String-based Molecule Generation via Multi-decoder VAE

• URL: http://arxiv.org/abs/2208.10718v1
• Date: Tue, 23 Aug 2022 03:56:30 GMT
• Title: String-based Molecule Generation via Multi-decoder VAE
• Authors: Kisoo Kwon, Kuhwan Jung, Junghyun Park, Hwidong Na and Jinwoo Shin
• Abstract summary: We investigate the problem of string-based molecular generation via variational autoencoders (VAEs) We propose a simple, yet effective idea to improve the performance of VAE for the task. In our experiments, the proposed VAE model particularly performs well for generating a sample from out-of-domain distribution.
• Score: 56.465033997245776
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we investigate the problem of string-based molecular generation via variational autoencoders (VAEs) that have served a popular generative approach for various tasks in artificial intelligence. We propose a simple, yet effective idea to improve the performance of VAE for the task. Our main idea is to maintain multiple decoders while sharing a single encoder, i.e., it is a type of ensemble techniques. Here, we first found that training each decoder independently may not be effective as the bias of the ensemble decoder increases severely under its auto-regressive inference. To maintain both small bias and variance of the ensemble model, our proposed technique is two-fold: (a) a different latent variable is sampled for each decoder (from estimated mean and variance offered by the shared encoder) to encourage diverse characteristics of decoders and (b) a collaborative loss is used during training to control the aggregated quality of decoders using different latent variables. In our experiments, the proposed VAE model particularly performs well for generating a sample from out-of-domain distribution.

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