2 research outputs found
Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation
Molecule generation is to design new molecules with specific chemical
properties and further to optimize the desired chemical properties. Following
previous work, we encode molecules into continuous vectors in the latent space
and then decode the vectors into molecules under the variational autoencoder
(VAE) framework. We investigate the posterior collapse problem of current
RNN-based VAEs for molecule sequence generation. For the first time, we find
that underestimated reconstruction loss leads to posterior collapse, and
provide both theoretical and experimental evidence. We propose an effective and
efficient solution to fix the problem and avoid posterior collapse. Without
bells and whistles, our method achieves SOTA reconstruction accuracy and
competitive validity on the ZINC 250K dataset. When generating 10,000 unique
valid SMILES from random prior sampling, it costs JT-VAE1450s while our method
only needs 9s. Our implementation is at
https://github.com/chaoyan1037/Re-balanced-VAE.Comment: 8 page
Deep Evolutionary Learning for Molecular Design
In this paper, we propose a deep evolutionary learning (DEL) process that
integrates fragment-based deep generative model and multi-objective
evolutionary computation for molecular design. Our approach enables (1)
evolutionary operations in the latent space of the generative model, rather
than the structural space, to generate novel promising molecular structures for
the next evolutionary generation, and (2) generative model fine-tuning using
newly generated high-quality samples. Thus, DEL implements a data-model
co-evolution concept which improves both sample population and generative model
learning. Experiments on two public datasets indicate that sample population
obtained by DEL exhibits improved property distributions, and dominates samples
generated by multi-objective Bayesian optimization algorithms