/MolBit

De novo Drug Design via Binary Representations of SMILES for avoiding the Posterior Collapse Problem (BIBM 2021)

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MolBit: De novo Drug Design via Binary Representations of SMILES for avoiding the Posterior Collapse Problem

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Many drug design studies have proposed combinations of VAEs and RNNs to generate SMILES strings.

Although those RNN-VAE models have good validity performance, they suffer from the posterior collapse problem, in which every latent vector has an identical molecular property distribution.

We proposed a Gumbel-Softmax-based generative model called MolBit to avoid the posterior collapse problem.

Paper

https://doi.org/10.1109/BIBM52615.2021.9669668