Difference between revisions of "Generative Tensorial Reinforcement Learning (GENTRL)"
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* [[Pharmaceuticals]] | * [[Pharmaceuticals]] | ||
| − | A deep [[Generative|generative]] model, [[Generative|generative]] tensorial [[Reinforcement Learning (RL)|reinforcement learning] (GENTRL). The GENTRL model is a [[Variational Autoencoder (VAE)]] with a rich prior distribution of the latent space. We used tensor decompositions to encode the relations between molecular structures and their properties and to learn on data with missing values. We train the model in two steps. First, we learn a mapping of a chemical space on the latent manifold by maximizing the evidence lower bound. We then freeze all the parameters except for the learnable prior and explore the chemical space to find molecules with a high reward. | + | A deep [[Generative|generative]] model, [[Generative|generative]] tensorial [[Reinforcement Learning (RL)|reinforcement learning]] (GENTRL). The GENTRL model is a [[Variational Autoencoder (VAE)]] with a rich prior distribution of the latent space. We used tensor decompositions to encode the relations between molecular structures and their properties and to learn on data with missing values. We train the model in two steps. First, we learn a mapping of a chemical space on the latent manifold by maximizing the evidence lower bound. We then freeze all the parameters except for the learnable prior and explore the chemical space to find molecules with a high reward. |
<img src="http://github.com/insilicomedicine/GENTRL/raw/master/images/gentrl.png" alt="GENTRL" width="800" height="800"> | <img src="http://github.com/insilicomedicine/GENTRL/raw/master/images/gentrl.png" alt="GENTRL" width="800" height="800"> | ||
Revision as of 19:49, 9 September 2019
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- Variational Autoencoder (VAE)
- Deep learning enables rapid identification of potent DDR1 kinase inhibitors | A. Zhavoronkov, Y. Ivanenkov, A. Aliper, M. Veselov, V. Aladinskiy, A. Aladinskaya, V. Terentiev, D. Polykovskiy, M. Kuznetsov, A. Asadulaev, Y. Volkov, A. Zholus, R. Shayakhmetov, A. Zhebrak, L. Minaeva, B. Zagribelnyy, L. Lee, R. Soll, D. Madge, L. Xing, T. Guo & A. Aspuru-Guzik - Nature Biotechnology
- Pharmaceuticals
A deep generative model, generative tensorial reinforcement learning (GENTRL). The GENTRL model is a Variational Autoencoder (VAE) with a rich prior distribution of the latent space. We used tensor decompositions to encode the relations between molecular structures and their properties and to learn on data with missing values. We train the model in two steps. First, we learn a mapping of a chemical space on the latent manifold by maximizing the evidence lower bound. We then freeze all the parameters except for the learnable prior and explore the chemical space to find molecules with a high reward.
