a-MOP: Molecule optimization with a-divergence

Tianfan Fu, Cao Xiao, Lucas M. Glass, Jimeng Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Automatic molecule optimization aims at generating a new molecule Y with more desirable properties based on an input molecule X. There are two learning strategies for molecule optimization: 1) Maximum Likelihood Estimation (MLE) methods that take a set of molecule pairs (X, Y) as training data where X is the input molecule and Y is the enhanced molecule. However, such molecule pairs are not naturally available in molecule databases and has to be constructed using ad hoc heuristics, which limits the performance of MLE methods. 2) Reinforcement Learning (RL) methods, though bypass the need of molecule pairs as training data, suffer from poor exploration efficiency, especially in the early phase of learning. To address both challenges, we propose \alpha-Molecular oPtimization (\alpha-MOP), which uses \alpha-divergence to unify both MLE and RL objectives automatically. In early phase it focuses more on maximum likelihood objective but gradually shifts more weight onto reinforcement learning objective. Evaluated on multiple datasets, \alpha-MOP obtains success rate of 49.91% in QED, 49.32% in DRD2 and 56.43% in LogP, which outperforms both MLE and RL based molecule optimization methods.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
EditorsTaesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages240-244
Number of pages5
ISBN (Electronic)9781728162157
DOIs
StatePublished - Dec 16 2020
Event2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of
Duration: Dec 16 2020Dec 19 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Conference

Conference2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
CountryKorea, Republic of
CityVirtual, Seoul
Period12/16/2012/19/20

Keywords

  • Drug Discovery
  • Molecule Optimization

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems and Management
  • Medicine (miscellaneous)
  • Health Informatics

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