişoara, Romania.
Abhishek Pandey
Abhishek is group lead for Machine Learning team called RAIDERS: Pharma Discovery in abbvie which is a pan-abbvie team working in field of Machine Learning in chemistry and drug discovery, Machine Learning in imaging/multi-omics and Machine Learning in-charge in the multi-billion-dollar Abbvie-calico collaboration. His team focuses on reducing the time and accuracy of the drug discovery process using state of art AI algorithms. He is a Principal Research Scientist in Machine Learning and Deep learning. In his previous life he was inaugural member of precision medicine AI team and helped build and transform Tempus Labs Inc. in the field of precision medicine. He had done his PhD work from University of Arizona in electrical and computer engineering with applications in imaging and medicine.
Zhiyong (Sean) Xie
Zhiyong (Sean) Xie is a director of Statistical Learning and Artificial Intelligence in Pfizer Drug Safety Research and Development (DSRD). He serves as a technical lead to establish AI/ML strategies and implement AI/ML solutions in DSRD. Before his current position, Dr. Xie had worked in translational imaging group of Pfizer for 15 years where his major responsibility includes developing imaging/digital biomarkers and supporting clinical/preclinical studies using these technologies.
Prior joining Pfizer in 2004, Zhiyong did post-doc training in radiology department of University of Pennsylvania. He received his Ph.D. in computer science from Arizona State University, MS in computer science from Peking University, and BS in Mathematics from Northwest University in China.
Neil Thompson
Over the last 30+ years, Neil’s career has seen him progress more than 10 drugs to patients and he’s led a number of drug discovery projects in areas of cardiovascular, neurology, inflammation and oncology among others.
For 15 years, Neil was Senior Vice President of Biology at Astex Pharmaceuticals, helping the company establish itself at the forefront of drug discovery by leading the biology and pre-clinical teams to deliver two new cancer drugs (Kisqali; ribociclib and Balversa; erdafitinib in partnership with Novartis and Janssen respectively) to market. Before that, he was Director of the Immunology Platform at GlaxoSmithKline (GSK).
Neil is also Deputy Chair of the UK MRC DPFS panel and Chair of the MRC CLD/ADI committee, has authored more than 40 publications and patents and sits on the advisory boards of several scientific institutions in the UK.
Neil received his doctorate in Biochemistry from King’s College London.
Bhupathy Alagiriswamy
Bhupathy is a qualified Pharmacist, with over 16 years of experience in the Clinical Research Industry. He worked with leading Pharmaceutical companies and CROs in diversified Clinical trials.
He is a Tech enthusiast; he developed a great passion for disruptive technology solutions in digital healthcare. He is an entrepreneur, and his strong skill sets are Artificial intelligence in Clinical research and Decentralised Clinical Trials. His vision to enhance patient care by cost-effective drug development using disruptive technology.
Gurpreet Singh
Yu Qiu
Experienced professional of antibody engineering, multi-specific Biologics modeling and design. My arsenal for bio-therapeutic engineering and design includes structural informatics, molecular dynamics simulation, function-driven rational design, and more importantly, their combination with power of data analysis. My goal is to design First-in-class and Best-in-class Biologics from de novo, which can truly empower patients’ lives.
Graham Dempsey
Graham Dempsey, Ph.D., leads Q-State’s research and development efforts. In this role, he oversees all internal development work and ensures successful execution of Q-State’s partnered programs with pharmaceutical companies and foundations. Graham began his scientific career at the University of Pennsylvania, developing novel single molecule methods for high sensitivity fluorescence measurements of molecular function. He then joined Harvard University where he developed a new super-resolution imaging technique called stochastic optical reconstruction microscopy (STORM), allowing for multicolor, live-cell fluorescence imaging of biological samples with nanometer spatial resolution. The technique was commercialized by Nikon Instruments. Graham received a B.A. in Biochemistry and Physics from the University of Pennsylvania and a Ph.D. in Biophysics from Harvard University.
Prashant Desai
Prashant Desai is currently a senior director in the ADME (DMPK) function of Eli Lilly and Company, responsible for in silico and in vitro modeling groups namely, Computational ADME, Mechanistic PK and Investigational Drug Disposition. He received BS in Pharmacy, MS in medicinal chemistry and PhD in Biophysics and Computational Chemistry from University of Bombay (India).
