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Genetic Algorithm for Constrained Molecular Inverse Design

arXiv 2112.03518 ↗First posted 7 Dec 2021cs.NE cs.AI cs.LGcited by 0

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Abstract (arXiv)

A genetic algorithm is suitable for exploring large search spaces as it finds an approximate solution. Because of this advantage, genetic algorithm is effective in exploring vast and unknown space such as molecular search space. Though the algorithm is suitable for searching vast chemical space, it is difficult to optimize pharmacological properties while maintaining molecular substructure. To solve this issue, we introduce a genetic algorithm featuring a constrained molecular inverse design. The proposed algorithm successfully produces valid molecules for crossover and mutation. Furthermore, it optimizes specific properties while adhering to structural constraints using a two-phase optimization. Experiments prove that our algorithm effectively finds molecules that satisfy specific properties while maintaining structural constraints.

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References · 13

  1. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  2. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 762
  3. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  4. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 647
  5. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 373
  6. 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 363
  7. 2018Learning Multimodal Graph-to-Graph Translation for Molecular Optimizationcited by 87
  8. 2019Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Spacecited by 66
  9. 2020Guiding Deep Molecular Optimization with Genetic Explorationcited by 51
  10. 2018PotentialNet for Molecular Property Predictioncited by 46
  11. 2021JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Designcited by 31
  12. 2018DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generationcited by 24
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  1. 2020Scaffold-constrained molecular generationcited by 18

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