Interpretable ontology extension in chemistry

2.1. Text-based approaches

Text-based ontology extension systems are largely based on the lexical features of ontology classes (e.g. names and synonyms), using sophisticated tools to match those to an available corpus of text. Artificial intelligence is used to analyse corpora of relevant literature in order to extract important terms and their relations. For example, in [2] a learning system is proposed that is able to identify entity mentions in a corpus of text (e.g. PubMed publications), recognise those that could be added to a given ontology, and suggest parents for them. Other approaches use the ontology as a seed to identify terms that are important for the target domain [2,5,29,30,40,45,54].2

Instead of relying on text corpora, therefore, our goal is to rely only on the content of the ontology that is being extended, in particular on the structured annotations it contains. The research question becomes: Given the design decisions by the ontology developers, how would they extend their ontology to cover a novel entity? That is, based on the internal structure of an ontology and its structural annotations, how can we predict, for a given new class, subsumption and other axioms that connect the new class to the ontology? Techniques answering these questions should be based on complex features of the existing ontology classifications and associated data. Our goal is to use a manually created ontology as the core of an automatically generated extended ontology that is consistent with the manually maintained ontology and improves in its quality when the manually created ontology grows. This leads to structure-based classification of novel entities.

Chemical ontologies provide stable identifiers and a shared vocabulary for biochemical ‘small molecules’ and their functions. The ChEBI reference ontology, mentioned above, has been widely adopted and can be considered the “gold standard” chemical ontology in the public domain. It is used for informatics applications such as bioinformatics and systems biology analyses of metabolism, biological data integration, natural language processing, and as a chemistry component for semantic web applications (e.g. [17,23,24,38]).

Chemical entities are classified based on features of their chemical structures. The chemical graph formalism encodes the chemical structures of small molecules as graphs of atoms connected via bonds, which may form rings or cycles. Chemical classes can be defined based on overall patterns or sub-graphs within these structures, whose sub-graphs may themselves contain structural sub-units. Although chemical classification is typically based on structural features, full computable structural definitions for classes –which would support the use case of ontology extension through automated reasoning– are seldom formally captured in chemical ontologies, as the underlying logical formalisms are not able to encompass the full range of relevant structural features [21,26,33]. Thus, chemical ontologies typically only reflect a partial axiomatization for the chemical domain. Various extensions to the OWL language have been explored in order to allow a fuller set of structural features to be exposed to the computable classification hierarchy, including description graphs (our work [19]), logic programs [34], and nonmonotonic existential rules [33]. Nevertheless, these formalisms have not seen widespread adoption, in part as they are not supported by the wide range of tools that are available for OWL, and in part because performance remains a challenge. Thus, they have not been able to scale to the use case of extending real-world ontologies such as ChEBI. Moreover, chemists themselves often only have implicit knowledge (e.g. about which functional groups or structural patterns are most relevant for a classification) that is hard to make explicit.

On the other hand, a rich algorithmic toolkit for structural definitions of chemical classes has arisen in the cheminformatics domain, based around generalisations of the chemical graph formalism for structures. Consequently, several attempts have been made to combine weakly axiomatised chemical ontologies with fast cheminformatics (algorithmic) chemical graph operations in hybrid systems that are able to expose some of the chemical knowledge in ontology axioms, but ultimately perform performance-intensive operations such as graph substructure or superstructure matching algorithmically, that is, outside of the ontology. One of the first such attempts was “CO” [15], an ontology of 260 classes based on combinations of chemical functional groups generated with the cheminformatics “checkmol” software. Later, we have developed this into the more complete approach reported in [9] which used a custom general molecular fragmentation algorithm to enumerate all the parts of each molecular entity and assert those as axioms in a resulting OWL ontology. However, this strategy quickly creates a combinatorial explosion of content, which becomes inefficient as the size of the knowledge base grows.

OPEN IN VIEWER

Fig. 1.

The class of naphthionic acids as represented in ChEBI. The bottom of the picture shows the structural annotation with SMILES. These annotations will be used by our system to predict subsumption relations.

The SMILES language is a chemical line notation widely used for compact chemical structure representation in cheminformatics, in which characters representing atoms are laid out in a sequence in which adjacency typically represents a chemical bond [52]. Thus, the SMILES string ‘CCC’ represents three Carbon atoms bonded together, such as in the small hydrocarbon molecule propane. This representation of chemicals has been used as a foundation for numerous chemistry-based ML approaches and other chemistry based systems. The ChEBI ontology uses SMILES strings as structural annotations for classes of molecules as shown in Fig. 1.

