Constrained Bayesian optimization for automatic chemical design using variational autoencoders
By Ryan-Rhys Griffiths, José Miguel Hernández Lobato. Chemical Science, 2019
317 citations recorded by the source corpus.
Automatic Chemical Design is a framework for generating novel molecules with optimized properties.
Subfields: Computational Theory and Mathematics, Environmental Chemistry, Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/5b59a3faf56a92b8b076a173ed23bdaac3f62f49
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Erik A. Daxberger
- Tianfan Fu
- Austin Tripp
- José Miguel Hernández-Lobato
- Shengchao Liu
- Jimeng Sun
- Yuanqi Du
- Peter I. Frazier
In Environmental Chemistry
- Erik A. Daxberger
- Austin Tripp
- José Miguel Hernández-Lobato
- Shengchao Liu
- Jimeng Sun
- Tianfan Fu
- Yuanqi Du
In Materials Chemistry
- Erik A. Daxberger
- Tianfan Fu
- Austin Tripp
- Jimeng Sun
- Shengchao Liu
- Yuanqi Du
- José Miguel Hernández-Lobato
Citations in the corpus
- MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design (2022, background): “[143] None 2D-Graph VAE GB-GA [112] None 2D-Graph GA GA+D [6] None SELFIES GA EDM [144] None SMILES GA JANUS [145] None SELFIES GA MolDQN [103] None 2D-Graph RL RationaleRL [109] None 2D-Graph RL GCPN [108] None 2D-Graph RL+GAN REINVENT [146] None SMILES RL+AR MCMG [147] None SMILES RL+AR GEGL [ [15”
- Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining (2020, methodology): “LSO using DGMs was first applied to chemical design [20], and further built upon subsequently [28, 37, 15, 31, 9, 10, 22, 43].”