Hanson Wade Group have taken the decision to cancel this meeting. Please do accept our apologies for any inconvenience or
disappointment this will cause

Pre-Conference Workshop Day

9:00 am Registration & Morning Coffee

10:00 am 10:00 – 12:00
Workshop A

10:30 am Propel a Digital Culture Shift by Devising Strategies to Better Obtain Data & Augment Its Quality


AI algorithms, particularly deep learning models, require large and high-quality datasets to make accurate predictions and generate meaningful insights. In drug discovery and preclinical development, obtaining comprehensive and well-annotated datasets can be challenging due to the complexity and diversity of biological systems. Moreover, experimental data can be expensive and time-consuming to generate, leading to limited data availability, especially for rare diseases or specific biological targets.

This workshop will gather experts to discuss

  • Leveraging external data to accelerate sample gathering and attain a better understanding of disease pathogenesis
  • How do we store huge volumes of data while minimizing costs?
  • Increasing open source to improve the quality of data by allowing AI to keep up with the complexity of the biological system
  • Building comparable data sets by developing highly robotic and automized procedures to enable all data sets to be generated in the same way
  • Standardising the approach towards data acquisition and sourcing to enhance data integrity

12:00 pm Lunch Break & Networking

1:00 pm 1:00 – 3:00
Workshop B

1:30 pm Overcoming Ambiguity: Delve Into the Ignition of the Explainable AI Field to Develop Human-Understandable Systems & Enhance Interpretability


AI models used in drug discovery often operate as black boxes, making it difficult for researchers to understand how they arrive at specific predictions or recommendations. Interpretability and explainability are crucial, especially in drug development, as stakeholders need to understand the underlying reasoning behind AI-driven decisions to trust the models’ predictions and make informed decisions about candidate compounds. Developing AI models that are both accurate and interpretable remains a significant challenge.

This workshop will gather experts to discuss

  • Accelerating towards the development of AI models with explainable mechanisms to augment their transparency in early drug discovery research and enable researchers to make informed decisions 
  • Delving into AI-DDIs studies, methods used in data manipulation and feature pre-processing