Computational Modeling of β-Secretase 1 (BACE-1) Inhibitors Using Ligand Based Approaches
By G. Subramanian, Bharath Ramsundar, Vijay S. Pande, R. Denny. 2016
303 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Molecular Biology, Computational Chemistry & Drug Discovery
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Jure Leskovec
- Chaopeng Shen
- Marinka Zitnik
- Bowen Liu
- Weihua Hu
- Simon Axelrod
- Junzhou Huang
- Insung S. Choi
In Molecular Biology
- Jure Leskovec
- Marinka Zitnik
- Chaopeng Shen
- Weihua Hu
- Junzhou Huang
- Bowen Liu
- Casey S. Greene
- Yu Rong
In Computational Chemistry & Drug Discovery
- Chaopeng Shen
- Jure Leskovec
- Insung S. Choi
- Hyeoncheol Cho
- Junzhou Huang
- Bowen Liu
- Joseph Gomes
- Marinka Zitnik
Citations in the corpus
- Opportunities and obstacles for deep learning in biology and medicine (2017, background): “A study of BACE-1 inhibitors included only 1547 compounds [304].”
- Enhanced Deep‐Learning Prediction of Molecular Properties via Augmentation of Bond Topology (2018, methodology): “Therefore, we chose datasets from the protein-ligand binding category for investigating whether our model could recognize the specific structures in a local field: BACE and HIV datasets.(44,45) These datasets contain the biochemical activities of small molecules for β-secretase 1 and human immunodef”
- Pre-training Graph Neural Networks (2019, background): “Qualitative binding results for a set of inhibitors of human β -secretase 1 [48].”
- SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction (2019, background): “The growth of deep learning has provided excellent flexibility and performance to learn molecular fingerprints from data samples, without explicit guides from experts [2, 8, 14, 29, 32, 39].”
- GEOM: Energy-annotated molecular conformations for property prediction and molecular generation (2020, background): “15% (45,712) of the molecules have specified stereochemistry, while 27% (83,326) have stereocenters but may or may not have specified stereochemistry.”
- Geometry-enhanced molecular representation learning for property prediction (2021, methodology): “We conduct experiments on multiple molecular benchmarks from 224 the MoleculeNet [53], including both classification and regression tasks [47, 33, 38, 16, 25, 28].”
- A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, background): “Moreover, the community has assembled large data sets and standard benchmarks (Wu et al., 2017) and a suite of DL packages like deepChem (Subramanian et al., 2016).”