Using beta binomials to estimate classification uncertainty for ensemble models
- Robert D Clark^{1}Email author,
- Wenkel Liang^{1},
- Adam C Lee^{1},
- Michael S Lawless^{1},
- Robert Fraczkiewicz^{1} and
- Marvin Waldman^{1}
https://doi.org/10.1186/1758-2946-6-34
© Clark et al.; licensee Chemistry Central Ltd. 2014
Received: 5 March 2014
Accepted: 16 June 2014
Published: 22 June 2014
Abstract
Background
Quantitative structure-activity (QSAR) models have enormous potential for reducing drug discovery and development costs as well as the need for animal testing. Great strides have been made in estimating their overall reliability, but to fully realize that potential, researchers and regulators need to know how confident they can be in individual predictions.
Results
Submodels in an ensemble model which have been trained on different subsets of a shared training pool represent multiple samples of the model space, and the degree of agreement among them contains information on the reliability of ensemble predictions. For artificial neural network ensembles (ANNEs) using two different methods for determining ensemble classification – one using vote tallies and the other averaging individual network outputs – we have found that the distribution of predictions across positive vote tallies can be reasonably well-modeled as a beta binomial distribution, as can the distribution of errors. Together, these two distributions can be used to estimate the probability that a given predictive classification will be in error. Large data sets comprised of logP, Ames mutagenicity, and CYP2D6 inhibition data are used to illustrate and validate the method. The distributions of predictions and errors for the training pool accurately predicted the distribution of predictions and errors for large external validation sets, even when the number of positive and negative examples in the training pool were not balanced. Moreover, the likelihood of a given compound being prospectively misclassified as a function of the degree of consensus between networks in the ensemble could in most cases be estimated accurately from the fitted beta binomial distributions for the training pool.
Conclusions
Confidence in an individual predictive classification by an ensemble model can be accurately assessed by examining the distributions of predictions and errors as a function of the degree of agreement among the constituent submodels. Further, ensemble uncertainty estimation can often be improved by adjusting the voting or classification threshold based on the parameters of the error distribution. Finally, the profiles for models whose predictive uncertainty estimates are not reliable provide clues to that effect without the need for comparison to an external test set.
Keywords
Artificial neural network ensemble ANNE Classification Confidence Error estimation Predictive value QSAR UncertaintyBackground
Drug discovery and development is an expensive business and its costs continue to rise. Exploitation of quantitative structure activity relationships (QSARs) and related in silico methods have the potential to speed development and reduce costs considerably, and regulatory agencies have expressed support for doing so [1–3]. Considerable progress has been made in recent years on assessing the overall predictive reliability of QSAR models, but research and regulatory applications both require good ways to estimate the accuracy of individual predictions. Considerable work has been done on ways to identify compounds for which predictions are unlikely to be reliable – i.e., on applicability domains [4–6] and on quantitative estimations of uncertainty for regression models [4, 7–13]. Some prior work has made use of ensemble variance for categorical estimation of confidence [14, 15]. To our knowledge, however, the degree of ensemble consensus in terms of votes has not been utilized to make quantitative estimates of predictive classification uncertainty for individual predictions.
where $\mathit{\Gamma}\left(\mathit{x}\right)$ is the gamma function [20], which is a continuous extension of the factorial (Γ(n + 1) = n !). Unlike the binomial distribution, the beta binomial can be convex as well as concave. The former is the case for α < 1 and β < 1 whereas the latter holds for α > 1 and β > 1. When α = β = 1, the beta binomial distribution reduces to the discrete uniform distribution on the interval 0 to K.
We attempted to fit beta binomial distributions to uncertainty profiles (i.e., the misclassification or error rate as a function of the number of K networks making k positive votes for a given compound) directly but the results were not satisfactory. On the other hand, separate beta binomials fit distributions of ensemble predictions and error counts for the training pool reasonably well. This is not altogether surprising, since both distributions result from a series of K events that are related but not independent. There are two possible outcomes in both cases: “the compound in question is a positive or a negative” for predictions and “the prediction is correct or incorrect” for errors. What is somewhat surprising is that the distributions fitted to the training pool match the corresponding distributions seen for large external validation sets as well.
To distinguish between underlying population distributions and estimated distributions fitted to samples, we introduce the following notation. Let φ(k) represent our estimate of P(k), the probability distribution of positive vote tallies across all predictions or simply the “prediction distribution”. Let ϵ(k) represent our estimate of P(k|ϵ), the probability distribution of all misclassified predictions as a function of k, which we refer to as the “error distribution”. P(ϵ|k) is the probability of an error in classification (i.e., the uncertainty of a prediction) that receives k positive votes; our estimate of it will be represented by u(k) and referred to as the uncertainty profile or distribution.
MR is obtained from the overall misclassification rate (i.e., overall number of incorrect predictions divided by the total number of predictions) for the training pool, and ϵ(k) and φ(k) are estimated by fitting beta binomial distributions to the training pool errors and predictions, respectively, as functions of k.
- 1.
Build an ensemble of K submodels.
- 2.
Establish a classification threshold for each submodel that determines its vote.
- 3.
Tally the number of positive votes k for each prediction.
- 4.
Establish a decision rule for the ensemble.
- 5.
Classify each ensemble prediction for the training pool as being correct or incorrect.
- 6.
Count the number of errors for each vote tally k.
- 7.
Add a continuity correction to the count for each tally by adding 1 to each prediction tally and adding 0.5 to each error count.
- 8.
Find the alpha and beta parameters of a beta binomial distribution, φ(k), that best matches the cumulative voting distribution as a function of k by minimizing the Kolmogorov-Smirnov (K-S) statistic [21] between the observed and beta binomial distributions.
- 9.
Find the optimal beta binomial distribution, ϵ(k), that best matches the observed distribution of the errors by similarly minimizing the K-S statistic between the cumulative distributions.
- 10.
Estimate the uncertainty distribution as u(k) = MR*ϵ(k)/φ(k).
The remainder of this paper is devoted to showing applications of this protocol to data sets of varying quality and showing the influence of imbalances in class size. Most of the examples described involve models where the ensemble decision is determined by voting: decision thresholds are established independently for each network and the classification “votes” that result are tallied to determine the ensemble classification. For unbalanced data sets, resetting the ensemble voting threshold to match the mean of ϵ(k) may substantially improve the overall balance between specificity and sensitivity.
One application makes use of an alternative method in which individual network outputs are averaged and compared to an aggregate classification threshold. In such cases, the model benefits from resetting the threshold to the geometric mean of the averaged threshold and the mean of ϵ(k). Working with large validation sets proved essential to getting good enough sampling of errors to meaningfully assess how well our uncertainty estimation method works. Typically, the numbers of predictions and errors receiving k votes is small for intermediate values of k (i.e., those not near the extremes of 0 and K), which can result in low counts in this region unless the validation set is comprised of thousands of compounds. This is especially true for good models. The desire to maximize the size of the validation set to minimize the effects of noise in assessing the performance of the model led us to use an unusually small fraction of the available data to train the models – 10 to 30% instead of the typical 80 to 90%. It also helped to demonstrate that the methodology does not require large training sets to work.
Note that though the examples examined here all involve artificial neural network ensembles, the algorithm above is cast in more general terms: we expect the method to be applicable to any ensemble of reasonably robust submodels, regardless of their source.
Results and discussion
Balanced data sets
S+logP is the ANNE regression model for octanol:water partition coefficient provided in ADMET Predictor [22]. Its excellent performance in third-party evaluations [23, 24] reflects the high quality of the large (12,580 compound), heavily curated data set upon which it is based. For our first example, that data set was split roughly in half for classification purposes by categorizing compounds having log P ≥ 2.0 as “positive” and those having log P < 2.0 as “negative”. Doing so yielded data set “logP2”, which was comprised of 5946 positives and 6634 negatives. The bulk (90%) of the data was set aside for use as an external validation set and was not used or referred to in any way for model building.
