Matching receptor to odorant with protein language and graph neural networks
Authors: Matej Hladiš
Maxence Lalis
Sebastien Fiorucci
Jérémie Topin
Venue: ICLR 2023 poster (2023)
Keywords: Olfaction
protein-ligand binding
olfactory receptors
computational biology
protein language modelling
graph neural networks

Abstract: Odor perception in mammals is triggered by interactions between volatile organic compounds and a subset of hundreds of proteins called olfactory receptors (ORs). Molecules activate these receptors in a complex combinatorial coding allowing mammals to discriminate a vast number of chemical stimuli. Recently, ORs have gained attention as new therapeutic targets following the discovery of their involvement in other physiological processes and diseases. To date, predicting molecule-induced activation for ORs is highly challenging since $43\%$ of ORs have no identified active compound. In this work, we combine [CLS] token from protBERT with a molecular graph and propose a tailored GNN architecture incorporating inductive biases from the protein-molecule binding. We abstract the biological process of protein-molecule activation as the injection of a molecule into a protein-specific environment. On a newly gathered dataset of $46$ $700$ OR-molecule pairs, this model outperforms state-of-the-art models on drug-target interaction prediction as well as standard GNN baselines. Moreover, by incorporating non-bonded interactions the model is able to work with mixtures of compounds. Finally, our predictions reveal a similar activation pattern for molecules within a given odor family, which is in agreement with the theory of combinatorial coding in olfaction.

Published as a conference paper at ICLR 2023
MATCHING RECEPTOR TO ODORANT WITH PROTEIN LANGUAGE AND GRAPH NEURAL NETWORKS
Matej Hladiš; Maxence Lalis, Sébastien Fiorueci, Jérémie Topin* Institut de Chimie de Nice, Université Côte d' Azur, UMR 7272 CNRS, France [matej h1 adi s, jeremie... (opin) funiv-cotedazan zur. fr
ABSTRACT
Odor perception in mammals is triggered by interactions between volatile or- ganic compounds and a subset of hundreds of proteins called olfactory receptors (ORs). Molecules activate these receptors in a complex combinatorial coding allowing mammals to discriminate ORs have gained attention as new therapeutic targets following the discovery of their involvement in other physiological processes and diseases. To date, pre- dicting coolecuee-inducee activation for ORs is highly challenging since 43% of ORs have no identified active compound. In this work, we combine [CLS] token from ProtBERT with molecular graph and propose tailored GNN architecture incorporating inductive biases from the protein-molecule binding. We abstract the biological process of mroeein- activation as the injection of a molecule into a protein-specific environment. On a newly gathered dataset of 46 700 OR- molecule pairs, this model outperforms state-of-the-art models drug-target interaction prediction as well as standard GNN baselines. Moreover, by incorporat- ing bonded interactions the model is able work with mixtures of compounds. Finally, our predictions reveal a similar activation pattern for molecules within a given odor family, which is in agreement with the theory of combinatorial coding in olfaction.
number of chemical stimuli. Recently,
INTRODUCTION
Mammalian sense of smell constantly provides information about the composition of the volatile chemical environment and is able to discriminate thousands of different molecules. At the atomic scale, volatile organic compounds are recognized by specific interactions with protein receptors expressed at the surface of olfactory neurons (Buck & Axel 1991). Mammalian epithelium ex- presses hundreds of different olfactory receptors (ORs), belonging to the G protein-coupled receptors (GPCRs), which constitute the largest known multigene family (Niimura & Nei (003). The recog- nition of odorants by ORs is based on the complementarity of structures and hydrophobic or van der Waals interactions which leads to low molecular affinity (Katada et al. 2005). With the excep- tion of a few conserved amino acids, the sequences of ORs show little identity. In particular, the ligand-binding pocket has hypervariable residues (Pilpel & Lancet. 1999) that relatively well conserved between orthologs. This property gives ORs the ability to bind a wide variety of molecules that differ in structure, size, chemical properties. The recognition of odorants is done according to the combinatorial code of activation (Malnic et al. 1999). Each odorant is recognized by several ORs, whereas an individual OR can bind several odorants with distinct affinities and specificities (Zhao et al.) 1998). This combinatorial code is sensitive to subtle modifications, so the response of a single receptor can have a major influence on the smell perception. Even small sequence modification could affect odorant responsiveness (Keller et al. 2007 Mainland et al. 20144). On the other hand, structural and functional modifications an odorant can abolish the interaction with a specific receptor (Katada al. 2005). and even lead to different smell perception (Sell, 2006). So far the combinatorial code of the majority of odorants remains unknown. Identifying the recognition spectrum of each OR is therefore essential decipher the mechanisms of the olfactory system and subsequently build models capable cracking the combinatorial code of activation.
