logD7.4 modeling using Bayesian Regularized Neural Networks. Assessment and correction of the errors of prediction.
about
Quantification of frequent-hitter behavior based on historical high-throughput screening data.Guidelines for the welfare and use of animals in cancer research.Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules.Lipophilicity in drug discovery.A renaissance of neural networks in drug discovery.A Risk Assessment Perspective of Current Practice in Characterizing Uncertainties in QSAR Regression Predictions.Predictivity of Simulated ADME AutoQSAR Models over Time.QSAR with experimental and predictive distributions: an information theoretic approach for assessing model quality.The Good, the Bad and the Ugly of Distribution Coefficients: Current Status, Views and OutlookIn silicoevaluation of logD7.4and comparison with other prediction methods
P2860
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P2860
logD7.4 modeling using Bayesian Regularized Neural Networks. Assessment and correction of the errors of prediction.
description
2006 nî lūn-bûn
@nan
2006 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2006 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2006年の論文
@ja
2006年論文
@yue
2006年論文
@zh-hant
2006年論文
@zh-hk
2006年論文
@zh-mo
2006年論文
@zh-tw
2006年论文
@wuu
name
logD7.4 modeling using Bayesia ...... n of the errors of prediction.
@ast
logD7.4 modeling using Bayesia ...... n of the errors of prediction.
@en
type
label
logD7.4 modeling using Bayesia ...... n of the errors of prediction.
@ast
logD7.4 modeling using Bayesia ...... n of the errors of prediction.
@en
prefLabel
logD7.4 modeling using Bayesia ...... n of the errors of prediction.
@ast
logD7.4 modeling using Bayesia ...... n of the errors of prediction.
@en
P356
P1476
logD7.4 modeling using Bayesia ...... n of the errors of prediction.
@en
P2093
Nathan R McElroy
Pierre Bruneau
P304
P356
10.1021/CI0504014
P577
2006-05-01T00:00:00Z