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Diffstat (limited to 'loael.Rmd')
| -rw-r--r-- | loael.Rmd | 40 |
1 files changed, 26 insertions, 14 deletions
@@ -190,21 +190,28 @@ total number of atom environments $A \cup B$ (Jaccard/Tanimoto index, [@eq:jacca $$ sim = \frac{|A \cap B|}{|A \cup B|} $$ {#eq:jaccard} A threshold of $sim > 0.1$ is used for the identification of neighbors for -local QSAR models. Compounds with the same structure as the query structure -are eliminated from the neighbors to obtain unbiased predictions in the presence of duplicates. +local QSAR models. A low similarity threshold has the advantage, that +predictions can be made even in the absence of closely related structures, and +that completely unrelated compounds are still not included as neighbors. As +neighbor contributions are weighted by similarity in local QSAR models, +neighbors with low similarity have also a low impact on the prediction result. + +Compounds with the same structure as the query structure are automatically +eliminated from neighbors to obtain unbiased predictions in the presence of +duplicates. ### Local QSAR models and predictions Only similar compounds (*neighbors*) above the threshold are used for local QSAR models. In this investigation we are using a weighted partial least squares regression (PLS) algorithm for the prediction of quantitative -properties. First all fingerprint features with identical values across all -neighbors are removed. The reamining set of features is used as descriptors -for creating a local weighted PLS model with atom environments as descriptors -and model similarities as weights. The `pls` method from the `caret` R package -[@Kuhn08] is used for this purpose. Models are trained with the default -`caret` settings, optimizing the number of PLS components by bootstrap -resampling. +properties. First all uninformative fingerprints (i.e. features with identical +values across all neighbors) are removed. The reamining set of features is +used as descriptors for creating a local weighted PLS model with atom +environments as descriptors and model similarities as weights. The `pls` method +from the `caret` R package [@Kuhn08] is used for this purpose. Models are +trained with the default `caret` settings, optimizing the number of PLS +components by bootstrap resampling. Finally the local PLS model is applied to predict the activity of the query compound. The RMSE of bootstrapped model predictions is used to construct 95\% @@ -239,6 +246,8 @@ optimisations were performed in order to avoid overfitting a single dataset. Results from 3 repeated 10-fold crossvalidations with independent training/test set splits are provided as additional information to the test set results. +The final model for production purposes was trained with all available LOAEL data (Mazzatorta and Swiss Federal Office datasets combined). + Results ======= @@ -304,7 +313,7 @@ experimental results within individual datasets and between datasets. ##### Intra dataset variability -```{r echo=F} +```{r echo=T} m.dupsmi <- unique(m$SMILES[duplicated(m$SMILES)]) s.dupsmi <- unique(s$SMILES[duplicated(s$SMILES)]) c.dupsmi <- unique(c$SMILES[duplicated(c$SMILES)]) @@ -317,6 +326,10 @@ m.dupnr <- length(m.dupsmi) s.dupnr <- length(s.dupsmi) c.dupnr <- length(c.dupsmi) +#m.dup +#m.dup$LOAEL +#m.dup$SMILES + m.dup$sd <- ave(m.dup$LOAEL,m.dup$SMILES,FUN=sd) s.dup$sd <- ave(s.dup$LOAEL,s.dup$SMILES,FUN=sd) c.dup$sd <- ave(c.dup$LOAEL,c.dup$SMILES,FUN=sd) @@ -328,17 +341,16 @@ p = t.test(m.dup$sd,s.dup$sd)$p.value The Mazzatorta dataset has `r length(m$SMILES)` LOAEL values for `r length(levels(m$SMILES))` unique structures, `r m.dupnr` compounds have multiple measurements with a mean standard deviation of -`r round(mean(m.dup$sd),2)` log10 units (@mazzatorta08, [@fig:intra]). +`r round(mean(10^(-1*m.dup$sd)),2)` mmol/kg_bw/day (`r round(mean(m.dup$sd),2)` log10 units @mazzatorta08, [@fig:intra]). The Swiss Federal Office dataset has `r length(s$SMILES)` rat LOAEL values for `r length(levels(s$SMILES))` unique structures, `r s.dupnr` compounds have multiple measurements with a mean standard deviation of -`r round(mean(s.dup$sd),2)` log10 units. +`r round(mean(10^(-1*s.dup$sd)),2)` mmol/kg_bw/day (`r round(mean(s.dup$sd),2)` log10 units). Standard deviations of both datasets do not show a statistically significant difference with a p-value (t-test) of `r round(p,2)`. -The combined test set has a mean standard deviation of `r round(mean(c.dup$sd),2)` -log10 units. +The combined test set has a mean standard deviation of `r round(mean(10^(-1*c.dup$sd)),2)` mmol/kg_bw/day (`r round(mean(c.dup$sd),2)` log10 units). {#fig:intra} |