Cambridge Healthtech Institute’s 6th Annual

AI/Machine Learning for Early Drug Discovery – Part 2

Generative AI & Predictive Algorithms for Small Molecule & Peptide Therapeutics

April 3-4, 2024

Artificial Intelligence (AI)/Machine Learning (ML) for Early Drug Discovery is a two-part conference that brings together a diverse group of experts from chemistry, target discovery, pharmacology and bioinformatics, to talk about the increasing use of computational tools, models, algorithms and data analytics for drug development. The talks will highlight the pros and cons of AI/ML-driven decision-making using relevant case studies from small molecule and peptide drug development. The second part of this conference will focus on emerging computational tools and models to identify new drug targets, predict PK/PD and safety issues in drug candidates, and to drive niche applications in drug discovery.

Coverage will likely include:

Using machine learning to understand cellular interactions and identify new drug targets
Applications of AI/ML in emerging areas like protein degradation, pursuing undruggable targets
Protein design and peptide discovery using deep learning approaches
Improving accuracy of AI predictions for PKPD properties and drug-related adverse events
Understanding limitations and caveats when using AI/ML predictions

The deadline for priority consideration is September 15, 2023.

All proposals are subject to review by session chairpersons and/or the Scientific Advisory Committee to ensure the overall quality of the conference program. Additionally, as per Cambridge Healthtech Institute’s policy, a select number of vendors and consultants who provide products and services will be offered opportunities for podium presentation slots based on a variety of Corporate Sponsorships.

We hope you can join us for the 5 concurrent day-and-a-half conferences during each half of our 19th Annual Drug Discovery Chemistry event. Registering for this conference lets you also move back and forth among parallel programs to create your own agenda.

Opportunities for Participation: