36 research outputs found
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
We view molecular optimization as a graph-to-graph translation problem. The
goal is to learn to map from one molecular graph to another with better
properties based on an available corpus of paired molecules. Since molecules
can be optimized in different ways, there are multiple viable translations for
each input graph. A key challenge is therefore to model diverse translation
outputs. Our primary contributions include a junction tree encoder-decoder for
learning diverse graph translations along with a novel adversarial training
method for aligning distributions of molecules. Diverse output distributions in
our model are explicitly realized by low-dimensional latent vectors that
modulate the translation process. We evaluate our model on multiple molecular
optimization tasks and show that our model outperforms previous
state-of-the-art baselines
Junction Tree Variational Autoencoder for Molecular Graph Generation
We seek to automate the design of molecules based on specific chemical
properties. In computational terms, this task involves continuous embedding and
generation of molecular graphs. Our primary contribution is the direct
realization of molecular graphs, a task previously approached by generating
linear SMILES strings instead of graphs. Our junction tree variational
autoencoder generates molecular graphs in two phases, by first generating a
tree-structured scaffold over chemical substructures, and then combining them
into a molecule with a graph message passing network. This approach allows us
to incrementally expand molecules while maintaining chemical validity at every
step. We evaluate our model on multiple tasks ranging from molecular generation
to optimization. Across these tasks, our model outperforms previous
state-of-the-art baselines by a significant margin
Analyzing Learned Molecular Representations for Property Prediction
Advancements in neural machinery have led to a wide range of algorithmic
solutions for molecular property prediction. Two classes of models in
particular have yielded promising results: neural networks applied to computed
molecular fingerprints or expert-crafted descriptors, and graph convolutional
neural networks that construct a learned molecular representation by operating
on the graph structure of the molecule. However, recent literature has yet to
clearly determine which of these two methods is superior when generalizing to
new chemical space. Furthermore, prior research has rarely examined these new
models in industry research settings in comparison to existing employed models.
In this paper, we benchmark models extensively on 19 public and 16 proprietary
industrial datasets spanning a wide variety of chemical endpoints. In addition,
we introduce a graph convolutional model that consistently matches or
outperforms models using fixed molecular descriptors as well as previous graph
neural architectures on both public and proprietary datasets. Our empirical
findings indicate that while approaches based on these representations have yet
to reach the level of experimental reproducibility, our proposed model
nevertheless offers significant improvements over models currently used in
industrial workflows