The remaining 10% of the data set was used to create artificial neural network ensemble classification models (ANNEs), each made up of 33 individual networks. All networks in an ensemble have the same architecture (same number of neurons and descriptor inputs) but are trained with different subsets of the shared training pool. Details of the model construction are provided in the Methods section, but they are unlikely to affect the general phenomena and conclusions discussed herein.
Performance statistics for the models described here and their beta binomial parameters ^{ a }
Data set | Model | Architect.^{b} | Threshold | Sensitivity | Specificity | J ^{ c } | α | β | MR^{d} |
---|---|---|---|---|---|---|---|---|---|
logP2 | 1 | 33×6×40 | 16.5 | 0.849 | 0.882 | 0.731 | 0.695^{c} | 0.772 | 0.083 |
2 | 33×3×45 | 16.5 | 0.861 | 0.857 | 0.718 | 0.635 | 0.571 | 0.087 | |
Ames | 1 | 33×2×26 | 16.5 | 0.791 | 0.596 | 0.387 | 0.926 | 0.537 | 0.222 |
2 | 33×4×24 | 16.5 | 0.700 | 0.676 | 0.376 | 0.489 | 0.462 | 0.239 | |
logP3 | 1a | 33×4×44 | 16.5 | 0.882 | 0.892 | 0.774 | 1.229 | 0.469 | 0.077 |
1b | 33×4×44 | 24.5 | 0.840 | 0.921 | 0.761 | 0.925 | 0.323 | 0.066 | |
2a | 75×4×42 | 37.5 | 0.889 | 0.885 | 0.775 | 1.163 | 0.472 | 0.089 | |
2b | 75x4x42 | 53.5 | 0.858 | 0.910 | 0.768 | 1.037 | 0.415 | 0.083 | |
CYP2D6 | 1a | 33×3×35 | 16.5 | 0.721 | 0.789 | 0.510 | 1.561 | 0.447 | 0.211 |
1b | 33×3×35 | 27.5 | 0.604 | 0.873 | 0.476 | 1.350 | 0.294 | 0.164 | |
logP3 | 3a ^{ e } | 33×3×24 ^{ e } | 16.2 | 0.874 | 0.862 | 0.736 | 0.891 | 0.263 | 0.095 |
3b | 33×3×24 | 20.3 | 0.862 | 0.874 | 0.742 | 0.690 | 0.306 | 0.096 |
Secondly, the data are very noisy at intermediate values of k because the sampling rate is low. The standard error of a rare event count is equal to its square root, so the relative standard error is large for counts below 10. As a result of the noisy data and low sampling counts, all that can be claimed for the training pool is that the uncertainty profile calculated from the training pool prediction and error beta binomials is consistent with what is actually observed. Note, too, that this “calculated uncertainty profile” crosses the ensemble voting threshold just below the theoretical threshold error rate of 0.5 (indicated by the horizontal dotted line in Figure 2B).
Finally, a continuity correction of 0.5 [25] has been applied to all error counts in Figure 2. This addresses a fundamental disconnect: the counts are integers, whereas the beta binomial probabilities to which we are fitting them can take on any value between 0 and 1. Hence uncorrected counts from finite samples can lead to poor estimates for the actual distribution parameters, especially when the expected number of errors (ϵ(k)*N) for a particular value of k is low. The error rate when there is only one prediction, for example, is 0.0 if the prediction in question is correct and 1.0 if it is not, and neither proportion is likely to be a good estimate of the uncertainty. Indeed, in the extreme case where there are no training pool predictions at all – erroneous or otherwise – the uncorrected error rate is equal to 0/0, which is indeterminate. This problem is often encountered when estimating frequencies of rare events, and adding a continuity correction of 0.5 is a standard way to deal with it [25]. A complementary continuity correction of 1 was added to each prediction count, yielding a sample error rate of 0.5 for values of k for which the training pool contains no predictions at all.