The SMILES language has an extension, SMARTS, which allows ambiguities and patterns in, and logical combinations of, chemical structural elements, to be represented in a way that allows for the definition of classes of chemicals. This language has been used to define chemical classes in chemical ontologies such as OntoChem’s SODIAC [7] and more recently the ClassyFire application [13]. At the time of writing, ClassyFire is the state of the art tool for structure-based chemical ontology classification, in terms of size (9,000 definition rules, and an associated ontology of 4,825 classes) and adoption, and is used in the automated extension of the ChEBI ontology. However, ClassyFire does not harness logic-based automated reasoning: even though it uses an ontology to structure the classes that it supports, the SMARTS chemical class definitions are not integrated with the definitions of ontology classes, nor are the rules for selecting the most appropriate parent when several structural matches are obtained integrated into the ontology. Thus, the tool operates as an algorithmic “black box”, in that the rules it uses are not semantically accessible, the associated chemical ontology still has to be maintained manually, and updating the integrated knowledge system can only be accomplished by updating the custom software suite. Learning approaches promise to overcome these problems.

Deep learning has been used in chemistry in various ways, e.g. for protein structure prediction, drug design, property prediction and even synthesis planning [12,35]. The DeepChem library [42] is an open-source machine learning framework that has gained widespread adoption in the life science community as it offers a variety of tools and algorithms to facilitate chemical property analysis. MoleculeNet [53], which serves as a benchmark and curated dataset collection for molecular machine learning, is also released as a part of the DeepChem library.

Deep learning has also been used for classification of molecules in chemical ontologies (which in turn can be used for ontology extension). In [25], a back-propagating artificial neural network is applied to classify natural products, that is, secondary metabolites largely of plant origin. Named NPClassifier, it is trained on a dataset of around 73,000 natural products sourced from public databases including Pubchem, ChEBI, and the Universal Natural Products Database. The hierarchy into which these molecules were organised consisted of three hierarchical levels: 7 Pathways, 70 Superclasses, and 653 Classes. Rather than training a single model for the full prediction task, they used three single-task models – one model for each of the classification hierarchical levels. They report promising performance in a direct comparison to ClassyFire for a selection of classes. However, the restriction to only natural products (a subset of organic molecules) and to only three hierarchical levels addresses an artificially simpler task than the general problem of classification in chemical ontologies, where classes can be arranged in a hierarchy of arbitrary depth and reflect a wider chemical diversity.

In [14], machine learning was used to predict class membership directly from mass spectrometry features in an untargeted metabolomics study. This is an important use case, as in untargeted metabolomics there are often many features which relate to ‘unknown’ molecular entities and thus are not mapped to defined molecular entities about which metabolic information is known, however, they may nevertheless share detectable chemical classes. In this effort, the chemical fingerprint was used as an intermediary structural representation for learning purposes: support vector machines were used to predict chemical fingerprints from mass spectrometry features, and a deep neural network was then used to predict class membership from the resulting fingerprints. However, their system predicts class membership only for a subset of the classes belonging to the chemical ontology underlying ClassyFire, and moreover does not attempt to extend the ontology itself.

Our recent work explored the applicability of machine learning and deep learning to chemical ontology extension based on chemical structures [20]. In order to allow machine learning from the existing ontology, which is inherently unbalanced, we introduced an ontology sub-sampling approach which selected a specified number of members in a balanced way for a subset of ontology classes. We then used the associated chemical structures as inputs to train a multi-label classifier able to predict ontology class membership based on chemical structural features. In [20], we compared several different families of “classical” classifiers including logistic regression, random forests, and support vector machines, as well as a deep neural network in the form of a long short-term memory network (LSTM). We also compared chemical fingerprint-based inputs and inputs based on embedding the SMILES representation of chemical structure. In this study, we found that the LSTM was the best-performing approach, clearly outperforming also ClassyFire. Moreover, no single approach gave the best results under all conditions. The performance of the LSTM was satisfactory as a whole, but several specific limitations were identified. In particular, the model failed to provide any prediction for a certain subset of input molecules. Furthermore, LSTMs embed their inputs into internal representations that are not interpretable.

The current contribution harnesses two Transformer-based architectures and describes how the attention weights of the resulting model can provide insights into how the model made its decisions. Furthermore, by using transfer learning, a broader applicability of this data- and compute-hungry method becomes computationally more feasible. One of our Transformer models is based on the ChemBERTa [10] architecture. This chemistry-focused Transformer-based architecture has been successfully employed for toxicity prediction. In the context of this work, it has been trained for the first time on the structural annotations of an existing chemical ontology. The learning method biases the classifier towards the ontology’s internal structure, yielding a model that is in line with the domain experts’ conceptualisation as represented in the existing ontology. The resulting model is then used to integrate previously unseen classes into the ontology.

Our training setup uses four different datasets. These datasets are sourced from the ChEBI ontology or from the PubChem database as depicted in Figure 4. Whilst PubChem has a much larger variety of different chemicals, molecules are not necessarily associated with their chemical classes as they are in ChEBI. Therefore, molecules that have been extracted from PubChem cannot be used for the fine-tuning task, as this would require chemical classes as labels. It is however possible to harness the diversity of the PubChem database during the unlabeled pre-training task and additionally it provides a good source of additional chemical structures for manual evaluation.

OPEN IN VIEWER

Fig. 4.

The data generation process.

To test our use case of ontology extension with a dataset of chemicals not yet present in ChEBI that is based on a plausible real-world use-case, we selected all chemicals from PubChem which have a hazard class annotation ( n = 152.205 , as of October 2021) which therefore can be assumed to be biologically relevant and well within the target scope for ChEBI inclusion, but which as a group are not yet well represented in ChEBI. We then excluded any which were already present in ChEBI, which reduced the overall number to 140,913. This collection of molecules serves as an exemplar of a set of molecules which would typically form the use case for ontology extension, for example in a scenario where an environmental pollutants study needs to interpret measurements of sets of molecules in different locations and would like to get an overview of their structural class representation and associated bioactivity annotations in ChEBI.

We also randomly sampled 100,000 molecules from the broader PubChem database and merged the result with the previously discussed “hazardous” dataset and all molecules from the ChEBI ontology. The resulting Mol-Pretrain dataset has been used to pre-train the final ELECTRA model.

Finally, the fine-tuning is based on two different datasets, both of which have been sampled from the ChEBI ontology. To use the existing ontology classification as input to the learning task, the ontology first has to be transformed into an appropriate form. The ontology classification is inherently unbalanced, as different classes have different numbers of members and are partially overlapping. In previous research, we explored different more traditional approaches [20]. These techniques were often more susceptible to imbalances in the dataset. It was therefore necessary to define a sampling strategy to select leaf node entities and classes to minimize the impact of imbalances and overlaps on the training. As described in [20], we first order the classes by size ascending, then select 100 distinct members for each class without selecting the same member for any two classes even if in practice the classes do share members. In order to be able to compare our results to our earlier findings, we have used the same dataset4

OPEN IN VIEWER

Fig. 5.

Distribution of members per class, ordered from the classes with the lowest to the highest number of members. Top: member counts in the dataset ChEBI 500 . Bottom: member counts in the dataset ChEBI 500 + .

In our past work, we explored methods that were sensitive to such imbalances and required less data [20]. This work focuses on deep learning models that have a higher number of trainable parameters and, therefore, require a larger training set. To address this, we created a second dataset without sub-sampling only 100 members for the selected classes – including the same classes as in [20], but for each class including all possible molecules that belong to this class in ChEBI. The resulting ChEBI 500 + dataset shows greater levels of imbalance and overlap, but features almost twice as many training instances. By using both datasets, we are able to contrast the detrimental effect of imbalances vs. the positive effect of increasing the dataset size for learning.

Tokenization is a pre-processing step to create a vocabulary (a set of unique tokens) from textual data. These tokens can be generated on different levels of the textual inputs. In this work, we will use two different tokenisation approaches (BPE and CHEM), which we will introduce in the following section. CHEM is a variant of one of the most common tokenisation methods: Character-level tokenisation, which splits texts at the character level and aims to embed these individually.

However, individual characters do not carry any chemically meaningful information. Therefore, the CHEM tokenizer parses the SMILES string according to its concrete syntax and uses the resulting syntactic categories as tokens. This means that tokens consist of single atoms including their respective charges and isotopes (e.g. “[Br]”, “[B]”, “[Fe2+]”), bonds (e.g. “-”, “=”) and ring and branch references. Figure 6 shows an example of this tokenisation.