"Corresponding authors.

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There is only limited number of models designed to match ligands and ORs. Namely Kowalewski & Ray (2020) follow a molecule-oriented approach and predict agonists for a subset of 34 ORs (24
wild types and 10 variants) by representing molecules via fingerprints (Morgan 1965 Klekota &
Roth 2008) and building an individual SVM model for each OR. On the other hand. Cong et al.
(2022) focus on receptors and address more complex problem of predicting active ORs for 4 given
molecules. In their random forest model, each amino acid of the protein sequence is described by 3 physico-chemical properties and the molecules by a subset of Dragon descriptors (Mauri al. 2006). In more general approach, Gupta et al. (202T) consider any OR-molecule pair and use BiL TM (Graves Schmidhuber 2005) to predict the binding of a molecule, represented by SMILES string. to an OR sequence.
In contrast, this work we use a graph and a [CLS] token embedding from protBERT (Inaagaar et al. 2021 to represent molecules and receptors, respectively. We abstract receptor-molecte binding as the injection of molecule into a protein specific environment. This is achieved by using molecular topology as layout for the message passing process and copying the protein representation to each node of the molecular graph. As a result, the redundancy protein information enables a local processing of the "protein environment" and achieves better performance than common strategy of processing the receptor and the molecule in parallel. This abstraction leads to a graph with the number of nodes depending only on the size of the molecule.
Molecules with flexible moieties can undergo conformational changes upon binding to maximize in- teractions with the receptor binding cavity. This structural adaptation modifies the strength of internal non-bonded forces. However, standard GNN architectures are restricted to the molecular topology. Thus, we have built tailored GNN architecture combining local interaction of bonded atoms, as done by standard GNNs, with multi-head attention, giving the model the ability to incorporate interactions between any pair of atoms. We show that this architecture outperforms other baselines as well as previous work on olfactory receptor- moleccle activation prediction.
Finally, we found a relationship between human odor perception and model predictions, strengthening the biological relevance of the model. The results for humans are in full agreement with the experimental work done by Nara et al. (201T). By analyzing the predictions for human ORs, we observe that the combinatorial codes exhibit large diversity. The OR repertoire contains mostly narrow receptors with several broadly-tuned ones. The results also highlight the existence of odor-specific receptors, but most odors coded in a complex activation patter..
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PRELIMINARIES
2.1 PROTEIN LANGUAGE MODELS
202 Vig jet al. 2021 Rives et al. 202T). , allowing to extract abstract vector representations of proteins. As in natural language processing (NLP), large models with millions of parameters (e.g BERT (Devlin et al.) ) are trained on vast databases of amino acid sequences (Steinegger et al. 2019 Steinegger & Söding 2018 UniProt Consortium] 2019 Suzek et al. 2007). Rao al. (2019) trained and evaluated various natural language processing models on a set of structurally relevant tasks. Elnaggar et al. (202T) went further and trained list of powerful NLP models ranging from 200M IIB parameters. Recently proposed MSA Transformer (Rao et al. using Multiple Sequence Alignment (MSA) as input rather than a single protein sequence at a time.
Recently, protein language models emerged unsupervised structure learners (Rao et al.
2021a) exploits evolutionary relationships by
AlphaFold2 (Jumper et al.) [022] Evans et al. 2021 extends the idea of using MSA and combines with an experimentally obtained protein template in an end-to-end model trained on supervised structure prediction.
2.2 GRAPH NEURAL NETWORKS
In recent years, graph neural networks (Kipf Welling 2016 Gilmer et al. 2017 Simonovsky & Komodakis 201 7 Veličković et al. 2018 Wang al.) 2018 Zhou et al. 2018 Battaglia et al.) 2018) have grown rapidly in popularity and received considerable attention in various domains such as drug design (Torng & Altman 2019), physics (Shlomi et al. (021), and chemistry (Gilmer et al.] 2017 Yang et al. 2019 Gasteiger et al. 2020b a). Chemistry is a particularly promising field for GNN applications since molecule be naturally represented as a graph G {V.&), where > is the set
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of nodes (atoms) and & the set of edges (bonds). Each node V and each edge (u, & in G have an initial vector of features, 2e and Cu,v. respectively. These feature vectors contain information about atom/edge properties such atomic number, atomic weight, bond type.
