Automating Genetic Algorithm Mutations for Molecules Using a Masked Language Model

Authors:
Publisher website
Google Scholar
Abstract
Inspired by the evolution of biological systems, genetic algorithms have been applied to generate solutions for optimization problems in a variety of scientific and engineering disciplines. For a given problem, a suitable genome representation must be defined along with a mutation operator to generate subsequent generations. Unlike natural systems, which display a variety of complex rearrangements (e.g., mobile genetic elements), mutation for genetic algorithms commonly utilizes only random pointwise changes. Furthermore, generalizing beyond pointwise mutations poses a key difficulty as useful genome rearrangements depend on the representation and problem domain. To move beyond the limitations of manually defined pointwise changes, here we propose the use of techniques from masked language models to automatically generate mutations. As a first step, common subsequences within a given population are used to generate a vocabulary. The vocabulary is then used to tokenize each genome. A masked language model is trained on the tokenized data in order to generate possible rearrangements (i.e., mutations). In order to illustrate the proposed strategy, we use string representations of molecules and use a genetic algorithm to optimize for drug-likeness and synthesizability. Our results show that moving beyond random pointwise mutations accelerates genetic algorithm optimization.
Funding Information
  • Exascale Computing Project (17-SC-20-SC)
  • a joint project of the U.S. Department of Energy’s Office of Science and National Nuclear Security Administration
  • Joint Design of Advanced Computing Solutions for Cancer (JDACS4C)
  • Program established
  • DOE and the NCI of the National Institutes of Health
  • DOE CARES emergency funding to the National Center for Computational Sciences at ORNL through the Advanced Scientific Computing Research (ASCR) Program

This publication has 35 references indexed in Scilit: