February 26-28, 2018

San Francisco, USA

Day One
Tuesday 27th February, 2018

Day Two
Wednesday 28th February, 2018

08.00
Breakfast & Networking

08.50
Chair’s Opening Remarks

Enhancing Drug Discovery Productivity Using AI & Machine Learning Technologies

09.00
Case Study: Massive Scale Machine Learning for Integrative Compound Activity Prediction

  • Hugo Ceulemans Scientific Director Discovery-Data Sciences, Johnson & Johnson

Synopsis

  • Multitask learning at industrial scale boosts predictive performance
  • Enlisting alternative data sources like cellular images and transcriptional profiles for compound boosts predictive performance
  • Towards collaborative learning from private data

09.30
Case Study: Pure AI Approach Versus Augmented Intelligence Approach: Augmentation of Data Using AI to Predict Missing Data to Consequently Build Disease Models

Synopsis

  • How to optimize the decision making process?
  • Is cognitive technology they way forward?
  • How to prepare for augmented intelligence as an emerging technology?

10.00
Case Study:The In Silico Biosciences Quantitative Systems Pharmacology (QSP) Platform as the Next Step in the Big Data AI Value Chain in CNS

Synopsis

  • Bridging the gap between big data AI Targets and clinical phenotypes with QSP Augmented intelligence technology
  • Optimizing in silico biomarkers as a quantitative target discovery and target validation engine, especially for multi-target drugs
  • Using the platform downstream in compound and clinical development

10.20
Case Study: Discovery of New Chemical Entities Using Network-driven Drug Discovery Augmented by AI

  • Ben Allen Senior Computational Research Scientist, e-Therapeutics

Synopsis

  • What is network driven drug discovery?
  • How does AI/machine learning contribute to this approach?
  • Validation case studies

Practical Applications of AI & Machine Learning in Drug Discovery

10.50
Case Study: Accutar Biotech Employs Artificial Intelligence to Revolutionize Drug Discovery

  • Jie Fan CEO, Accutar Biotechnology

Synopsis

What is our philosophy?

  • To derive a data-driven principle that has the power of explaining physical and
    chemical nature of biological systems, which we harness to accelerate drug discoveries
    What have we done?
  • A data-driven atom-based scoring function is learned from 100,000 protein crystal
    structures containing information of >100 million amino acid side chains
  • A dynamic deep neural network specifically designed for chemical informatics

What have we done?

  • A data-driven atom-based scoring function is learned from 100,000 protein crystal structures containing information of >100 million amino acid side chains
  • A dynamic deep neural network specifically designed for chemical informatics

Why is our work impactful?

  • Beating current gold standard in computation-aided drug discovery
  • Drug pocket side chain conformation prediction and drug docking with significantly
    increased accuracy compared to standard method (Schrodinger)
  • Prediction of chemical compound characteristics (solubility, binding affinity, etc)
    significantly better than current standard

11.20
Morning Refreshments & Networking

11.50
Case Study: Harnessing the Power of AI to Improve Translation: From Biology to Discovery and Discovery to Development

Synopsis

  • Novel molecule modulation for identified targets
  • Optimizing molecule properties/profiles to enhance productivity
  • PK/PD modelling & simulation
  • Evaluating compound’s ADMET characteristics

12.20
Case Study: Multi-dimensional Drug Discovery with AI-enabled Phenomics

  • Ron Alfa VP, Discovery & Product, Recursion Pharmaceuticals

Synopsis

  • Combining AI methods with high throughput phenomics enables drug discovery at a radical pace
  • Cellular images contains a wealth of biological information that can be used for drug discovery campaigns and hit prioritization
  • Recursion is systematically mapping human biology through images

Current Focus of AI & Machine Learning Technologies in Drug Discovery

12.50
Case Study: The Role of Structural Analysis in the Improvement of AI Methods Used in Drug Design

Synopsis

  • Current status and challenges for the application of AI in drug discovery
  • Describing the methods based on structural analysis for the generation of protein fingerprints and how that contributes to small molecule drug discovery
  • Challenges for the application of AI methods in the development of therapeutic antibodies

13.20
Networking Lunch

14.20
Case Study: Accurate Data and Domain Expertise: Key Ingredients of AI-driven Target and Drug Discovery

  • Tudor Oprea Professor-Medicine & Chief of Translational Informatics & Internal Medicine, University of New Mexico

Synopsis

  • Illuminating the Druggable Genome is an NIH project focused on accurate data wrangling, processing and analytics for target discovery
  • DrugCentral, an on-line compendium for drugs and drug targets, can serve as basis for understanding the molecular basis of therapeutic action
  • Drug-, protein- and disease- knowledge graphs can be used to enable AI systems in drug discovery

What the Future of AI & Machine Learning in Drug Discovery Holds: The Emerging Trends

14.50
Case Study: Machine Learning for Safety Risk Models in Development

  • Gregory Bell VP, Global Therapeutic Area Head, Immunology, Infectious Disease and Ophthalmology, PD Safety Science, Genentech

Synopsis

  • Leveraging what we know about the drug & the available data
  • The use of pharmacovigilance data in AI-driven drug repurposing approaches
  • Developing risk models with or without machine learning: Almost no one does this explicitly

15.20
Afternoon Refreshments & Networking

15.50
Case Study: The Application of AI & Machine Learning in Multi-target Drug Discovery: Polypharmacology

Synopsis

  • In silico approaches for polypharmacology and its application
  • Challenges of integrating big data in biology
  • Application of knowledge and algorithms for drug discovery

16.20
Case Study: Deep Learning in Pharmacogenomics: From Drug Discovery to Patient Stratification

  • Gerald A. Higgins Research Professor, Computational Medicine and Bioinformatics, University of Michigan Medical School

Synopsis

  • Promising applications of pharmacogenomics drug discovery and development as well as medication optimization
  • Identification of regulatory pharmacogenomic variants and drug target discovery using deep learning
  • The predictive power of machine learning is realized when it is combined with prior domain knowledge, such as gene networks and pharmacodynamic pathways

16.50
Chair’s Closing Remarks

17.00
Close of Day Two & End of AI Pharma Innovation: Drug Discovery Summit 2018