Machine Intelligence for Quantitative Modeling in Drug Discovery & Development Applications

September 15, 2022 - September 16, 2022

Workshop

Location: Virtual

Venue: GoToWebinar

While there is significant interest from both industry & regulatory agencies in applying Artificial Intelligence (AI) and Machine Learning (ML) to quantitative modeling for drug development applications, several challenges remain: these include the generalizability, interpretability, and data requirement of such models. To help address these issues, within the IQ Consortium the AI/ML Working Group was formed with the aim to foster scientific dialogue on AI/ML applications and identify a set of good-practices, so as to enable broader impacts in drug development. With this IQ Workshop, we aim to bring together experts from industry, academia, and the FDA to initiate a scientific dialogue and collaboration across disciplines to elevate the impact of ML.

Click to view the program brochure or register now to attend!

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DAY 1

Session 1: AI/ML-enabled Analytics & Pharmacometrics Workflows 

Speakers:

• Qi Liu (FDA): Application of Machine Learning in Drug Development
• Meng Hu (FDA): Use of AI/ML technologies to enhance regulatory efficiency
• Nadia Terranova (Merck Serono): Machine Learning-empowered Fast Screening of Covariates in Population Modeling
• James Lu (Genentech): Neural-PK/PD as a Pharmacology-Informed Deep Learning Architecture
• Kamrine Poels (Pfizer): A Machine Learning Based Approach for Toxicity Predictions in Immuno-Oncology

Session 2: Explainable ML for Disease Progression/Digital Twins

• Michaela van der Schaar (University of Cambridge): TBD
  James Kozloski (IBM Research): Constructing virtual cohorts and coherent data distributions with generative adversarial networks
  Nadia Terranova (Merck Serono): Explainable Machine Learning for Disease Progression in Multiple Sclerosis patients: Application to Mavenclad trials
  James Lu (Genentech): Explainable Deep Learning for Tumor Dynamic Modeling and Overall Survival Prediction using Neural-ODE

Session 3: NLP in Quantitative Pharmacology Modeling 

• Jenny Ding (Merck): Using Deep Learning NLP Technique to Streamling Meta-Analysis
• Frank Kloprogge (University College London): An Automated Approach to Extract Pharmacokinetic Parameters from Scientific Publications
• Jinfeng Zhang (Insilicom/University of Florida): Constructing a Biomedical Knowledge Graph for All PubMed Articles and its Applications 

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DAY 2

Session 4: AI/ML Utilization in Drug Discovery

• John Sanders (Merck): In vivo QSAR ADME tools to augment drug discovery efforts
• Rich Bonneau (Prescient/Genentech): Use of AI/ML to design antibodies
• Nigel Greene (AstraZeneca): Use of AL/ML in Discovery Toxicology