3 MODEL
To tackle the problem of recepior-molecule activation prediction we take a proteo-chemometry approach (Qiu et al. 2017) where the model estimates the probability P(m,r) that given molecule m activates a given receptor r. The input to the model is the amino acid sequence of the receptor on one side and the molecular graph on the other side. See Fig.
(a) for the model outline.
Protein sequence representation is obtained using protBERT (Elnaggar et al. 2021) pretrained on a dataset with 217M protein sequences (Suzek et al. 2007). Olfactory receptors are biopolymers of around 300 amino acids and modeling each amino acid explicitly is computationally demanding. To keep the costs relatively low, we take the advantage of the BERT's classification token ((CLS)) (Devlin et al. 2019) which aggregates information about an entire protein sequence. This way model amino acids implicitly as they contribute to the [CLS] token embedding, and at the same time, the sequence representation is reduced from roughly 300 x d to only d. where d is embedding dimension. We concatenate the [CLS] token embedding from the last 5 layers of protBERT and use this vector as the receptor input. A molecule is represented as a graph G with node and edge features in Tab. 5 Note that the representation relies solely on the molecular topology and does not take into account spatial characteristics.
The model architecture itself was designed by observing several inductive biases which emerge in molccule-receptor interactions:
Locality. The binding between receptor and a molecule relies on subtle interactions between the binding groups of both protagonists. In particular, molecules contain functional groups that are crucial for forming intermolecular bonds with the receptor. Since we use an aggregated sequence representation, and thus do not explicitly model interactions between amino acids and atoms in the molecule, we emulate this "locality" by copying the receptor embedding to each node of the molecular graph (Fig- (a)). This way the sequence representation part of the node embedding and can be locally changed based the connectivity and neighborhood properties of each node.
Non-bonded interactions. addition to bonded interactions, atoms in the same molecule interact with each other via electrostatic and hydrophobic interactions. To account for these, we the attention mechanism (Vaswani et al. (2017), ) which allows a flow of information between any pair of atoms (Fig. (b)). This is particularly relevant for flexible molecules where two distant atoms in the topology could be close in the conformation. Such approach also inherently takes into account interactions between multiple molecules and thus allows for modeling mixtures.
Receptor-molecule complex. When a molecule binds to a receptor, the two form a stable complex that can be considered single entity. To accommodate this observation, we combine the molecule and receptor inputs early in the model pipeline rather than mixing them at a late stage. This facilitates the flow of information between them throughout the model.
Combining the above observations, we propose the model architecture in Fig. Based on the locality and receptor-molecule complex observations, we inject receptor embedding into the molecular graph as additional node features, and we use the molecular topology as a layout for the message passing process (Fig. [ (a)). This approach reduces the biological problem of protein-molest molecule interaction to small-scale graph-level binary classification where a molecule is injected into a receptor environment. In this formulation, the size of the graph depends solely on the number of atoms the molecule.
Given the above abstraction, any standard GNN architecture might be applied to predict molecule- receptor activation. However, standard models do not allow for information to pass outside of the graph topology. To account for non-bonded interactions, we combine the local processing done by a GNN with the multi-head attention. To enhance the expressive power of the network, we use two separate identical GNNs for queries and keys/values. We denote these GNNs Q-MPNN for "queries" " and KV-MPNN for "keys/values". Each row in the queries and keys/values is a node embedding resulting from the corresponding GNN (Fig. (b)). Then the multi-head attention
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Graph neural network
Dense network
CLS token
BERT
MLP
N
Graph to vector operation
N
Multi-head
Add
Feed forward
Add
new
attention
Norm
network
Norm
KV-MPNN
(Xoid
Figure 1: Model design. (a) Overview of the model. The input is pair of protein sequence and molecular graph. The sequence is embedded using [CLS] token from protBERT (Elnaggar et al.) 2021) and the resulting representation concatenated to each node of the molecular graph. (b) Graph processing block. The node embeddings Xold of the input graph are first locally updated using two identical message passing neural networks (Q-MPNN and K KV-MPNN), and then pairwise interactions are modeled by the multi- head attention. This is followed by residual connections, layer normalization and feed forward network (Vaswani et al., (017) to obtain the updated node embeddings Xnew- Graph to vector operation is an ECC layer (Simonovsky & Komodakis 2017) followed by an attention pooling (Section A equation 2 Edge features are used Q-MPNN and KV-MPNN and are reset at the end of the block.
