Human-in-the-loop assisted de novo molecular design

Loading...
Thumbnail Image
Access rights
openAccess
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2022-12
Major/Subject
Mcode
Degree programme
Language
en
Pages
16
1-16
Series
JOURNAL OF CHEMINFORMATICS, Volume 14, issue 1
Abstract
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.].
Description
Funding Information: This work was supported by AstraZeneca, the Academy of Finland (Flagship programme: Finnish Center for Artificial Intelligence FCAI, and grant number 341763) and UKRI Turing AI World-Leading Researcher Fellowship, EP/W002973/1. Funding Information: We thank Pierre-Alexandre Murena and Sebastiaan De Peuter for comments and inspiring discussions. We acknowledge the computational resources provided by the Aalto Science-IT Project from Computer Science IT. Publisher Copyright: © 2022, The Author(s).
Keywords
AI-assisted design, De novo molecular design, Expert knowledge elicitation, Goal-oriented molecule generation, Human-in-the-loop, Interactive algorithms, Reward elicitation
Other note
Citation
Sundin, I, Voronov, A, Xiao, H, Papadopoulos, K, Bjerrum, E J, Heinonen, M, Patronov, A, Kaski, S & Engkvist, O 2022, ' Human-in-the-loop assisted de novo molecular design ', JOURNAL OF CHEMINFORMATICS, vol. 14, no. 1, 86, pp. 1-16 . https://doi.org/10.1186/s13321-022-00667-8