Molecular generative model based on conditional variational autoencoder for de novo molecular design
By Jaechang Lim, Seongok Ryu, Jin Woo Kim, Woo Youn Kim. Journal of Cheminformatics, 2018
408 citations recorded by the source corpus.
We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.
Subfields: Computational Theory and Mathematics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/e4f0bf3074d5ae6d55d22068dd50158dc81b2a0a
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
- José Miguel Hernández-Lobato
- Austin Tripp
- Tomohide Masuda
- Artem Cherkasov
- David Ryan Koes
- Nathan Brown
- Zaccary Alperstein
In Molecular Biology
- José Miguel Hernández-Lobato
- Austin Tripp
- Tomohide Masuda
- Artem Cherkasov
- David Ryan Koes
- Zaccary Alperstein
- Marwin H. S. Segler
- Matthew Ragoza
Citations in the corpus
- Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining (2020, background): “Although many variants of these algorithms have been applied to real-world problems such as chemical design [22, 26, 31, 32, 4], the sample efficiency of this paradigm is currently unclear.”
- Recent advances and applications of deep learning methods in materials science (2021, neutral): “com/jaechanglim/CVAE [143] DeepChem https://github.”
- GuacaMol: Benchmarking Models for De Novo Molecular Design (2018, methodology): “Generative models based on neural networks have since been further explored, with work on VAEs, 17,36–45 RNNs, 46–58 generative adversarial networks (GANs), 54,55,59,60 adversarial au-toencoders (AAEs), 61–64 and graph convolutional networks, 16,57,60,65 using SMILES strings or graphs to represent…”
- All SMILES Variational Autoencoder (2019, background): “Alternatively, semi-supervised VAEs condition the decoder on the molecular properties [28, 41], so the desired properties can be specified directly.”
- Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models (2020, methodology): “VAEs in conjunction with SMILES were adopted to generate novel molecules from a trained continuous latent space [27, 28].”