However, as chemical classes may consist of many atoms, this low-level encoding will lead to large input sizes, which may increase the computational cost of training deep neural networks. Embeddings of whole words lead to smaller input sizes, but cannot handle words that have not been seen during training. Sub-word tokenization strategies, such as Byte Pair Encoding (BPE), aim to find the middle ground between these approaches. They break a text sequence into sub-words that can then be recombined to generate embeddings for previously unseen words. The BPE algorithm begins by counting the number of times each character pair appears in the dataset. After each iteration, the most frequently occurring pairings are merged and added to the vocabulary. For the next iteration, the two characters in each pairing are combined and considered as a single unit. The performed merge operations are stored and used when the tokenization algorithm confronts an unknown token. So, by considering the possible merges for the character pairs in an unknown token, this unknown token can be broken into smaller known tokens.

While the BPE technique performed well on our classification task, its tokenization strategy of grouping certain character pairs does not allow deeper insights into the influence that some singular atoms had on the model’s predictions. Thus, given a token OCB it would not be possible to ascertain whether the model based its prediction on Oxygen, Carbon, Barium or a combination of those. Further, since the BPE tokenizer is based on subword frequency, it does not respect the intended semantics of the chemical abbreviations. E.g., BPE might treat in OC(CCl)CCl the occurrences of [CC] as a token, in spite of the fact that in CCl the first C represents a carbon and Cl represents a chlorine atom, and the l on its own has no meaning. Another downside are the kinds of tokens that have been derived by the BPE tokenizer. The token vocabulary frequently contained SMILES specific reference characters that capture branches, cycles or molecule charges resulting in chemically infeasible tokens. Figure 6 exemplifies the tokenization of a SMILES string. It can be seen that the BPE tokenizer merges a charge indicator (“-” and a adjacent double bound into a single token (“-])=”). The chemical tokenizer, on the other hand, groups them with the corresponding atom.

OPEN IN VIEWER

Fig. 6.

Comparison of BPE tokenizer (top) and chemical tokenizer (bottom).

To train the model, we used two GPUs (GeForce GTX Titan X, 12 GB GDDR5). Table 1 shows the hyper-parameters for our model. The parameters for RoBERTa were based on the respective publication [10]. Electra then derived from that configuration, the number of attention heads and size of hidden layers were derived from the model’s defaults. Both models require different kinds of pre-training. RoBERTa was pre-trained on a dynamically masked set of SMILES strings and ELECTRA on SMILES with randomly substituted tokens for 100 epochs each (unsupervised). As discussed in Section 3, the pre-trained model provides a good starting point for training a model on a related task. This starting point incorporates the trained weights of the model. Furthermore, we validated the model on a separate dataset after each training epoch. The validation during training has no effect on the model’s trained weights, nevertheless, it helps in adjusting the model’s hyper-parameters. For the final multi-label classification task, we loaded the pre-trained model and trained it for 100 epochs with the class labels (supervised). The respective setup that performed best on the validation set has then been selected for further analysis. Figure 7 (a) shows the train and validation loss during the fine-tuning step. Similarly, Fig. 7 (b) shows the F1 score after fine-tuning.

OPEN IN VIEWER

Fig. 7.

Train and validation results for ELECTRA on ChEBI 500 + : (a): loss for fine-tuning (class prediction) of a model that has been pre-trained on the same dataset. (b): F1 score for fine-tuning (class prediction).

We applied the model to predict parent classes amongst the 500 ChEBI classes on which the model was trained, for the 140,913 classes in the dataset of hazardous molecules extracted from PubChem. Subsequently, we created a composite ontology consisting of (a) the subset of ChEBI corresponding to our 500 classes and the additional classes needed to complete their classification hierarchy to the root of the ontology, (b) together with the newly created classes for each input hazard molecule, connected by (c) asserted subclass relationships to those classes to which the model predicts they belong. We are then able to use logical reasoning to validate the extended ontology together with the set of disjointness axioms that is associated with ChEBI (as described in [16]). Disjointness axioms specify classes that should not overlap or share any members. They are thus useful to detect invalid predictions: in cases where multiple superclasses have been predicted for a single molecule spanning classes that are flagged as disjoint, at least one of the predictions is incorrect.

4.1. Model training and evaluation

OPEN IN VIEWER

Fig. 8.

Part of the extended ontology. Existing subsumption relations (black) have been enriched with new subclasses, shown with dashed borders. Correct subclass predictions are depicted with cyan, dashed arrows, while red, dotted arrows indicate misclassifications.

The presented model takes a given class of molecules, represented by a SMILES string, and assigns the corresponding superclasses from the CHEBI ontology. Figure 8 shows an illustrative example of the result of this process.

