Lang, Andrew SID; Bradley, Jean-Claude Abraham descriptor E.
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Andrew Lang创建时间：
2012-06-05
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Comprehensive Evaluation and Comparison of Machine Learning Methods in QSAR Modeling of Antioxidant Tripeptides
Due to their multiple beneficial effects, antioxidant peptides have attracted increasing interest. Currently, the screening and identification of bioactive peptides, including antioxidative peptides b
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Predictions and residuals (biological activities) from the model validated by non-flavonoids.
Predictions and residuals (biological activities) from the model validated by non-flavonoids.
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PoseidonQ: A Free Machine Learning Platform for the Development, Analysis, and Validation of Efficient and Portable QSAR Models for Drug Discovery
The advent of powerful machine learning algorithms as well as the availability of high volume of pharmacological data has given new fuel to QSAR, opening new unprecedented options for deriving highly
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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
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Idealization of correlations between optimal simplified molecular input-line entry system-based descriptors and skin sensitization
The Index of Ideality of Correlation (IIC) is a new criterion of the predictive potential for quantitative structure–property/activity relationships. The value of the IIC is a mathematical function se
Taylor & Francis Group2024-05-03 更新00
