Richard N. Zare
Materials Chemistry · 2003–2018
A researcher in the Science corpus: 9 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 37 citations into it
- Computational Theory and Mathematics — 26 citations into it
- Artificial Intelligence — 18 citations into it
- Computational Chemistry & Drug Discovery — 15 citations into it
- Reinforcement Learning — 11 citations into it
- Inorganic Chemistry — 10 citations into it
Whom Richard N. Zare trusts
Their strongest outbound trust, strongest first, inferred from their citations.
- Alán Aspuru-Guzik (in Computational Theory and Mathematics)
- Pieter Abbeel (in Artificial Intelligence)
- David Kristjanson Duvenaud (in Materials Chemistry)
- Yan Duan (in Artificial Intelligence)
- Carlos Florensa (in Artificial Intelligence)
Papers from Richard N. Zare's perspective
Ordered by how strongly the researchers Richard N. Zare trusts endorse each paper. Same corpus, a different order for every researcher.
- Regression Shrinkage and Selection via the Lasso
- Stochastic Neural Networks for Hierarchical Reinforcement Learning
- Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
- Tumor vascular permeability and the EPR effect in macromolecular therapeutics: a review. (Journal of Controlled Release)
- Gold nanoparticles: a new X-ray contrast agent. (British Journal of Radiology)
- Stealth liposomes and long circulating nanoparticles: critical issues in pharmacokinetics, opsonization and protein-binding properties. (Progress in lipid research)
- Application of Generative Autoencoder in De Novo Molecular Design
- Neural Networks for the Prediction of Organic Chemistry Reactions
- Chemistry for peptide and protein PEGylation. (Advanced Drug Delivery Reviews)
- Nanoparticle therapeutics: an emerging treatment modality for cancer (Nature reviews. Drug discovery)
Their papers
- Microfluidic device for single-cell analysis. (2003) — 606 citations
- Optimization of Molecules via Deep Reinforcement Learning (2018) — 665 citations
- Optimizing Chemical Reactions with Deep Reinforcement Learning (2017) — 395 citations
- A monomeric G protein-coupled receptor isolated in a high-density lipoprotein particle efficiently activates its G protein (2007) — 691 citations
- Nanoparticle PEGylation for imaging and therapy (2011) — 1,967 citations
- One-pot synthesis of protein-embedded metal-organic frameworks with enhanced biological activities. (2014) — 845 citations
- Protein-inorganic hybrid nanoflowers. (2012) — 1,099 citations
- Advances in Asphaltene Science and the Yen–Mullins Model (2012) — 989 citations
- Comet 81P/Wild 2 Under a Microscope (2006) — 877 citations