followed by layer normalization (Ba et al. 2016) and feed forward network (Vaswani et al. 2017 7). A similar approach was taken in (Rong et al. 2020). but our methodologies differ. Rong et al.) (2020) replace linear transformations in the multi head attention with graph neural networks and thus have 3 |heads * |layerss different message passing steps. Instead, we use two GNNs to construct queries and keys/values, and the standard multi-head attention with linear transformations, 2 [layers] steps, where N is the number of graph processing blocks.
model has
The final model simply consists of N blocks followed by ECC layer (Simonovsky & Komodakis 2017) and an attention pooling as the graph to vector operation, and a dense layer for final output. We chose to keep the GNNs architecture simple since there subsequent processing. We use dense layer followed by ReLU to construct messages from concatenated sender, receiver and edge features, sum aggregation and GRU cell (Cho al. 2014 update nodes
Training. To avoid overfitting we adopt similar strategy as in (Rong et al. 2020). During training, random number of message passing steps 7 is sampled from truncated normal distribution N(u, a, a, b) in each iteration of gradient descent. We use the Adam optimizer (Kingma & Ba 2015) with the learning rate schedule from (Vaswani et al. 2017) and in all experiments for 10 000 epochs. then switch to the full dataset during the remaining 2000 epochs. For evaluation and prediction. we set the number of message passing steps to μ.. In our experiments, we set = 6, a = and the truncation interval between 3 and b
train the model the first 8000 epochs we train on graphs with up to 32 nodes and 64 edges, and
Model code available here:
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4 DATA
Only limited amount of organized and curated data on OR-molecule pairs is available (Cong et al. 2022] Sharma et al. 2021 Liu et al. 2011). To fill this gap, we have gathered and curated new dataset of 46 700 unique OR OR-molecule pairs from the literature, tripling the size of the currently largest database of this kind (Cong et al. 2022). The data puts together experiments for 1237 unique sequences for 1 mammalian species with additional information on experimental procedure and varying data quality. See Supplementary material G for further details about the dataset.
Mammalian olfaction is sensitive to chirality and two enantiomers can have different smell perception. However, 21% of the gathered data are actually pairs of an OR and a mixture of enantiomers. To address this, we consider two modeling strategies for the mixtures of enantiomers: approach where we exploit the ability of our proposed model to work with mixtures and a "single" approach where mixture of enantiomers is treated implicitly, by changing the chiral property of the nodes. See further details in sections and
"mixture"
In addition, there are several biases in the data that need to be addressed during training. Label noise coming from high-throughput screening, class imbalance, and the combinatorial nature of the task. In response, we construct sample weights for each sub-problem and use weighted binary cross entropy loss where the final weights are the product of the partial weights. Further details for weights construction are discussed in Supplementary material
4.1 TEST SET
Because of the varying data quality, special attention needs to be paid the test set construction. In order to have high confidence in the results we select only pairs from the highest quality dose-response data for the test set. Unless otherwise stated, in each gross-validation run the experiments, we randomly select 30% of the dose-response samples for the test set. We run 5 cross-validation runs for all experiments.
5 EXPERIMENTS
We performed a series of experiments on the newly gathered dataset of OR-molecule pairs. In 5.1 we compare our proposed architecture with other GNNs and perform ablation studies on the inductive biases. We explore the generalization of the model to unseen receptors and molecules in Section 5.2. and in 5.3 we compare our approach with the previous studies of OR-molecule interactions. We present additional ablations and experiments on two other datasets in Supplementary material E Due to the imbalance nature of molecule-proted activations, we use Mathews correlation coefficient (MCC) as the main evaluation metric. We also report others for comparison with previous work.