For our evaluations during and after training, we used the F1-score as the main measure. The F1 score is defined as the harmonic mean between precision and recall. These three metrics may be computed in different ways depending on the averaging scheme: (1) micro: collects the total number of true positives, false positives, and false negatives for each individual (i.e. each data row) and calculates the overall metric score as the average over all rows. (2) macro: calculates the score for each class and then computes their average. Table 2 and Fig. 9 compare the results of the current model with the previously obtained results for the LSTM model from [20] and the RoBERTa model from [36]. We saw an improvement in performance compared to the LSTM model both when we look at the distributions of values for the molecule-wise F1 scores (Fig. 9a) and for the class-wise F1 scores (Fig. 9b). A statistical comparison of the overall F1 score distributions shows that the difference in F1 scores is statistically significant ( p < 0.001 , Fig. 9c). A comparison to the RoBERTa model did not show statistically significant differences in predictive performance, but the ELECTRA model converged faster.

The raw output values of our model are the probabilities of a sigmoid function. Therefore, a threshold value must be applied to these probabilities to produce a binary vector, indicating the final classifications. These results are based on the classification threshold value of 0.5. The precision – in our classification task – shows the ability of the model to not wrongly assign a label to a molecule, while the recall score reflects the model’s capability to discover all labels that were assigned to a molecule.

OPEN IN VIEWER

Fig. 9.

F1 scores on test fragment of the ChEBI 500 dataset. (a): Kernel density diagram based on the molecules. (b): Histogram diagram based on the classes. (c): Boxplots for the F1 scores of all 500 classes. A statistical test comparing the two class-wise F1 scores distributions yields a p-value of less than 0.001, indicating the distributions significantly differ and that Electra (blue) outperforms the LSTM model (red).

OPEN IN VIEWER

Fig. 10.

Top: Stacked bar chart for the number of false negatives (orange) and true positives (teal), Bottom: Bar chart for the number of false positives. Both plots are based on Electra’s performance on the test portion of the ChEBI 500 + dataset. Classes are sorted as in Fig. 5.

As can be seen in Fig. 10, some of the predicted subclass relationships are correct according to ChEBI, while others are incorrect. Among the classes in the predictions, some have a larger number of false-positive classifications than others. This aligns with our analysis from previous work, discussed in detail in [20], that has shown that certain classes were harder to predict, often due to specific structural features such as cycles and aromaticity. The performance for smaller classes (those with fewer members in the underlying ontology) worsened compared to the ChEBI 500 dataset, but the performance improved for larger classes (those with a larger number of members). The model shows good average performance, as indicated by the overall F1-score of 0.8 for the ChEBI 500 dataset and 0.82 for the ChEBI 500 + dataset. Our experiments have shown that there are no significant differences in classification performance between RoBERTa and ELECTRA, yet, the latter model has shown faster convergence [11]. The F1 scores in Table 3 show that, contrary to our previous research [36], the BPE tokeniser shows a slightly better performance with ELECTRA. This, however, comes at the expense of interpretability because the frequency based aggregation results in chemically infeasible tokens. We will therefore limit further discussions to a combination of ELECTRA with chemical tokenization. The performance of this model is sufficient for use as an automated classification system. Additional features, such as specific warnings for classes that are known to have a higher rate of errors during classification, may further improve usability and trust in the system.

Self-attention in Transformer-based models enables the model to explore several locations in the input sequence to produce a better embedding for the tokens. As a result, the embeddings encode different contextual information for the same token in different positions (and different sequences). The architecture of the RoBERTa and ELECTRA models contain a stack of Transformers’ encoders, each consisting of multiple attention heads. Since the attention heads do not share parameters, each head learns a unique set of attention weights. Intuitively, attention weights determine the importance of each token for the embeddings of the next layers [50]. In this sense, visualizing the attention weights of Transformer-based models helps to interpret the model with respect to the relative importance of different input items for making classifications [49]. While the benefit of attention visualization may be limited in explaining particular predictions, depending on the task, attention can be quite useful in interpreting the model’s overall predictions [37,41,47]. In fact, attention heads can reveal a wide variety of model behaviors and some of these heads may be more significant for model interpretation than others [49].

We examined, for our ELECTRA model, how attention corresponds to different chemical structural elements, at both the token and molecule level. Figures 11 and 12 illustrates a selection of the attention weights within the model when processing the molecule naphthionic acid (illustrated). Darker lines in the network plot in Fig. 12 indicate a stronger attention relation between tokens. Similarly, darker green in the molecule plot indicates that higher attention was paid to that particular part of the molecule.

