Active Retrosynthetic Planning Aware of Route Quality

Luotian Yuan, Yemin Yu, Ying Wei, Yongwei Wang, Zhihua Wang, Fei Wu

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Keywords: Retrosynthetic planning, route evaluation, reinforcement learning

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.

Abstract:

Retrosynthetic planning is a sequential decision-making process of identifying synthetic routes from the available building block materials to reach a desired target molecule.
Though existing planning approaches show promisingly high solving rates and low costs, the trivial route cost evaluation via pre-trained forward reaction prediction models certainly falls short of real-world chemical practice.
An alternative option is to annotate the actual cost of a route, such as yield, through chemical experiments or input from chemists, while
this often leads to substantial query costs.
In order to strike the balance between query costs and route quality evaluation, we propose an Active Retrosynthetic Planning (ARP) framework that remains compatible with the established retrosynthetic planners.
On one hand, the proposed ARP trains an actor that decides whether to query the cost of a reaction; on the other hand, it resorts to a critic to estimate the value of a molecule with its preceding reaction cost as input.
Those molecules with low reaction costs are preferred to expand first.
We apply our framework to different existing approaches on both the benchmark and an expert dataset and demonstrate that it outperforms the existing state-of-the-art approach by 6.2\% in route quality while reducing the query cost by 12.8\%.
In addition,
ARP consistently plans
high-quality routes with either abundant or sparse annotations.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 7875