Lang, Andrew SID; Bradley, Jean-Claude Abraham descriptor E.
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Andrew Lang创建时间：
2012-06-05
相关数据集
Monte-Carlo method-based QSAR model to discover phytochemical urease inhibitors using SMILES and GRAPH descriptors
Urease inhibitors are known to play a vital role in the field of medicine as well as agriculture. Special attention is attributed to the development of novel urease inhibitors with a view to treat the
NIAID Data Ecosystem50
Supplementary file 1_QSAR-guided discovery of novel KRAS inhibitors for lung cancer therapy.docx
IntroductionKRAS mutations are key oncogenic drivers in lung cancer, yet effective pharmacological targeting has remained a major challenge due to the protein's elusive and dynamic binding pockets. Co
NIAID Data Ecosystem20
Predictivity of algorithms trained upon T . pyriformis growth inhibition/rat acute lethality parent and feature-reduced subsets (with corresponding descriptor totals relating to each further displayed).
Performance is presented in terms of R2train, R2cv (k = 10) and R2over. Highest- and lowest-scoring models in former two metrics shaded green and orange respectively. Highest- and lowest-scoring in th
NIAID Data Ecosystem10
Standardized coefficients of predictive variables in the QSAR model.
Standardized coefficients of predictive variables in the QSAR model.
NIAID Data Ecosystem30
Benigni, R.; Bossa, C.; Netzeva, T. I.; Rodomonte, A.; Tsakovska, I. Mechanistic QSAR of aromatic amines: New models for discriminating between homocyclic mutagens and nonmutagens, and validation of models for carcinogens. Environ. Mol. Mutagen. 2007, 48, 754–771.
Because of its environmental and industrial importance, the aromatic amines are the single chemical class most studied for its ability to induce mutations and cancer. The large database of mutagenicit
QSAR DataBank2015-03-23 更新10
