Molecular graph convolutions: moving beyond fingerprints
Abstrak
Molecular “fingerprints” encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph—atoms, bonds, distances, etc.—which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.
Topik & Kata Kunci
Penulis (5)
S. Kearnes
Kevin McCloskey
Marc Berndl
V. Pande
Patrick F. Riley
Akses Cepat
- Tahun Terbit
- 2016
- Bahasa
- en
- Total Sitasi
- 1574×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1007/s10822-016-9938-8
- Akses
- Open Access ✓