Figures 2C and D show the distribution of predictions and errors and of error rates for compounds in the external validation set along with the beta binomial distributions and uncertainty profiles generated using the distribution parameters derived from fitting to the training pool results. The deviations observed are small in both figures, remarkably so for the validation set’s error rate profile (Figure 2D). That the error rate for the validation set tracks the uncertainty profile calculated from the training pool data more closely than the training pool’s own error rate tracks it is due in large part to the validation set having ten times as many observations, which significantly reduces the noise. Note that the observed error rate is again quite close to 0.5 at the ensemble voting threshold of 16.5 (Figure 2D).
The Ames mutagenicity data set (taken from the publicly available compilation by Hansen et al. [26]) represents a more “real world” classification problem than the logP2 data set. The authors compiled it with admirable care, but the data set can at best be categorized as medium quality, given that variability between laboratories limits the reproducibility of the underlying assay to only about 85% [26, 27]. Here, too negatives are roughly balanced with positives and about 10% of the data set was allocated to the training pool.
Ames-1 does a good job of predicting uncertainties at intermediate vote tallies, but underestimates errors somewhat at the extremes. Having some errors at the extremes is consistent with the rather modest historical accuracy rate of 85% for the Ames assay. The fact that most or all of the networks agree on how these compounds should be classified, however, suggests that some of them may be miscategorized due to mistakes in the literature occurring in the course of publication and subsequent data compilation – i.e., that a number of the misclassifications may in fact be false false negatives. We have found confidence analysis of the sort described here a valuable tool for identifying such potential errors in data sets.
Ames-2 does a better job of matching the observed error rates at the extremes, but consistently underestimates them (and therefore overestimates confidence in the predictions) at intermediate levels of consensus. These may well be compounds that are borderline in their mutagenicity, but the practical outcome with respect to the reliability of confidence estimates is the same: the profile is less useful than it could be for confidence estimation.Fortunately, Ames-2 provides a clue to its weakness even in the absence of any external validation set, in that its calculated uncertainty profile crosses the voting threshold well below 0.5 – below 0.4, in fact (Figure 4D). To appreciate why this is indicative of a problem, consider a compound receiving a number of positive votes just above or below the voting threshold. A small change in the properties reflected by the model descriptors could flip it from being correctly classified to being misclassified. There has to be a point between the extremes where the threshold is perfectly placed, but as that point is approached, the prediction becomes a guess (at best) and the expected uncertainty will converge to 0.5 (or higher) at the threshold. The fact that the predicted uncertainties at the voting threshold are reasonably close to 0.5 for Ames-1 (Figure 4B) and for the logP2 models described above (Figures 2 and 3) is consistent with their confidence estimations being reliable.The central flattening evident in Figure 4D, in contrast, should be taken as a warning flag. The associated model itself (as opposed to the uncertainty estimate) need not necessarily be discarded, however, especially if it is a case of the profile being distorted by undersampling of the error distribution. When this occurs, it may be preferable to provide aggregate class confidences – i.e., positive and negative predictive values – in lieu of individual confidence estimates.
Unbalanced data sets
Data sets in which there are many more observations in one class than another are problematic for many classification methods. Rather than trying to balance the training pool (e.g., by undersampling the larger class), ADMET Modeler addresses such imbalances by scaling the terms in the objective function by class size and by relying on Youden’s index J[28] to set classification thresholds (see Methods for details). Doing so provides robust performance statistics across a wide range of data sets, but can complicate confidence estimation. To illustrate, an imbalanced logP data set was created by categorizing compounds having log P ≥ 3.0 as “positive” and those having logP < 3.0 as “negative”. Doing so yielded a high-quality data set (logP3) comprised of 3161 positives and 9419 negatives, by coincidence a ratio just over 1:3. The fraction used as the training pool was increased from 10% to 15% to offset the reduced number of positive examples.
