Human-in-the-loop assisted de novo molecular design
Journal article, 2022

A de novo molecular design workflow can be used together with technologies such as reinforcement learning to navigate the chemical space. A bottleneck in the workflow that remains to be solved is how to integrate human feedback in the exploration of the chemical space to optimize molecules. A human drug designer still needs to design the goal, expressed as a scoring function for the molecules that captures the designer’s implicit knowledge about the optimization task. Little support for this task exists and, consequently, a chemist usually resorts to iteratively building the objective function of multi-parameter optimization (MPO) in de novo design. We propose a principled approach to use human-in-the-loop machine learning to help the chemist to adapt the MPO scoring function to better match their goal. An advantage is that the method can learn the scoring function directly from the user’s feedback while they browse the output of the molecule generator, instead of the current manual tuning of the scoring function with trial and error. The proposed method uses a probabilistic model that captures the user’s idea and uncertainty about the scoring function, and it uses active learning to interact with the user. We present two case studies for this: In the first use-case, the parameters of an MPO are learned, and in the second use-case a non-parametric component of the scoring function to capture human domain knowledge is developed. The results show the effectiveness of the methods in two simulated example cases with an oracle, achieving significant improvement in less than 200 feedback queries, for the goals of a high QED score and identifying potent molecules for the DRD2 receptor, respectively. We further demonstrate the performance gains with a medicinal chemist interacting with the system. Graphical Abstract: [Figure not available: see fulltext.].

Expert knowledge elicitation

Goal-oriented molecule generation

AI-assisted design

Human-in-the-loop

Interactive algorithms

De novo molecular design

Reward elicitation

Author

Iiris Sundin

Aalto University

Alexey Voronov

AstraZeneca AB

Haoping Xiao

Aalto University

Kostas Papadopoulos

Odyssey Therapeutics

AstraZeneca AB

Esben Jannik Bjerrum

AstraZeneca AB

Odyssey Therapeutics

Markus Heinonen

Aalto University

Atanas Patronov

AstraZeneca AB

Odyssey Therapeutics

Samuel Kaski

Aalto University

University of Manchester

Ola Engkvist

Chalmers, Computer Science and Engineering (Chalmers)

AstraZeneca AB

Journal of Cheminformatics

1758-2946 (ISSN) 17582946 (eISSN)

Vol. 14 1 86

Subject Categories

Interaction Technologies

Human Computer Interaction

Computer Science

DOI

10.1186/s13321-022-00667-8

PubMed

36578043

More information

Latest update

10/26/2023