A Bayesian Approach to in Silico Blood-Brain Barrier Penetration Modeling

By Inês Martins, Ana L. Teixeira, Luis C Pinheiro, André O. Falcão. 2012

365 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Oncology, Probabilistic & Bayesian Methods, 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

1. Sepp Hochreiter
2. Günter Klambauer
3. Haifeng Wang
4. Jieqiong Lei
5. Qiang Yang
6. Ying Wei
7. Shuiwang Ji
8. Junzhou Huang

In Molecular Biology

1. Sepp Hochreiter
2. Günter Klambauer
3. Michael Widrich
4. Haifeng Wang
5. Hao Yuan
6. Qiang Yang
7. Ying Wei
8. Shuiwang Ji

In Probabilistic & Bayesian Methods

1. Qiang Yang
2. Ying Wei
3. Junzhou Huang
4. Haiyang Yu
5. Sepp Hochreiter

In Computational Chemistry & Drug Discovery

1. Haifeng Wang
2. Jieqiong Lei
3. Qiang Yang
4. Ying Wei
5. Shuiwang Ji
6. Junzhou Huang
7. Xiaomin Fang
8. Günter Klambauer

Citations in the corpus

• Strategies for Pre-training Graph Neural Networks (2019, neutral): “• BBBP. Blood-brain barrier penetration (membrane permeability) (Martins et al., 2012).”
• Graph Self-Supervised Learning: A Survey (2021, neutral): “13 38 2 [74], [102] BBBP [144] 2,039 24.”
• 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].”
• Pre-training Graph Neural Networks (2019, neutral): “• BBBP. Blood-brain barrier penetration (membrane permeability) [28].”
• Pre-training Molecular Graph Representation with 3D Geometry (2021, neutral): “Molecular Property: Pharmacology The Blood-Brain Barrier Penetration (BBBP) [61] dataset measures whether a molecule will penetrate the central nervous system.”
• Frustratingly Easy Transferability Estimation (2021, methodology): “We consider four target tasks that predict the molecular ADMET properties, including BBBP [39], BACE [40], Esol [41], FreeSolv [42].”
• Explainability in Graph Neural Networks: A Taxonomic Survey (2020, methodology): “To provide a comprehensive study, we employ six different datasets; these are, Graph-SST2, Graph-SST5, Graph-Twitter, BBBP, BA-2Motifs, and BA-Shapes, which include both graph classification and node classification tasks, both real-world and synthetic data.”
• Hopfield Networks is All You Need (2020, background): “(2016), c) metabolic effects as blood-brain barrier permeability (BBBP) (Martins et al., 2012) and d) side effects of a chemical compound from the Side Effect Resource (SIDER) Kuhn et al.”