Note that the two predicted uncertainty profiles bracket 0.5 at the refined threshold. The discontinuities near the thresholds arise because Youden’s index is used to set the classification thresholds for the individual networks. For a large training pool and a well-trained model, the density of negative predictions will be high near 0 for each network and fall off as one moves to the right, towards 1. Similarly, the density of positive predictions will be high near 1 and fall off as one moves to the left. In the limit of an infinite training pool, maximizing Youden’s index will place the classification threshold at a point where any shift to the left will increase sensitivity less than it will decrease specificity. Similarly, any increase in specificity achieved by a shift to the right will be more than offset by a decrease in sensitivity.
Increasing the number of networks did not improve the situation in this case (details not shown). One possible alternative strategy for estimating uncertainty is to replace beta binomial profiles with average class uncertainties calculated for the training pool: ${\overline{\mathit{u}}}_{0}$ for the negative class and ${\overline{\mathit{u}}}_{1}$ for the positive class^{d}. The resulting estimates are shown by the dotted gray lines in Figure 8B. As expected, the average error rates underestimate uncertainty except at the extremes of consensus, with ${\overline{\mathit{u}}}_{1}$ clearly inferior to the composite beta binomial estimate for positive predictions. While the latter overestimates uncertainty for negative predictions by a factor of 2–3, ${\overline{\mathit{u}}}_{0}$ underestimates them by a similar factor. The more conservative option – in this case, underestimating uncertainty – will be preferable in most circumstances, especially if the degree of over- and underestimation is similar.
Confidence estimation when using averaging
It is perhaps not surprising that the uncertainty estimation method described above works well on ensemble models in which predictive classifications are determined by tallies of independently determined positive votes, since in that case the degree of network consensus reflects the variability of outcomes fairly directly. To explore how broadly applicable the method is, we turned to ANNEs in which classification is determined by averaging the individual network outputs (which are logistic functions ranging from 0 to 1 for classification models) and comparing that average to an ensemble threshold. If the average output lies above the ensemble classification threshold, the compound is classified as a positive; if the average falls below the threshold, the compound is classified as a negative. The ensemble threshold that maximizes Youden’s index across the training pool (the “J_{max} threshold”) is used by default. The vote tallies for estimating predictive uncertainty are obtained by comparing each network’s output to the ensemble threshold but are not themselves used for classification.
Not all networks have an equal impact on prediction, but all do contribute equally to uncertainty estimation. Those whose output lies closest to one output extreme or the other – i.e., to 0 or to 1 – shift the average most and so have the greatest “voice” in the ensemble classification; networks that dissent strongly enough from the consensus classification can sway the ensemble classification in their direction. Only in the case of unanimity does the vote tally necessarily reflect the ultimate classification: if all network outputs fall below (or above) the threshold, their average output must do so as well.
Just as for the individual network thresholds used in the voting method, the default ensemble classification threshold used in averaging maximizes J and can be suboptimal in terms of predictive values. The mean of the beta binomial distribution fitted to the training pool for logP3-3a is 25.5, which is 0.772 on a per network basis; however, we have found that shifting the ensemble classification threshold to that value generally constitutes an overcorrection. Averaging models are better refined by shifting the threshold to the geometric mean of the J_{max} threshold and the mean of the beta binomial fitted to the initial error distribution. In this case, refinement shifted the classification threshold for the sum from 16.2 for logP3-3a to 20.3 for logP3-3b (the vertical dotted green lines in Figure 10), which is equivalent to a shift in the threshold for the average from 0.491 to 0.616.
