AI in Pharma: Discovery West serves as a meeting point for thought leaders from the most innovative biotechs and the biggest pharma across the AI in pharma industry. Learning through a wide selection of thought-provoking case studies, presentations and interactive panel discussions, this Summit is not to be missed!
With an agenda focused on cutting-edge innovation, led by an expert speaker faculty of industry innovators, this is an unparalleled learning opportunity. Topics include overcoming hurdles in AI-enabled target identification and drug design, data standards and industry bottlenecks, the gender gap, overcoming hype, and the automated lab of the future.
The intimate conference size enables you to maximize senior level networking opportunities with key decision makers and innovators from biotech and pharma. With multiple networking sessions throughout the day, this is your chance to meet and reconnect with new and existing peers and engage in impactful discussions.
“I’ve always enjoyed AI in Pharma events for their high-quality speaker line-up and timely topics. It is a great way to catch up on the latest happenings in the field and meet colleagues new and old.”
“AI in Pharma is an opportunity for me to connect with an area of analytics I find incredibly interesting but I rarely have the chance to dig into. Each time I attended the conference in the past I significantly expanded my understanding of how AI is helping R&D become more effective and efficient, and I am expecting the same experience this year.”
“I’m excited to join the “the AI in Pharma Discovery West Summit” because data science in general and artificial intelligence technologies in particular are a critical component of our efforts to accelerate the drug discovery process. They materialize at various stages of our value-chain from library design and reaction optimization to the analysis of profiling data.”
“I am looking forward to presenting my most recent work in massively-multitask machine learning, and to hearing about others’ efforts applying AI to target discovery and drug design. I look forward to stimulating discussions with many experts in the vibrant Bay Area AI/drug discovery ecosystem.”
“I am looking forward to the AI in Pharma: Target identification conference because AI applications in pharma have matured beyond the hype and are driving real impact in drug discovery. Having the opportunity to interact – in person – with other scientific leaders pushing the envelope in data science applications to problems in human health is something I’m particularly excited about.”
“Recent advances in AI/ML (including large language models) have the potential to drive unprecedented productivity improvements in drug discovery and early development. Biopharma enterprises that embrace these technologies and architect their data strategy accordingly will be advantaged in building compelling pipelines. I look forward to discussing the opportunities and practical challenges ahead with some of the leaders in our industry at the AI in Pharma Summit.”
Expert Speaker Faculty
Wade Davis is VP of Computational Science at Moderna. He holds a PhD in Statistics and completed an NIH Fellowship in Biomedical Informatics. Afterwards, he began his career in academia at Baylor University and then University of Missouri School of Medicine, where he was a tenured Associate Professor. Wade has received research funding from dozens of federal research grants and served on the editorial board for the Journal of the American Statistical Association. Wade then joined AbbVie, where he took roles of increasing responsibility and ultimately became Global Head of Bioinformatics and Computational Biology. Later, he was VP of Analytics and AI at Karius, a late-stage start-up using artificial intelligence for unbiased diagnosis of pathogens using microbial cell-free DNA. In his current position at Moderna, he leads wet and dry lab teams using computational methods to solve complex problems in R&D. Throughout his career, Wade and his teams have received many awards and he has given multiple conference keynotes. He has authored 100+ publications with dozens focused on target identification, predicting target-indication success, mechanism of action, and precision medicine.
Dr. Mohindru is the Chief Executive Officer and Board Member of Novasenta, Inc. a privately held biotechnology company focused on the discovery of novel targets to develop innovative therapies for cancers. She also serves as a member of the board of directors of CytomX Therapeutics and Cardiff Oncology. Prior to Novasenta, she has held senior leadership positions in the biotech industry, having served as Chief Executive Officer of CereXis, Inc. (private), Chief Financial Officer and Chief Strategy Officer of Cara Therapeutics, Inc. (public), and Chief Strategy Officer of Curis, Inc (public).
Prior to her leadership roles in the biotechnology industry, Dr. Mohindru spent several years as an equity research analyst covering the biotechnology sector at UBS, Credit Suisse and ThinkEquity. She also co-founded a private biotechnology company and was a healthcare industry consultant. On the non-profit side, she is a member of the Executive Advisory Board of the Chemistry of Life Processes Institute of Northwestern University and a member of the Scientific Investment Advisory Committee of the Gates Center of Regenerative Medicine at the University of Colorado.
Dr. Mohindru received her Ph.D. in Neurosciences from Northwestern University and her Masters in Biotechnology and BS in Human Biology (Hons) from the All India Institute of Medical Sciences, India.
Patrick Schwab is Senior Director of Machine Learning and Artificial Intelligence and Head of the Biomedical AI group at GSK AI/ML. His work aims to advance personalised medicine and drug discovery by utilising machine learning and computational systems biology methods and large-scale health data, such as genetics, single-cell, multi-omics, cell-based assays, and continuous measurements from smart devices and electronic health records, to better understand and treat complex diseases. His research appeared both in leading machine learning venues (NeurIPS, ICML, ICLR, AAAI) as well as interdisciplinary journals (Nature Communications, npj Digital medicine, PloS Digital Health).
Prior to joining GSK, Patrick was a Principal Architect working on Machine Learning for Personalised Medicine at Roche in Basel, Switzerland and at Genentech in South San Francisco, US. Before joining Roche, he was a doctoral researcher working on Machine Learning for Healthcare at ETH Zurich where he worked, in close collaboration with students, medical researchers, clinical experts and industrial professionals from University Hospital Zurich, the Balgrist clinic and Hocoma AG, on applying machine learning methods in the medical domain. Prior to that, he collected 5 years of experience in using machine learning to automate business processes and engineering custom software solutions.