ChEBI specifies that this molecule is a subclass of arenesulfonic acid (CHEBI:33555), defined as a sulfonic acid that features one or more carbon rings. These defining structures are attended to by our model, when it classifies naphthionic acid correctly as a subclass of arenesulfonic acid. The left column in Fig. 12b shows the attention of the model from the ring index “2” at position 8 to the fused carbon ring structure. Those parts of the molecules to which the system pays high attention when visiting ring index “2” are also highlighted in 11a. The right column in Fig. 12b and, respectively, Fig. 11b show the attention of the model to the sulfonic acid group from the sulfur atom “S” at position 17. These structures are essential for the correct classification of the given molecule as arenesulfonic acid. Visualisations such as those in Fig. 11 provide a representation of the attention structure that is more intuitive for chemists, and provide a sort of visual explanation for the classification.

OPEN IN VIEWER

Fig. 11.

Some attentions for naphthionic acid, projected to the molecule.

OPEN IN VIEWER

Fig. 12.

Attention relations for naphthionic acid.

The middle column of attention weights in Fig. 12b shows that the model also paid attention to a substantial portion of the molecular structure from the nitrogen atom at position 0. Nitrogen is the essential element in amino compounds. As there were other amino classes that were part of the 500 selected classes, such as amino monosaccharide (CHEBI:60926), glutamic acid derivative (CHEBI:22693) or ethanolamines (CHEBI:23981), it is possible that the model needs to analyse larger parts of the molecule in order to rule out those possible candidates for superclasses.

We also found that some universal patterns emerged during classification. The attention distribution in the first layer was almost homogenous. The second and third layer focused mostly on direct neighbourhoods (preceding and following tokens) within the SMILES string, as depicted in Fig. 12a. Attention to neighbourhood tokens is important for understanding how general chemical connectivity within molecules is specified in the SMILES language. The fourth, fifth and sixth layer focused on larger structures, as shown in Fig. 12b.

The visualisations that we presented in this section provide a useful way to interpret the attention mechanism of the model, because in the case of true positives or true negatives, they enable a chemist to check whether the classification by the model was indeed based on the chemically salient substructures of a molecule. If that is the case, it provides some validation for the assumption that the model learned some chemically meaningful distinctions. Analogously, if a molecule is classified wrongly, because the model fails to pay attention to the presence of one of the relevant substructures (e.g., a false negative classification), then the visualisation of the attention mechanism is helpful to understand which of the salient substructures the model fails to recognise. A presentation of these predictions and visualisations to a group of chemists informally received enthusiastic confirmation that these visualisations provide meaningful insights into the operation of the system accessible to domain experts. We therefore expect the visualisations presented here to improve the ontology development process. However, our approach is limited to the visualisation of attention to the presence of substructures. This is a significant limitation because some chemical classes are defined by the absence of certain structures, which our approach cannot visualise.

To evaluate the performance of the model on never-seen chemicals, we applied it to a set of 140,913 SMILES extracted from PubChem that had ‘hazard class’ annotations and were not present in ChEBI.

OPEN IN VIEWER

Fig. 13.

Left: Bar plot indicating how many molecules were assigned a particular number of class predictions, for example the bar at 0 indicates that 41,097 molecules were assigned no class prediction. (right) Scatter plot indicating the number of molecules predicted to belong to each of the 500 ChEBI classes in the prediction task (ordered), with the rolling average indicated by a line.

Among these, we found that 29% (41,097) – almost a third – of the input molecules were not assigned any class at all (Fig. 13, left), which can be regarded as a ‘don’t know’ response from the model. Assessing these molecules reveals that many contain isotopic SMILES with unusual atoms such as 13C Carbon isotopes or 2H Hydrogen isotopes, not present in the training dataset. Others had explicit charges, complex branching structures and explicit stereochemistry, all attributes that are poorly represented in the training data. Although a high percentage, this result is consistent with our earlier findings with similar models and offers a good motivation to connect deep learning-based models together with other approaches in ensembles so that if the learning-based model offers a ‘don’t know’ response, the system as a whole can still offer a reasonable prediction for every input molecule.

The remaining input molecules were assigned to at least one class, and amongst those 57,039 were assigned to multiple classes, including one molecule that was assigned to 13 distinct classes (Fig. 13, left). The distribution of molecules into classes is spread quite well across the 500 classes with which the model is trained (Fig. 13, right). Figure 14 shows a subset of the ChEBI hierarchy illustrating the assignment of molecules to classes, with colours indicating how many molecules were assigned to each class (red = fewer, green = more). In general, the classes lower down in the hierarchy received fewer molecule assignments, as would be expected. The full visualisation of all the 500 selected classes is available at Zenodo.5

OPEN IN VIEWER

Fig. 14.