Shifting the averaging threshold affects the entire range of tallies, not just those between the thresholds, and the distribution of predictions is affected as well as that of errors. The predictions most affected are those in which all network outputs for a particular compound lie between 0.491 and 0.616: all networks in logP3-3a will classify it as a positive, whereas all networks in logP3-3b will classify such a compound as a negative. One of those ensemble predictions will be wrong, of course, but the vote will be unanimous in both cases – a tally of 33 positive votes for logP3-3a vs. no positive votes for logP3-3b.The model using a threshold based on Youden’s index alone (logP3-3a) underestimates the error rates seen in the validation set and thereby overestimates how much confidence one can have in its individual predictions (Figure 10D). This is in contrast to model logP3-3b (indicated by green lines in Figure 10), which uses the same network outputs but a refined threshold and provides more appropriate uncertainty estimates. Here again, the less reliable model signals its weakness by having a predicted uncertainty profile that crosses its voting threshold well below 0.5. The crossover point for the model with the more robust confidence estimates, on the other hand, lies reasonably close to 0.5.Despite the unbalanced nature of the data set, the error rate profiles seen in logP3-3a and logP3-3b both lack the discontinuity near the threshold that is so evident for models built on the same data set but using the voting method to determine ensemble classification (Figure 10 vs Figure 6). This is due at least in part to the transition zone between positive and negative predictions being spread across seven tally bins rather than concentrated in two.
Conclusions
Tallying votes and averaging submodel outputs are both useful ways to assess the degree of consensus within an ensemble. Fitting ensemble misclassification rates to a binomial distribution to assess confidence is intuitively appealing but flounders in practice because predictions by the component submodels are not independent. Using beta binomial distributions to model predictions and errors works well, however, and the ratio of such distributions can be used to estimate the likelihood that a given prediction will be incorrect.
The corresponding distributions seen in large, held-out validation sets match those seen for the training pool remarkably well in most cases. Moreover, those cases for which the predicted error rate profile is not reliable provide a good clue to that effect, in that their calculated uncertainty profiles cross the model’s voting threshold (or, in the case of averaging, the ensemble classification threshold applied to the summed outputs) well below the expected value of 0.5. Why this occurs is not clear at this time but it may reflect a lack of diversity in predictions among the networks that make up the ensemble. If consensus is complete across the entire training pool, for example, all predictions and errors will lie at the extremes and no information will be available regarding the distribution of predictions and errors in between.
Models based on voting that are built on unbalanced data sets and use Youden’s index as the criterion for setting individual network classification thresholds tend to have a discontinuity in observed error rates near the ensemble voting threshold. Resetting that threshold to match the mean of the beta binomial for the error, ϵ(k), helps attenuate that discontinuity. In many cases, the uncertainty profile can still be adequately described by a combination of just two distributions – one fitted to the training pool predictions and another fitted to the training pool errors. In other cases, four sets of distribution parameters and a threshold value are required to create a composite distribution, one pair applying to the left of the threshold (where the true and false negatives are found) and the other pair to its right (where true and false positives are found). Models built using the averaging method avoid this complication.
Though the examples discussed here all involve ANNE classification models, the technique should be applicable to any ensemble classification model in which the constituent submodels represent subsamples of a shared model space and the predictions for individual submodels are accessible. Random forest models come to mind as one example, but most methods that involve bagging [31] probably also qualify. Most importantly, perhaps, the training pool distributions should be equally applicable to external predictions – provided that, as is the case in these examples, model building is done in a way that avoids overtraining and the training pool is representative of the population for which uncertainty estimates are desired. If the model-building tool used requires an artificially biased training pool, it needs to be augmented with examples from the undersampled class before the prediction and error beta binomials are fitted. Applying this approach to the logP3 data set yields results qualitatively similar to those seen when using an unbalanced training pool (details not shown), but full validation of such an approach is beyond the scope of this paper.
