Augmented Intelligence in Drug Discovery Xchange
West Coast 2021
Welcome to hubXchange’s West Coast Augmented Intelligence (AI) in Drug Discovery Xchange 2021, bringing together executives from pharma and biotech to address and find solutions to the key issues faced in AI-led drug discovery.
Discussion topics will cover Data Quality, Target Identification, Lead Generation, Lead Optimization and Drug Response Prediction.
Take advantage of this unique highly interactive meeting format designed for maximum engagement, collaboration and networking with your peers.
- Industry perspective from a vendor
- Predicting the impact of AI/ML on drug discovery
- Our recommendation for the ecosystem
Chief Solutions Officer
Haranath Gnana has over two decades of strategy consulting and analytics-led solutioning experience across Life Sciences & Healthcare. His expertise involves solving complex business problems with data & analytics and enabling enterprises to become data-driven organizations. Before joining Excelra, Haranath co-founded two start-ups in the Bay Area where he extensively worked on AI/ML technologies. He has also worked for leading companies such as Kaiser, Genentech, Franklin Templeton, Cisco, and TiVo.
- Explore the effective, and realistic approaches to annotate and share data; define data quality aspects for ML applications
- How do we best utilize and interpret data of quality?
- Discuss why executing data quality is a continuous process
- Share experiences in tackling data quality challenges
Associate Director Protein Engineering
Bristol Myers Squibb
Sean West is a computational protein engineer at Bristol Myers Squibb. He has spent over 15 years using computational methods to understand and explore protein interactions, from transcription factors and RNA binding proteins, to antibody-antigen recognition. Throughout the antibody discovery process, we use a variety of methods to produce molecules with good specificity, affinity, immunogenicity, and biophysical characteristics. Computational workflows can be used to supplement experimental techniques but require good datasets. Part of the challenge of antibody discovery and molecular design is generating datasets appropriate for computational analyses, a challenge faced by many in our industry.
Discuss the Challenges in Acquiring and Leveraging Clean Data in Drug Discovery
- What are the areas in drug discovery where clean data is most lacking?
- How do we harmonize disparate data formats from different sources?
- How can data be stored and organized for maximum utility AND security?
Collaborative Drug Discovery
Barry Bunin, Ph.D. is CEO of Collaborative Drug Discovery, Inc., provider of the CDD Vault research informatics platform and BioHarmony drug data store. Prior to CDD, he was an Entrepreneur in Residence with Eli Lilly & Co., and was also the founding CEO, President, & CSO of Libraria (now Eidogen-Sertanty). Dr. Bunin is on a patent for Carfilzomib, a protease inhibitor drug for the treatment of multiple myeloma. Dr. Bunin received his B.A. from Columbia University and his Ph.D. from UC Berkeley, and has co-authored textbooks on cheminformatics and chemical synthesis.
- Share experiences in multi-omics data acquisition and curation.
- Discuss sources of bias in data, and approaches to overcome these problems.
- What are the best methods to facilitate data integration?
- How do we best leverage multi-omics data for drug discovery?
Head of Computational Biology
Victor Hanson-Smith, PhD, leads a world-class team of computational biologists to discover new drugs for neurodegenerative diseases, including ALS and Parkinson’s Disease. His team’s work combines a wide range of methods, including genomics, systems biology, machine learning, and supercomputing. Prior to joining Verge Genomics, Victor completed his PhD in computer science at the University of Oregon, and a post-doctoral fellowship at University of California San Francisco. He has published extensively on topics related to eukaryotic genome function and evolution.
2:35 – 3:05pm
Combining text analytics + semantic enrichment with Elsevier’s content to power AI/ML initiatives
- Generate artifacts using Elsevier’s large repository of full text articles, domain specific scientific databases containing third party publisher information.
- Leverage Elsevier’s industry standard drug discovery ontologies and taxonomies to create knowledge graphs, network maps and machine learning models.
- Transform unusable text to data in a richly annotated, machine-readable and standardized data format
- Harmonize technology through scientific ontologies adhering to public standards
4:10 – 5:10pm
- Discuss how to balance data obfuscation with data utility for meaningful data sharing
- Explore if organisations are over-valuing their data assets and preventing meaningful data sharing
- Explore the ideal composition of data-sharing consortiums and initiatives- larger pharma and biotech versus smaller start-ups
Director, Computational Chemistry
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. 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 8000 IC50 assays, and
2) “rational oral bioavailability design” during lead optimization by applying machine learning and global sensitivity analysis to physiologically-based pharmacokinetics simulations.
For the former, Eric was awarded the lifetime title of “Novartis Leading Scientist”.