Generative Tensorial Reinforcement Learning (GENTRL)

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• 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
  • GENTRL | GitHub
  • Supplementary Code
  • AI in Medicine: Life Sciences and Drug Discovery - Transcript | Alex Zhavoronkov & Michael Krigsman
  • How an AI Startup Designed a Drug Candidate in Just 46 Days | Edd Gent - SingularityHub
• Variational Autoencoder (VAE)
• Generative Adversarial Network (GAN)
• Pharmaceuticals
• Generative AI ... OpenAI's ChatGPT ... Perplexity ... Microsoft's BingAI ... You ...Google's Bard ... Baidu's Ernie

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.