Zooming in on a subset of the predicted classes, coloured by how many molecules are assigned to that class with red = fewer and green = more. Dark red = fewer than 10, Dark Green = more than 100, Orange = 10–100, Grey = not included in the 500 predicted classes but included in the visualisation for hierarchical completeness.

We then created an ontology extension from the subset of ChEBI classes included in our model with newly added classes based on the classified input molecules. Each input molecule is assigned a class in the extended ontology and the predicted classes are asserted as superclasses. The extended ontology is available at Zenodo.6

We combined this extended ontology with the disjointness axioms between chemical classes in ChEBI [16], and then tested for consistency using the HermiT reasoner in Protégé. Most of the predicted classifications were consistent, but we found that 5 of the input molecules’ predicted classifications resulted in a disjointness violation. These were caused by:

As a final step, we then removed those 5 molecules which had conflicting classifications from the automated ontology extension, resulting in a consistent ontology extension. The extended ontology was manually inspected for correctness and no further problems were detected.

5.1. Ontology extension and interpretability

ChEBI already makes use of an automated tool to extend its coverage beyond the manually curated core, namely ClassyFire, a rules-based system. This approach has limitations, notably that ClassyFire is structured around a different chemical ontology with only a partial mapping to ChEBI, and ClassyFire’s rules are manually maintained.

The deep-learning-based approach that we presented can overcome the limitations of rules-based approaches by allowing dynamic creation of classifiers based on a given existing ontology structure, and can be integrated into the ChEBI development process in the same way. The resulting system can then be used to integrate the given class into the ontology and translate the classification results into subsumption relations. By applying this approach to all classes in the dataset of hazardous chemicals, we were able to extend ChEBI automatically with an additional 99,816 classes. Yet, for optimal applicability, the approach must meet certain quality criteria. Ozaki [39] defined six goals for ontology learning, which we use to structure our discussion of our results.

Handling of inconsistencies and noise Our model is trained on information that originated solely from the ontology itself. This design decision eliminates external sources of inconsistencies and noise compared to approaches that utilise text corpora. The comparison of the F1 scores in the table in Fig. 9 shows that this classification outperforms the current state-of-the-art approaches – including the formerly leading LSTM-based model. In particular, for those chemical classes that were the most challenging in the previous approach, the current approach performed almost twice as well, as illustrated in Fig. 9 (b). It should be noted that there nevertheless remain some chemical classes that perform worse than others. For example, classes that are based on cyclic structures pose challenges, as their information may be scattered around the respective SMILES strings. Alternative input formats and network architectures may be explored in the future to better handle these structures. The model may also benefit from a larger amount of data. The distribution of class memberships depicted in Fig. 5 indicates that the dataset features some classes far more often than others. These classes are more prominent, often by virtue of being higher in the ontology subclass hierarchy and, therefore, represent broader classes of chemicals that may share members with other classes. Such an imbalance can skew the training in favour of those classes. Different sampling and regularisation techniques may be explored in the future to address this issue.

Unsupervised The presented approach is a variant of ontology extension. The ontology is therefore a mandatory input, from which the information that is needed for the ontology extension is extracted. The resulting dataset includes labels for each molecule. Strictly speaking, it is thus a supervised learning approach. However, these labels are extracted fully automatically from the input – the ontology. Therefore, no additional annotation by experts or other manual data pre-processing is necessary.

Human interaction As the ontology is extended automatically, no interaction is required.

Expressivity The system extends the given ontology using the same ontology language that has been used to build it. ChEBI is developed as an OWL ontology, which comes with expressive OWL-DL semantics. The extension adds classes and additional subclass relationships, which only uses a small element of the OWL-DL language, but the final extended ontology includes the axioms from the original as well and is verified for consistency accordingly.

Efficiency In [39] ‘efficiency’ is defined as the time it takes to build the ontology. This notion of efficiency does not translate well to a multi-level task that can be split into training and classification. This is due to differences in the times required for training and for classification. While the former task is often time consuming and elaborate, the same does not hold for the often quick classification. We, therefore, distinguish between two different types of efficiency.

Training efficiency describes the time that has to be invested in training the system for its specific task. Training of our model is divided in pre-training and fine-tuning. The pre-training with 100 epochs took around 20 hours for the ChEBI 500 + dataset and 50 hours for the Mol-Pretrain dataset. The fine-tuning with 100 epochs took around 10 hours.