5.1 MODEL.
GNN architecture We compare model with 4 standard GNN models and we experiment with additional modifications of our approach. We also compare our results with 2 state-of-the -oht-----thh-rt models on drug-target interaction prediction (DTI), namely HyperAttentionDTI (Zhao al. [021] and MolTrans (Huang et al. (2020), which based on Zhao et al. (2021) perform best on three different DTI datasets. We ECC (Simonovsky & Komodakis 2017). GGNN (Li et 2013), GAT (Velicković et al. 2018) and GIN (Xu et al.) 2019) as standard GNN baselines. GAT is particularly interesting baseline because it generalization of attention mechanism an arbitrary graph. In the first ablation, we consider dropping the non-bonded interactions observation and we replace Q-MPNN, KV-MPNN and the multi-hean attention Fig. (b) with GAT layer (Transformer GAT in Tab. [T]. This way the attention mechanism is kept, but do not allow direct interactions between all pairs of atoms, and the model restricted solely to the topology of the molecule. The second ablation is in the opposite direction. We drop the molecular topology and use only the multi-head attention by setting Q-MPNN and KV-MPNN to identity functions. Note that this reduces to the Transformer Vaasaani et al. 2017) encoder model.
As can be observed in the results in Tab. T our model outperforms the standard baselines and also proves to be the best compared to the DTT models. The ECC model failed to converge at all and its
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Table 1: Summary of results for different architectures. Transformer GAT corresponds to replacing Q-MPNN, K-MPNN and the multi-head attention in Fig. (b) with the GAT layer, and Transformer corresponds setting Q-MPNN and K-MPNN to identity functions. All ablation models have layers/blocks. Since baseline models cannot process mixtures, we use "single" approach to represent mixtures of enantiomers for all models except Ours - mixture (see Section for details).
Model
AveP
Precision
Recall
F-score
MCC
ECC
0.307 (0.02)
0.325 (0.02)
0.527 (0.03)
0.402 (0.03)
0.187 (0.03)
GGNN
0.676 (0.02
0.447 (0.02)
0.833 (0.02)
0.582 (0.02)
0.455 (0.02)
GAT
0.686 (0.03)
0.466 (0.03)
0.834 (0.03)
0,597 (0.02)
0.476 (0.02)
Transformer GAT
0.704 (0.02)
0.556 (0.01)
0.771 (0.04)
0.645 (0.01)
0.536 (0.01)
GIN
0.743 (0.02)
0.641 (0.03)
0.708 (0.04)
0.672 (0.02)
0.574 (0.02)
Transformer
0.748 (0.04)
0.654 (0.04)
0.695 (0.05)
0.671 (0.01)
0.576 (0.01)
MolTrans
0.638 (0.07)
0,402 (0.05) 0.822 (0.03)
0.556 (0.05)
0.476 (0.04)
HyperAttentionDTTI
0.737 (0.02)
0.609 (0.03)
0.773 (0.02)
0.681 (0.02)
0.584 (0.02)
Ours single 0.765 (0.02)
0.665 (0.01)
0.711 (0.02)
0.687 (0.02)
0.595 (0.02)
Ours mixture
0.780 (0.01)
0.689 (0.02) 0.698 (0.04)
0.693 (0.02) 0.605 (0.02)
Table 2: Summary of results for strategies to combine sequence and molecule inputs. Mixtures of enantiomers are treated single graph.
Mixing
AveP
Precision
Recall
F-score
MCC
Concatenation
0.687 (0.03)
0.550 (0.05)
0.729 (0.02)
0.626 (0.03)
0.509 (0.03)
Attention
0.720 (0.02)
0.604 (0.02)
0.671 (0.33)
0.635 (0.02)
0.526 (0.02)
Ours single
0.765 (0.02)
0.665 (0.01)
0.711 (0.02)
0.687 (0.02) 0.595 (0.02)
performance is poor compared to the others. The best from the baseline models is GIN with MCC 0.574, followed by GAT and GGNN. Compared simple GAT, the performance increases in the first ablation when we combine the GAT layer with feed-forward network, normalization, and residual connections. Interestingly. ignoring the molecular topology and using Transformer yields better performance than any of the baseline GNNs or than Tranformer GAT. This is surprising because Transformer has no access to the topology of the molecule and this suggests that the multi-head attention component is crucial for performance. However, combining both the multi-head attention and GNN together works the best (our model), implying that both the molecular topology and pairwise interactions are needed. Comparing to the DTT models, MolTrans performs worse than most of the baseline models and ablations. Our approach surpasses it by large margin of 0. 0.119 (0.129 with the "mixture" approach). HyperAttentionDTI performs better than MolTrans and the baseline GNNs, but is still outperformed by our approach.