He holds a PhD in Machine Learning (2019) from ETH Zurich, Switzerland, a MSc in Computer Science (2015) with distinction from the University of Vienna, Austria and a BSc in Computer Science (2013) with honors from Technikum Vienna, Austria.
Camilo is a data scientist currently leading the commercial advanced analytics team supporting the biologics business unit at Astrazeneca. Before Astrazeneca he led the commercial advanced analytics organization at Alkermes, the compliance analytics efforts at Pfizer and the text mining and natural language processing hub at Lilly. He also co-led the creation of the advanced business analytics group at Lilly and he was responsible for building the analytical capabilities of Lilly’s clinical supply chain organization.
Camilo is an engineer by training with M.S. degrees in Chemical and industrial engineering and a PhD in Chemical engineering from Purdue University.
Peter leads a small, nationally distributed team of data scientists in Chemical Biology & Therapeutics working to accelerate drug discovery with AI and data-driven insights at the Novartis Institutes for BioMedical Research (NIBR) in Emeryville, CA. Peter joined NIBR in 2011 from the Biological & Medical Informatics PhD program at UC San Francisco, where he built molecular and software tools to aid in the discovery of novel and divergent viruses using next-generation sequencing. His other experience includes working in corporate IT at PwC; extending his undergraduate training (UC Berkeley, Immunology) to research on intrauterine bone marrow transplantation at UCSF; and designing, spotting, and running microarrays and writing data analysis tools to study pancreas development at the Diabetes Center at UCSF. His team’s current research interests focus on AI in several contexts: identifying drug targets from phenotypic data, optimizing compound properties using generative chemistry, and predicting structures and complexes for targets of interest. He also maintains an infectious diseases bent to his research in collaboration with the co-located Novartis Institute for Tropical Diseases. Peter lives in the Bay Area with his wife and four children.
Eric Martin has a Ph.D. in physical organic chemistry from Yale University. He has worked in
computational drug design and herbicide design for over 35 years at Dow, DowElanco, Chiron
and Novartis. He is currently developing novel methodologies for two areas of drug discovery:
1) Developing “Profile-QSAR”, a massively multitask machine learning method that builds
experimental-quality virtual screening models for over 9000 IC 50 assays, and 2) “rational oral
bioavailability design” during lead optimization by applying global sensitivity analysis to
physiologically-based pharmacokinetics simulations. Eric was awarded the lifetime title of
Novartis Leading Scientist for the former.
Brad Parry, Ph.D. is the Head of Artificial Intelligence at AI Therapeutics. Dr. Parry graduated with a Ph.D. from Yale University in the lab of Christine Jacobs-Wagner where he pursued questions in biological physics concerning single living cells. To answer these questions, he developed novel experimental and computational approaches based in machine learning and deep learning to investigate the physical nature of the cytoplasm. After completing his degree, he continued developing and applying deep learning based approaches to investigate questions in cell biology as a research scientist at Yale. He now leads efforts to develop new datasets, algorithms and predict novel disease treatments as the Head of AI at AI Therapeutics.
Alexander Marziale obtained his PhD from the Technical University of Munich, Germany in
bioinorganic chemistry under the supervision of Prof. W. A. Herrmann in 2011. He subsequently moved to
the California Institute of Technology where he conducted his post-doctoral studies in the field of
asymmetric catalysis, biocatalysis and organic synthesis with Prof. Brian M. Stoltz and Prof. Frances
Alexander subsequently joined the Global Discovery Chemistry group at Novartis in late 2013. In
this role he has served as team- and project leader for the so-called MicroCycle platform for automated
and integrated drug discovery.
In early 2022 Alexander moved to Gothenburg, Sweden and joined AstraZeneca as Director of
the innovation Lab (iLAB). The mission of the iLAB is to generate tangible impact for portfolio projects and
accelerate drug discovery by leveraging emerging technologies such as machine learning and lab
Rohan Ganesh is a Partner at Obvious Ventures, an early-stage venture capital firm that invests in world positive companies reimagining trillion-dollar industries. At Obvious, Rohan specializes in investing in companies that apply cutting-edge AI/ML techniques to improve drug discovery and development, clinical trial operations, and biomanufacturing. He supports companies such as Anagenex, Gandeeva Therapeutics, Inato, and LabGenius as a board director or observer.
Prior to Obvious, Rohan was a biotech and life science venture investor with Northpond Ventures, a dedicated life sciences venture firm, and Verily Life Sciences, a Google spinout. He started his career at the intersection between data science, life sciences and healthcare as product manager for CompuGroup Medical, a leader in health-IT in Europe, and in the computer-aided detection and machine learning group at Verily.
Rohan studied Biological Sciences at the University of Oxford and holds an MBA from Harvard Business School.
Lead Research Data Sciences function to support research discovery and pipeline programs across therapeutic areas (oncology, inflammation, fibrosis, virology). Established and enhanced capabilities in computational biology, analytical methods, data infrastructure and next generation sequencing technologies. Built cross-functional partnerships across Research, Development, IT and helped to establish enterprise data governance and data sharing principles and guidelines.
The time has come to meet in-person once again! Join your peers for live presentations, engaging discussions and face-to-face networking. Catch up with familiar faces and make new connections within the field.
We will be coming together in the Bay Area, CA.
Our venue will be announced soon!
Bringing together senior-level executives from across the industry, AI in Pharma: Discovery West promises an unrivalled networking and learning opportunity for everyone working in this space.
AI in Pharma: Discovery West provides a rare opportunity to showcase your offering to a dedicated, world-class audience. Spaces are limited this year, please contact our commercial manager, Elliott (firstname.lastname@example.org) to discuss opportunities for involvement.
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