Our approach complements classical overall performance measures based on partitioning predictions into true positive, false positive, true negative and false negative categories; it does not replace them. Implementing it is straightforward: it was originally done using the GAMMALN function and the Solver add-in for Excel [32] to fit the beta binomial parameters. The uncertainty values obtained thereby have subsequently been incorporated into many of the models distributed with ADMET Predictor 7.0 as predictive “confidences” (equal to 1 – u(k)). Such confidence values can also be generated for classification models created using the ADMET Modeler module of the program.
Experimental
Data sets
The log P data set is diverse and has been heavily curated. It consists of the 12,580 values used to build and test the S + logP model distributed with ADMET Predictor, the bulk of which are derived from the BioByte database [33]. Some entries have been modified to accommodate discrepancies found with respect to literature references, while others have been added from the original literature in the interests of expanded coverage of chemistry and property space. Categorizing compounds having log P ≥ 2.0 as “positive” and those having log P < 2.0 as “negative” yielded a relatively balanced data set (logP2) comprised of 5946 positives and 6634 negatives.
The Ames mutagenicity data set was taken from the compilation by Hansen et al.[26] and contains data of medium quality. A handful of structures were corrected for structural errors and redundant entries were removed, as were salts of metals other than sodium or potassium. It is quite a balanced data set: of the 6471 entries surviving curation, 2983 (46%) were categorized as “positives” based on their having been classified as “active” in the original publication.
High-throughput screening data on CYP2D6 inhibition comes from PubChem AID 1851 [29, 34] provided an example of an unbalanced real-world data set. Here the negative class was comprised of compounds for which no significant inhibition was seen at any concentration of the test compound (“activity outcome” = 1 [34]) or for which the AC50 was ≥ 10 μM. The positive class was made up of compounds for which the AC50 was < 10 μM.
An indication of titration reliability was provided for each compound and those labeled as reflecting dubious titrations (“activity outcomes” of 3 [34]) were disregarded. There were a substantial number of such unreliable titrations: 35% of the positives and 70% of the negatives for which an AC50 was obtained. Salts were split into their constituent components and the largest component based on number of atoms was retained. Organometallics were eliminated and replicated evaluations [30] were resolved by majority rule of their reliable titrations. In cases where the number of valid positive and negative entries were equal, the structure was discarded. This left a data set of 13331 observations, 1643 of which (12.3%) were categorized as “positive”. The majority (70%) of the observations were selected at random and set aside as a validation set comprised of 1158 positives and 8180 negatives. The remaining 3993 observations were used for model training and ensemble selection.
Tautomeric ambiguities for all data set entries were resolved using the pK_{a} predictions and microstate analysis in ADMET Predictor 7.0.
Methods
Models considered here were ensembles of 33 artificial neural networks (except as otherwise noted) constructed in the ADMET Modeler module of a prerelease version of ADMET Predictor 7.0, with all the networks in a given ensemble having an identical architecture, i.e., the same set of descriptor inputs and number of hidden neurons. We do not expect that the conclusions drawn here are limited in applicability by exactly how the particular ensemble models were constructed, but the general procedure followed to build them is described in some detail below.
Model construction
Data remaining after extraction of the external validation set was further divided into a training pool (further divided into “training” and “verification” sets) and an external test set. The latter is used to help identify the ensemble having the “best” architecture but is not used during the model building process itself. A grid of ensemble architectures is trained using a total of 165 networks per architecture from which the best 33 networks are selected. Each architecture uses a different combination of neurons and inputs and each network has its own training and verification set. Two thirds of the training pool was used for training and the remaining third served as a verification set. These training pool splits were made randomly and independently for each network in the ensemble.
where q(l) is the class indicator value for observation l (0 for negatives and 1 for positives) and g(l) is the output function evaluated on the input vector x_{ l }. The net effect of minimizing Obj is to drive the outputs for observations in the negative and positive classes towards 0 and 1, respectively. The class weights c_{0} and c_{1} are the fractions of observations in the positive and negative classes, respectively, resulting in the smaller class having the larger weight.
