Cambridge Healthtech Institute’s 9th Annual
AI/ML for Early Drug Discovery – Part 1
AI-Driven Design and Optimization of Small Molecule, Peptide, and Antibody-Based Drugs
April 20-21, 2027
Artificial Intelligence (AI) and Machine Learning (ML) are rapidly becoming an integral part of drug discovery, and regulatory agencies are increasingly recognizing their role in drug development and submissions. This two-part conference on AI/ML for Early Drug Discovery examines practical applications of AI/ML, emerging computational technologies, and the data needed to make them work effectively. Part One explores how AI/ML is accelerating drug design, virtual screening, and hit identification and lead optimization across a growing range of therapeutic modalities. Part Two looks at using generative and agentic AI, foundation models, multimodal data, and advanced computational approaches to identify and validate new targets, unravel complex cellular biology, explore vast chemical space, and find new ways to tackle challenging and previously “undruggable” targets. Through real-world applications and candid discussions of both successes and limitations, attendees will gain a clearer understanding of which AI/ML approaches are working today, how they are changing discovery workflows, and where the technology is headed next.
Coverage will likely include:
- AI-Driven molecular modeling and virtual screening for drug design, reaction kinetics, and specificity
- AI- and ML-enabled hit identification, drug candidate prioritization, and lead optimization
- Improving compound potency and safety based on AI/ML-based drug property predictions
- Using machine learning to deconvolute cellular interactions and identify novel drug targets
- AI-Enhanced virtual, direct-to-biology phenotypic screening
- Case studies highlighting the use of AI/ML in therapeutic areas like oncology, obesity, CNS
- De novo peptide and antibody design using deep learning approaches
- AI models for predicting protein structure, binding, and interactions in specific drug target classes
- Understanding limitations and caveats when using and integrating AI/ML predictions
The deadline for priority consideration is October 5, 2026.
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.
Opportunities for Participation:
- To submit a podium presentation, please click here.
- To become a sponsor, click here.
- To present a poster, click here.