Prashant has been working with the DMPK function in Eli Lilly for the past 15+ years. He started his career at Lilly as a computational ADME scientist in 2007 and has been instrumental in building the infrastructure for machine learning models for predicting ADME properties, among other cheminformatics tools. He has also served as ADME project leader across various therapeutic areas during his career at Lilly. As a scientific leader in the preclinical ADME research, Prashant’s primary focus has been on effective integration of in silico, in vitro, and in vivo ADME models during the early phase of drug discovery in line with the mechanistic pharmacokinetics principles. He has contributed to more than 50 papers in peer reviewed journals and book chapters.
Jason Johnson
Jason Johnson, PhD, joined the Institute in April 2016 and serves as Chief Data and Analytics Officer and Senior Vice President, Informatics & Analytics. Prior to joining Dana-Farber, Dr. Johnson was executive vice president and head of R&D at PatientsLikeMe, a patient-focused research company in Cambridge, MA. He came to that position after serving in various leadership roles in scientific informatics, bioinformatics, and genomics at Merck for many years. Dr. Johnson holds AB and BS degrees from Stanford University, a master’s degree from the University of Cambridge (UK), and a PhD in Biophysics from Harvard University.
Dane Corneil
Dane is the Target ID AI Team Lead at BenevolentAI, where his work focuses on building models to flexibly identify novel therapeutic targets in an interpretable way. He holds PhD and Masters degrees in computational neuroscience, with a particular focus on model-based reinforcement learning.
Austin Clyde
Deisy Morselli Gysi
Virginia Savova
Dr. Virginia Savova is an internationally renowned expert with over 15 years if experience in leveraging cutting-edge genomic technologies and machine learning in clinical research. After graduating from Harvard cum laude in 1999, and pursuedgraduate and post-graduate training in computational Cognitive Science at the Johns Hopkins University and MIT. Driven by a life-long fascination with biology, she ultimately joined the ranks of the genomics revolution at the Broad Institute and the Harvard Medical School, where she developed machine learning models of epigenetic regulation, contributed to the first microfluidic single-cell protocol and led on its early clinical applications. In 2017, she joined Sanofi to establish a group combining single-cell omics and artificial intelligence to uncover disease-related changes in immune cells. She represents Sanofi at the Accelerating Medicine Partnership – an NIH initiative which brings together clinicians, academics and industry experts seeking to apply these technologies to a broad range of a disease, and served as the industry co-chair for strategy development for AMP-AIM. In recognition of her impact, she was named by Business Insider among 100 People Transforming Business in 2020. In 2022, she became is a Senior Director and Global Head of Single-cell Biology at in the newly established Precision Medicine and Computational Biology Research Platform at Sanofi.
Peter Clark
Peter Clark, PhD is the Head of Computational Science & Engineering within the Therapeutics Discovery (TD) organization of Janssen R&D, where he leads a dynamic, interdisciplinary team of scientists focused on accelerating the research and development of protein based therapeutics through the design and implementation of disruptive computational approaches and platforms. Prior to joining Johnson & Johnson, Peter served as the Director of Bioinformatics at the University of Pennsylvania, Perelman School of Medicine, where he worked closely with academic and commercial collaborators on the design, optimization, and evaluation of various gene therapy platform technologies from early research and development through commercially partnered IND enabling clinical studies. Peter is also a clinically trained molecular pathologist (The Children’s Hospital of Philadelphia) with extensive experience in the design, validation, and implementation of diagnostic and prognostic next generation sequencing (NGS) assays within a clinical, CAP/CLIA certified laboratory setting. During his clinical molecular pathology fellowship at CHOP, Peter co-developed and commercialized the first to market, high resolution, next generation sequencing based HLA genotyping assay and was subsequently awarded the Scholar of the Year award by the American Society for Histocompatibility and Immunogenetics (ASHI) in 2015 for his contributions to the field of solid organ and bone marrow transplantation. Peter earned his B.Sc. and Ph.D. from Drexel University, School of Biomedical Engineering prior to completing a postdoctoral fellowship at The Center for Computational Medicine at Thomas Jefferson University. Peter’s diverse expertise in computational science, engineering, clinical molecular genetics, computational biology, and translational research has led to the publication of over 30 peer-reviewed scientific publications as well as two book chapters, several issued patents and three biotechnology spin-off companies.
Nicholas Nystrom
Nicholas A. Nystrom, PhD, is Chief Technology Officer at Peptilogics, where he leads development of Peptilogics’ Nautilus™ AI peptide drug design platform.
Nick’s areas of expertise include deep learning, traditional machine learning, computer architecture and high-performance computing, software engineering, and computational chemistry. He earned his PhD in quantum chemistry.
Prior to joining Peptilogics, Nick was Chief Scientist at the Pittsburgh Supercomputing Center (PSC), where he accelerated scientific discovery by pioneering the convergence of AI and HPC. He designed and was principal investigator for supercomputers including Blacklight, Bridges, and Bridges-2, which together drove advances for over 50,000 users globally. He also codesigned Neocortex to allow scaling deep learning training across multiple wafer-scale processors.
Specific to the life sciences, Nick was PI for the Human BioMolecular Atlas Program (HuBMAP) program (NIH), creating computational hardware and software infrastructure to develop a map of all tissues of the human body at single-cell resolution, with data spanning genomic, proteomic and imaging modalities. He led early work to establish interoperability between HuBMAP and other medical data initiatives involving childhood cancers, structural birth defects and nerve-organ interactions. Nick has also led machine learning for breast and lung cancer and causal discovery focusing on cancer driver mutations, lung fibrosis, and the brain causome, the latter being an NIH Big Data to Knowledge Center of Excellence. In 2020, Nick participated in the White House COVID-19 High Performance Computing Consortium, through which the Bridges supercomputer was made available for urgent research on the emerging virus.
In fields beyond the life sciences, Nick has led R&D in computational chemistry, computer architecture, computer languages, ab initio molecular dynamics, performance engineering, combustion kinetics, high energy nuclear physics, and nuclear engineering. He has organized international conferences focusing on AI, HPC and computational science, presented and published on the topics listed above, and chaired or otherwise served on numerous federal review panels.
Natalia Vassillieva
Natalia was a Sr. Research Manager at Hewlett Packard Labs, where she led the Software and AI group and served as the head of HP Labs Russia from 2011 until 2015. Prior to HPE, she was an Associate Professor at St. Petersburg State University in Russia and worked as a software engineer for several IT companies. Natalia holds a Ph.D. in computer science from St. Petersburg State University.
Tom Wilson
Marcin Skwark
Dr Marcin J. Skwark is the Bioinformatics Lead at InstaDeep Ltd. Before joining InstaDeep in the beginning of 2020, he conducted academic research in computational structural biology at universities in Stockholm (Stockholm University), Helsinki (Aalto University), Beijing (Tsinghua University), Nashville (Vanderbilt University), and Cambridge, UK (University Cambridge). His interests revolve around application of machine learning to modeling complex biological systems, structural bioinformatics (esp. protein design), as well as computational methods to enable groundbreaking discoveries in life sciences.
AGENDA
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We’re optimistic for a future where people live healthier, more informed, more creative lives. We see a world where technology enhances human potential, and takes us into a new era of intelligence and progress that everyone can benefit from.
We let innovators create the next breakthroughs in machine intelligence.
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Cerebras
Website: https://www.cerebras.net/industry-pharma/
Cerebras Systems makes the world’s most powerful AI accelerator, removing roadblocks to biomedical research, drug discovery and data-driven healthcare. Our CS-2 system is doing groundbreaking work at leading institutions including GlaxoSmithKline, AstraZeneca, and Argonne National Laboratory. We offer cluster-scale deep learning acceleration in a single, easy-to-program device, so your researchers can focus on medical innovation, not on working around the limitation of traditional computing systems.
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Website: https://nanome.ai/
Nanome is changing how we understand, design, and interact with science. Nanome’s immersive virtual workspaces allow users to visualize, modify, and simulate chemical compounds, proteins, and nucleic acids to help improve the Drug Discovery process.
Our virtual reality platform facilitates communication of structural data in drug discovery which has proved beneficial to pharmaceutical and biotech companies across the globe. This is especially helpful for organizations that are interested in improving their cross-site collaboration.
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