Entangled Conditional Adversarial Autoencoder for de-novo Drug Discovery

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https://doi.org/10.1021/acs.molpharmaceut.8b00839

Modern computational approaches and machine learning techniques accelerate the invention of new drugs. Generative models can discover novel molecular structures within hours, while conventional drug discovery pipelines require months of work. In this paper, we propose a new generative architecture—Entangled Conditional Adversarial Autoencoder—that generates molecular structures based on various properties such as activity against a specific protein, solubility, or ease of synthesis. We apply the proposed model to generate a novel inhibitor of Janus Kinase 3, implicated in rheumatoid arthritis, psoriasis, and vitiligo. The discovered molecule was tested in vitro and showed high activity and selectivity.