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Molecular generative model based on conditional variational autoencoder for de novo molecular design
We propose a molecular generative model based on the conditional variati...
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MoFlow: An Invertible Flow Model for Generating Molecular Graphs
Generating molecular graphs with desired chemical properties driven by d...
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Multi-Objective De Novo Drug Design with Conditional Graph Generative Model
Recently, deep generative models have revealed itself as a promising way...
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Molecular De Novo Design through Deep Reinforcement Learning
This work introduces a method to tune a sequence-based generative model ...
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Generative Models for Automatic Chemical Design
Materials discovery is decisive for tackling urgent challenges related t...
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GuacaMol: Benchmarking Models for De Novo Molecular Design
De novo design seeks to generate molecules with required property profil...
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SketchBio: A Scientist's 3D Interface for Molecular Modeling and Animation
Background: Because of the difficulties involved in learning and using 3...
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Improving Molecular Design by Stochastic Iterative Target Augmentation
Generative models in molecular design tend to be richly parameterized, data-hungry neural models, as they must create complex structured objects as outputs. Estimating such models from data may be challenging due to the lack of sufficient training data. In this paper, we propose a surprisingly effective self-training approach for iteratively creating additional molecular targets. We first pre-train the generative model together with a simple property predictor. The property predictor is then used as a likelihood model for filtering candidate structures from the generative model. Additional targets are iteratively produced and used in the course of stochastic EM iterations to maximize the log-likelihood that the candidate structures are accepted. A simple rejection (re-weighting) sampler suffices to draw posterior samples since the generative model is already reasonable after pre-training. We demonstrate significant gains over strong baselines for both unconditional and conditional molecular design. In particular, our approach outperforms the previous state-of-the-art in conditional molecular design by over 10 absolute gain.
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