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Deep Protein-Ligand Binding Prediction Using Unsupervised Learned Representations

preprint
submitted on 06.01.2020 and posted on 08.01.2020 by Paul Kim, Robin Winter, Djork-Arné Clevert
In-silico protein-ligand binding prediction is an ongoing area of research in computational chemistry and machine learning based drug discovery, as an accurate predictive model could greatly reduce the time and resources necessary for the detection and prioritization of possible drug candidates. Proteochemometric modeling (PCM) attempts to make an accurate model of the protein-ligand interaction space by combining explicit protein and ligand descriptors. This requires the creation of information-rich, uniform and computer interpretable representations of proteins and ligands. Previous work in PCM modeling relies on pre-defined, handcrafted feature extraction methods, and many methods use protein descriptors that require alignment or are otherwise specific to a particular group of related proteins. However, recent advances in representation learning have shown that unsupervised machine learning can be used to generate embeddings which outperform complex, human-engineered representations. We apply this reasoning to propose a novel proteochemometric modeling methodology which, for the first time, uses embeddings generated via unsupervised representation learning for both the protein and ligand descriptors. We evaluate performance on various splits of a benchmark dataset, including a challenging split that tests the model’s ability to generalize to proteins for which bioactivity data is greatly limited, and we find that our method consistently outperforms state-of-the-art methods.

History

Email Address of Submitting Author

paul.kim@bayer.com

Institution

Bayer AG

Country

Germany

ORCID For Submitting Author

0000-0002-8950-7179

Declaration of Conflict of Interest

No Conflict of Interest

Version Notes

Presubmission - 0.1

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