Mixing is not evident which point in the model the protein and molecule inputs should be mixed. We argue that the molecule and the receptor create a complex, and since they constantly interact with each other, the information should be mixed in an early stage of the processing. We test this hypothesis against more common strategy (Du et al. (022) of late mixing of protein and molecule information. We compare our model with standard strategy of processing molecular information separately via GNN and then mixing the molecular embedding with the protein representation. This breaks the locality and receptor-molectt concatenation and attention. Concatenation is the most standard one and in this case the molecule processed without the protein information and the resulting embedding from the graph to vector operation is concatenated to the protein [CLS] token. This concatenated vector is then the input to dense network. The second strategy is to use the resulting molecule embedding as keys/values in the multi-head attention and the [CLS] representation of the protein query. This is followed by layer normalization and rred-forward network like in the Transformer block. We use the proposed blocks in Fig. [ ((b to generate molecular embedding.
complex observations. We consider two mixing strategies:
The results for mixing ablation are summarized in Tab. 2 Despite the redundancy of the protein representation in the nodes, our approach outperforms other mixing strategies. This suggests that by

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incorporating the locality and receptor-molest complex inductive biases, the model can lear to exploit the dependencies between various molecular binding groups and the receptor.
5.2 GENERALIZATION
An important and perhaps the most interesting experiment we conduct is to test the generalization of the model. 43% of mammalian ORs have identified active compound, and only 206 out of 385 human ORs have a known responsive molecule. Therefore, the prediction of active compounds for so-called orphan receptors (i.e. deorphanization task) remains great challenge. Moreover, among 6389 described odorant molecules (Castro et al. [2022) 504 have been tested so far, and finally only 336 of them are known to induce an OR response. Thus, is of major interest to identify new active molecules for ORs (i.e. expanding chemical space).
Deorphanization. Model generalization to unseen ORs is tested in two scenarios. In the first one (called random in Tab. B). several ORs are randomly chosen to be exclusively included in the test set and all their occurrences are removed from the training set. This scenario appears more realistic because deorphanized ORs are found in each OR family. In the second, more complex scenario, entire clusters of receptors are selected for the test set. The ORs are clustered according to their sequence similarity, and then all dose-response pairs for all receptors in 9 randomly picked clusters placed in the test set. In this complex case, the model has to extrapolate to proteins with dissimilar sequences. In both scenarios, we also experiment with keeping negative primary and secondary screening pairs for the selected receptors (OR-keep in Tab. 3)
Expanding chemical space. Identifying new active compounds for olfactory receptors remains challenging. To test the ability of the model to generalize to new molecules, we follow the same strategy for "deorphanization" (Molecules in Tab. 3) We either randomly select molecules and put all their occurrences to the test set, or we choose several clusters based on Tanimoto coefficient (i.e. structural similarity). See Section for the full description of the experimental setup.
Generalization results. The results of the generalization experiments summarized in Tab. 3 In the first random scenario, mainly recall is affected compared to an i.i.d. split and precision undergoes minor changes by 2-5%. Keeping non-active molecules has small impact and leads to more conservative model with higher precision and lower recall. It appears that the model generalizes poorly to entirely new family of receptors (Tab. S cluster task). This is due to the fact that subtle variations in the amino acids sequence of ORs can utterly alter its recogmition profile (de March et al.) 2018 Trimmer et al. 2019). Interestingly, even though overall MCC is low for the cluster scenario, the precision is still maintained at relatively high level with a difference of 11-15% compared to the i..dd split. From the molecule perspective, the model can generalize well to new compounds that have similar distribution to the training set. As expected, the performance decreases on entirely unseen group of molecules, yet the model still keeps the precision of 54.4%, only 14% lower than in the i.i.d. case. The recall on the other hand deteriorates by half. This suggests that the model is conservative and reluctant to predict many responsive pairs, which can be an advantage for in vitro experiments. Although some responsive pairs missed as result of lower recall, the predictions keep similar reliability across scenarios, and thus the number of laboratory hits would remain stable.
