2k citations
7 papers
Directed Graph Embeddings in Pseudo-Riemannian Manifolds
Aaron Sim, Maciej Wiatrak, Angus Brayne +2
The inductive biases of graph representation learning algorithms are often encoded in the background geometry of their embedding space. In this paper, we show that general directed…
Molecular representation learning with language models and domain-relevant auxiliary tasks
Benedek Fabian, Thomas Edlich, Héléna Gaspar +4
We apply a Transformer architecture, specifically BERT, to learn flexible and high quality molecular representations for drug discovery problems. We study the impact of using diffe…
Learning Informative Representations of Biomedical Relations with Latent Variable Models
Harshil Shah, Julien Fauqueur
Extracting biomedical relations from large corpora of scientific documents is a challenging natural language processing task. Existing approaches usually focus on identifying a rel…
A Hierarchical Transformer for Unsupervised Parsing
Ashok Thillaisundaram
The underlying structure of natural language is hierarchical; words combine into phrases, which in turn form clauses. An awareness of this hierarchical structure can aid machine le…
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
Rim Assouel, Mohamed Ahmed, Marwin H Segler +2
Generating novel molecules with optimal properties is a crucial step in many industries such as drug discovery. Recently, deep generative models have shown a promising way of perfo…
GuacaMol: Benchmarking Models for De Novo Molecular Design
Nathan Brown, Marco Fiscato, Marwin H. S. Segler +1
De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles. With the emergence of deep learning and neural generative models in m…