Kaitlyn M. Gayvert
Computational Theory and Mathematics · 2016–2019
A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computational Theory and Mathematics — 20 citations into it
- Molecular Biology — 18 citations into it
Whom Kaitlyn M. Gayvert trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Gerd Kempermann
- Gábor Csárdi
- Jishnu Das
- Simon Anders
- Christopher A. Lipinski
- Trevor J. Hastie
- Michael Kuhn
- Haiyuan Yu
- Tamás Nepusz
- Paul Theodor Pyl
Who trusts Kaitlyn M. Gayvert overall
Papers from Kaitlyn M. Gayvert's perspective
Ordered by how strongly the researchers Kaitlyn M. Gayvert trusts endorse each paper. Same corpus, a different order for every researcher.
- HINT: High-quality protein interactomes and their applications in understanding human disease
- DrugBank 4.0: shedding new light on drug metabolism
- A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases.
- HTSeq—a Python framework to work with high-throughput sequencing data (bioRxiv)
- The influence of drug-like concepts on decision-making in medicinal chemistry (Nature reviews. Drug discovery)
- Random Forests
- Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings.
- Lead- and drug-like compounds: the rule-of-five revolution. (Drug Discovery Today : Technologies)
- Molecular properties that influence the oral bioavailability of drug candidates. (Journal of Medicinal Chemistry)
- Effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. (New England Journal of Medicine)
Their papers
- A data-driven approach to predicting successes and failures of clinical trials (2016) — 300 citations
- A Bayesian machine learning approach for drug target identification using diverse data types (2019) — 197 citations