@inproceedings{
calvi2023leap,
title={Leap: molecular synthesisability scoring with intermediates},
author={Antonia Calvi and Th{\'e}ophile Gaudin and Dominik Miketa and Dominique Sydow and Liam Wilbraham},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=TdV9PaTvCS}
}
Assessing whether a molecule can be synthesised is a primary task in drug discovery. It enables computational chemists to filter for viable compounds
or bias molecular generative models. The notion of synthesisability is dynamic as it evolves depending on the availability of key compounds. A common approach in drug discovery involves exploring the chemical space surrounding synthetically-accessible
intermediates. This strategy improves the synthesisability of the derived molecules due to the availability of key intermediates.
Existing synthesisability scoring methods such as SAScore, SCScore and RAScore, cannot condition on intermediates dynamically.
Our approach, Leap, is a GPT-2 model trained on the depth, or longest linear path, of predicted synthesis routes that allows information on the availability of key intermediates to be included at inference time.
We show that Leap surpasses all other scoring methods by at least 5% on AUC score when identifying synthesisable molecules, and can successfully adapt predicted scores when presented with a relevant intermediate compound.
Leap: molecular synthesisability scoring with intermediates
Antonia Calvi Théophile Gaudin Dominik Miketa Dominique Sydow Liam Wilbraham