February 26-28, 2018

San Francisco, USA

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Workshop A
Monday 26th February, 2018

09.00 - 11.30

AI in Drug Discovery: A Workshop on Strategies & Applications


This interactive workshop session will delve deep into the multiple applications of AI & machine learning while addressing the bottlenecks and opportunities to assess success and failure of AI-driven drug discovery projects.

 

  • Fundamentals, challenges & opportunities of machine learning, deep learning & probabilistic computing
  • Datasets: Opportunities & obstacles with specific reference to electronic health records & genotypeenabled electronic health records
  • Applications in drug discovery: Drug repurposing, ligand-based & structure based prediction of bioactivity, de novo drug design & toxicology
  • Case studies: DeepSEA, Basset, DeepChem, DeepMetabolism & Deep Pharmaco-phenomics
workshop picture 1 Workshop Leader
Dr. Gerald A. Higgins, Research Professor, Computational Medicine and Bioinformatics,
University of Michigan Medical School
Dr. Gerald A. Higgins is Research Professor of Computational Medicine and Bioinformatics at the University of Michigan Medical School. His background includes chief of molecular neurobiology at the NIH, VP of R&D of Laerdal Medical Corporation, CIO of MedStar Health Hospital network, several start-up companies (e.g., SimQuest, Medscape), and VP of pharmacogenomic science at Assurex (now Myriad Genetics). His research combines expertise in pharmacogenomics, psychiatry, and computational analysis to understand human brain networks involved in psychotropic drug response. His work using artificial intelligence emphasizes pharmacology expertise combined with development of UX (user interface) design to enhance understanding by experimental biologists.

 

Workshop B
Monday 26th February, 2018

13.30 - 16.00

How to Use the Wealth of Data Sets Currently Available Within the Pharma Industry to Set up an AI-driven Drug Discovery Approach?


The pharma industry is investing in AI & machine learning technologies as the drug discovery paradigm shifts towards safe, effective and value-based drug discovery. Our workshop focuses on how to leverage the large datasets currently available as well as how to successfully identify, prepare, assess and implement data into AI algorithms.

  • Data quality versus data quantity
  • How to avoid the ‘junk in, junk out’ scenario by cleaning & prioritizing data?
  • Accepting the data noise/dirt and dealing with the integrity of the output generated by AI platforms
workshop picture 22 Workshop Leader
Leonardo Rodrigues, Associate Director, Advanced Analytics,
BERG
Leonardo Rodrigues, Ph.D. is the Associate Director of Advanced Analytics at BERG. With more than 15 years of experience in data analysis and R&D, Dr. Rodrigues is a reference in applying AI and advanced analytical methods to extract actionable insights from clinical and biological data. At BERG, he leads the research and analysis of disruptive projects, as well as the development and deployment of innovative analytics technologies and IT platforms. Dr. Rodrigues holds a Ph.D. in Biochemistry and concluded his postdoctoral studies at the Whitehead Institute-MIT, where he applied statistics, bioinformatics, and molecular/cell biology in the study of cancer stem cells and metastasis initiation.