Molecular design using graph Bayesian optimization with shortest-path kernels

Yilin Xie, Shiqiang Zhang, Jixiang Qing, Ruth Misener, Calvin Tsay

Track: Machine learning: computational method and/or computational results

Nature Biotechnology: Yes

Keywords: Molecular design, graph Bayesian optimization, shortest-path kernel

TL;DR: This paper proposes a graph Bayesian optimization using shortest path kernels for optimal molecule generation.

Abstract: Past decades have seen the great potential of generative machine learning in molecular design while also exposed the gap between research contributions and practical applications. Considering the expensive-to-evaluate nature of molecular properties and hard-to-satisfy validity of molecular structures, this paper tackles molecular design from graph Bayesian optimization viewpoint, i.e., generating feasible and optimal molecular graph with compatible features given limited number of evaluations. Our proposed method, BoGrape, uses shortest-path graph kernels to measure the similarity between molecules and utilizes mixed-integer programming to allow global exploration of molecular space while maintaining the feasibility of generated candidates. Preliminary results show promising performance of BoGrape.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Presenter: ~Yilin_Xie1

Format: Yes, the presenting author will attend in person if this work is accepted to the workshop.

Funding: No, the presenting author of this submission does *not* fall under ICLR’s funding aims, or has sufficient alternate funding.

Submission Number: 71