Unless otherwise indicated, 165 networks were trained for each architecture (number of inputs and number of neurons) and the 33 having the best performance (combining training and verification statistics) were retained in the final ANNE for that architecture. A matrix of architectures is trained, varying the numbers of inputs and neurons, and the architecture that provides the best, statistically significant performance is selected as the final model.
ADMET Modeler combines outputs from the constituent networks in an ensemble model in one of two ways: one is to tally positive “votes” based on each individual network’s own threshold (see above), whereas the other is to compare the averaged output to an ensemble threshold that maximizes J for the average. The former is the “voting method”, whereas the latter is the “averaging method”.
Candidate ensembles are compared on the basis of their performance on the training and test sets that they share. To ensure that the results obtained are qualitatively robust, experiments should be run across a range of training pool/test set splits, a range of architectures and a range of random number seeds – as was done for the present study.
Confidence estimation
The distributions f and e of predictions and predictive errors, respectively, are calculated as functions of the number k of positive votes for compounds in the training pool, which ranges from 0 to K (the number of networks in the ensemble; K = 33 unless otherwise noted). A continuity correction [25] of 0.5 error or 1 prediction is added to the respective counts for each value of k to help compensate for undersampling (see above). As a result, the observed contingent error rate is e(k)/f(k) = 0.5 for tallies with no predictions, 0.5/2 = 0.25 for tallies having a single correct prediction, and 1.5/2 = 0.75 for tallies with a single incorrect prediction.
where MR is the overall misclassification rate for the training pool. Confidence in the prediction is reported as 1 – u(k). In the case where the confidences are the same for all compounds of a given class, this reduces to the positive or negative predictive values for the respective classes.
Descriptors
Of the 366 molecular descriptors generated by default in ADMET Modeler, the analyses presented here used a subset of 221: substructure counts (e.g., nitro, amide, ester groups, etc.) were omitted to maximize the size of the applicability domain, as were molecular weight, total bond count, number of hydrogens and four other descriptors. The latter group was dropped because they were highly correlated with more informative ones. Other descriptors were set aside for individual analyses if they had a coefficient of variation less than 1%, failed to be nonzero in at least 4 cases, or had absolute correlation coefficients with one or more other descriptors above 0.98. The number of descriptors passing those filters in each case ranged from 172 to 183 and included: counts of common element and bond types, rings and molecular volumes; electrotopological and connectivity indices; topological size measures; molecular and partial atomic charge values; polarizabilities; and topological autocorrelation vectors of various atomic properties such as partial charge and Fukui indices.
The ranking of descriptors was determined using the Input Gradient option in ADMET Modeler, wherein candidate descriptors are ranked by analysis of their response gradients. Trial neural networks with a specified number of hidden neurons (here, 1 to 6) are built using all descriptors, then the analytical sensitivity gradient is extracted from that network for each candidate descriptor. Then, a series of ensemble models are built for a given number of neurons in which the number of descriptors is progressively increased, selecting them based on the ranking assigned by the Input Gradient procedure. The resulting Input Gradient descriptor rankings are thus dependent on the number of neurons used.
Endnotes
^{a}The means for the error beta binomials for the logP2 and Hansen data sets are very close to 16.5, and shifting the voting threshold has little effect.
^{b}Models whose names differ only by a terminal letter have the same component networks but have different classification criteria at the ensemble level.
^{c}To avoid ties, the actual threshold is calculated from the theoretical threshold set to floor(mean)+0.5.
^{d}Note that ${\overline{\mathit{u}}}_{0}$ = 1 – (negative predictive value) and ${\overline{\mathit{u}}}_{1}$ = 1 – (positive predictive value).
Declarations
Acknowledgements
Dr. Jinhua Zhang prepared the Ames mutagenicity data set for use, including substantial structure curation.
The work described here was originally presented in part at the 6 ^{ th } Joint Sheffield Conference on Chemoinformatics, 22–24 July 2013.
Authors’ Affiliations
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