Classification efficiency refers to the time that is invested during the actual classification, i.e. the time that it would take to extend an existing ontology. Classification of 6,569 chemical entities in our test dataset took around 2 minutes. The classification of more than 140,000 molecules from the hazardous dataset can be done in under 10 minutes. This lays the foundation for a classification system that can be used to classify unseen chemical in near real-time, and represents a significant performance improvement over classification of never-seen chemicals using rule-based approaches.

Interpretability The formerly best classifier was based on an LSTM architecture. This approach outperformed ClassyFire, but this performance came with a disadvantage: The reason for a specific classification was not transparent. This is problematic, because the experts that check the ontology extension need insights into the system’s decision processes in order to evaluate the classifications. An interpretable approach is therefore crucial. The attention mechanism of the RoBERTa and ELECTRA architectures that have been used in the present work helps to address this issue. Attention weights can be seen as a measure of how much focus is put on an individual token. A homogeneous distribution of attention shows that nothing has been focused on in particular, whilst a high attention weight shows that a particular token had a high impact. Figure 12b shows that structures that are important for chemical classification are attended to during the classification process. Yet, not all attention heads show the same level of interpretability. On the one hand there were clear patterns in the attention within the first three layers, but these patterns were universal and did not differ between different chemical classes. This limits the use for interpretability of these layers. Attention heads in layers four and five, on the other hand, showed a tendency to focus on distinct structures within the molecules. Visualisations such as those in Figs 11 and 12b can be used to help interpret decisions made by the model, and aid the experts during the ontology extension process. While it is a limitation of the present work that we did not conduct a formal evaluation with users of the interpretability of these visualisations, we informally presented the visualisations to two chemists who are familiar with the ontology, and received positive feedback. It should, however, be noted that not all attention can be seen as a positive indicator for the classifications that have been made. Our analysis has shown that the model is also attending to substructures that are a contraindication for certain subsumption relations. Yet, these attentions can also be valuable for human experts, as they indicate the reason for the decision of the model for why certain subsumptions were not annotated.

Our analysis shows that the presented approach achieves the goals of ontology learning stipulated in [39]. One additional issue that needs to be addressed is applicability. At the heart of the presented approach is a neural network that is trained based on the annotations of the ontology. In the same way as any text analysis approach to ontology generation is dependent on the existence of suitable text corpora, our approach requires that the ontology contains enough information to train a model to predict the superclasses of a new class. ChEBI is an ideal use case, because SMILES annotations provide rich, structured information that we could harness for training the model. Another potential application domain for our approach in biology are proteins, which are also classified based on their structures, features of which can be annotated in relevant ontologies. Moreover, our approach is not limited to ontologies with structural information represented in annotations. E.g., for ontologies in material science one could consider training the model based on the physical properties (e.g., density, hardness, thermal conductivity), which are typically represented using data properties. In short, our approach to ontology extension is applicable to reference ontologies that associate classes with sufficient information that a neural network may learn the classification criteria that the ontology developers are using.

5.2. Data sets and learning methods

The current approach has been pre-trained on a combination of different data sources. The dataset that could be used for fine-tuning, however, is still just a subset of the ontology corresponding to 500 classes, thus falling short of the full scope of the entire ontology. In principle, with a longer training time corresponding to a larger dataset, the approach could be extended to encompass all classes in ChEBI which include a sufficient number of structurally defined member molecules for training purposes. While we do not want to speculate as to what this minimum number of examples for successful training might be, as this is an empirical question, also considering that the model may require fewer examples for classes that have very unusual or paradigmatic structural features, we can nevertheless estimate that at least 50 members per class would be required, which is met by around 1,200 classes in ChEBI currently. This could be seen as a limitation of applicability of this method, as many of the classes that chemists may be interested in may have fewer members. However, targeted curation efforts could be used to add examples to important classes for any given use case. In future work we will explore the benefit of up-sampling smaller classes to expand the applicability of our approach, and we will also explore ensemble-based architectures in which more specific classes are predicted by other methods that require smaller training datasets.

It may be worth observing that the performance of the deep learning model may be evaluated differently depending on the ontology extension use case. The model gives a substantial number of ‘don’t know’ results (i.e. no predicted classification for a given input molecule) which under the current evaluation metric (F1) count negatively alongside the incorrect classifications, as they affect recall. However, for some use cases these may be treated differently: For the use case of ontology extension it may be preferable for the model to preferentially avoid incorrect classifications by returning no classification for challenging input structures, however, if the use case involves automated curation of a set of measured molecules – for example in a metabolomics assay – then this behaviour would be more detrimental. Thus, future work may explore different weightings of training metrics suitable for these different use cases.