Towards Interpretable Models of Chemist Preferences for Human-in-the-Loop Assisted Drug Discovery
Paper in proceeding, 2025

In recent years, there has been growing interest in leveraging human preferences for drug discovery to build models that capture chemists’ intuition for de novo molecular design, lead optimization, and prioritization for experimental validation. However, existing models derived from human preferences in chemistry are often black-boxes, lacking interpretability regarding how humans form their preferences. Enhancing transparency in human-in-the-loop learning is crucial to ensure that such approaches in drug discovery are not unduly affected by subjective bias, noise or inconsistency. Moreover, interpretability can promote the development and use of multi-user models in drug design projects, integrating multiple expert perspectives and insights into multi-objective optimization frameworks for de novo molecular design. This also allows for assigning more or less weight to experts based on their knowledge of specific properties. In this paper, we present a methodology for decomposing human preferences based on binary responses (like/dislike) to molecules essentially proposed by generative chemistry models, and inferring interpretable preference models that represent human reasoning. Our approach aims to bridge the gap between human-in-the-loop learning and user model interpretability in drug discovery applications, providing a transparent framework that elucidates how human judgments can shape molecular design outcomes.

Interpretability

Human-in-the-loop machine learning

Feature decomposition

De novo molecular design

User modelling

Author

Yasmine Nahal

Aalto University

Markus Heinonen

Aalto University

Mikhail Kabeshov

AstraZeneca AB

Jon Paul Janet

AstraZeneca AB

Eva Nittinger

AstraZeneca AB

Ola Engkvist

AstraZeneca AB

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Samuel Kaski

University of Manchester

Aalto University

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 14894 LNCS 58-70
9783031723803 (ISBN)

1st International Workshop on AI in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024
Lugano, Switzerland,

Subject Categories

Bioinformatics (Computational Biology)

DOI

10.1007/978-3-031-72381-0_6

More information

Latest update

10/11/2024