Abstract

This project will explore the way that scientists make decisions at key points during the Antibody drug discovery process. Program teams generate many different types of data during the drug discovery process: structure/amino acid sequence of the molecule, affinity for target, functional potency, mode of binding and numerous parameters describing the stability and manufacturability of the molecule. Each team needs to make choices about which molecules to progress on the basis of their experience of which attributes predict clinical success. Whilst experienced scientists make these decisions they are likely to be subject to a range of cognitive biases and challenged by the need to weight parameters appropriately and take account of the characteristic variance of each data type.

The goal of the project would be to combine best practise in human decision-making heuristics with any notable new findings in an accessible tool that applies this standard consistently whilst providing teams with powerful visualisations describing the data supporting these decisions and recording the criteria and evidence for them.