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DE NOVO DRUG DESIGN WITH DEEP GENERATIVE MODELS : AN EMPIRICAL STUDY

Abstract : We present an empirical study about the usage of RNN generative models for stochastic optimization in the context of de novo drug design. We study different kinds of architectures and we find models that can generate molecules with higher values than ones seen in the training set. Our results suggest that we can improve traditional stochastic optimizers, that rely on random perturbations or random sampling by using generative models trained on unlabeled data, to perform knowledge-driven optimization.
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https://hal.archives-ouvertes.fr/hal-01773760
Contributor : Akin Osman Kazakci <>
Submitted on : Monday, April 23, 2018 - 10:23:01 AM
Last modification on : Thursday, April 9, 2020 - 5:08:14 PM
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  • HAL Id : hal-01773760, version 1

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Mehdi Cherti, Balázs Kégl, Akın Kazakçı. DE NOVO DRUG DESIGN WITH DEEP GENERATIVE MODELS : AN EMPIRICAL STUDY. International Conference on Learning Representations, Apr 2017, Toulon, France. ⟨hal-01773760⟩

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