5.3 COMPARISON WITH OR- OR-MOLECULE MODELS
In Tab. we compare our work with Kowalewski & Ray 2020), Cong et al. (022). and Gupta et al. (2021). Overall, when generality taken into account, our model outperforms previous approaches. In particular, Kowalewski & Ray (2020) perform slightly better in AUROC than others, but this approach fits a separate SVM model for each OR and can only be generalized ORs that already have at least 3 known active compounds. This is a limitation for most of ORs. On the other hand, Cong et al./ 2022) took a more general approach by fitting a single model to data for 10 molecules (4 targets and 6 similar compounds). It is applicable to variety of ORs, but only for compounds structurally close to the 4 targets. Even though Cong et al. 2022) consider an easier setting with a limited number of molecules, our approach outperforms it by a large margin. Finally, our model and the BiLSTM model of Gupta et al. (202T) consider any given molecule-rcceppe pair. Our approach outperforms Gupta et al. 2021 in all the metrics and more than twice the precision.
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Table 3: Summary of results for generalization test set in case that a cluster of molecules/sequennes is not present in the training set (Cluster) and randomly chosen molecules/sequence are not present in the training set (Random). OR- keep corresponds to a case where primary and secondary screening
non-active molecules are kept in the training set. Mixtures of enantiomers are treated as "mixture".
Split
Num data
AveP
Precision
Recall
F-score
MCC
i.i.d. 1565.0
0.780 (0.01)
0.689 (0.02)
0.698 (0.04)
0.693 (0.02)
0.605 (0.02)
Random Cluster
Molecule
1320.4
0.580 (0.08)
0.544 (0.07)
0.342 (0.06)
0.418 (0.06)
0.334 (0.07)
OR 1062.0
0.558 (0.14)
0,535 (0.12)
0.132 (0.04)
0.203 (0.04)
0.088 (0.06)
OR- keep
1062.0
0.625 (0.03)
0.576 (0.06)
0.095 (0.03)
0.161 (0.05)
0.091 (0.09)
Molecule
1056.2
0.729 (0.08)
0.657 (0.11)
0.629 (0.04)
0.638 (0.06)
0.533 (0.07)
OR 1217.8
0.684 (0.10)
0.636 (0.07)
0.491 (0.11)
0.552 (0.09)
0.417 (0.10)
OR- keep
1217.8
0.710 (0.09) 0.670 (0.06)
0.470 (0.13) 0.548 (0.11) 0.430 (0.10)
Table 4: Comparison with previous studies on OR-molecule activation prediction. ORs and Mols average numbers of unique receptors and molecules, respectively. in the test set.
Model
ORs
Mols
TNR
AUROC
Precision
Recall
F-score
SVM
32
n/a
0.80 (0.06)
0.88 (0.07)
n/a
0.77 (0.10)
n/a
R
80
4
0.91
0.74
0.52
0.53
0.53
BiLS
n/a
n/a
n/a
0.77
0.34
0.71
0.46
Ours mixture
205
237
0.91 (0.01)
0.79 (0.03)
0.69 (a.02)
0.70 (0.04)
0.69 (0.02)
Kowalewski & Ray 2020)
Cong et al. 2022
Cupta 2021
results taken from supplementary Fig. 3.
6 AGREEMENT WITH ODOR PERCEPTION
The combinatorial nature of olfaction was first hypothesized in 1973 by Polak (973)) and further confirmed by the discovery of ORs by Buck & Axel (199T). Malnic et al. 1999 demonstrated that different odorants are recognized by different subsets of ORs. Here, we refer to a subset of 385 human ORs activated by given odorant as the combinatorial code, and in this section asses biological plausibility of our approach by analyzing consistency between the combinatorial code predicted by our model and odor perception of molecules (i.e. the odor families).
We evaluate the concordance between our model and the olfactory perception as follows: given a large dataset of known odorants (Castro al. 2022), construct abstract smell characteristic of a molecule by taking an embedding of a GNN-based odor prediction model (Sanchez-Lengellng al. 2019). This model was recently shown to outperform median panelist in odor description task, and its embedding can capture perceptual similarity of molecules (Lee et al. (2022). The odorants are clustered based on their odor embedding to abstract odor families. For each family, we define an agreement score as the o-guantile of the distribution of median I distances between the combinatorial codes within the family? In other words, let A&t A& E {0. 1) 385 be the combinatorial codes for molecules i.j in family R. Then the agreement score Q2 for family k is given by
(a = ivi E k P(M 1 S q) > a)
(1)
where m denotes the median through k. When considering a percent of the most consistent codes in family k. Qu (ax) represents the largest deviation of OR responses in that family. We report several values of due to the known ambiguity of odor descriptors which depend on several cultural and personal factors of the panelists such as varying verbal expressions. current state and previous experience, or are even affected by OR mutations (Kaeppler & Mueller 2013).
Fig. 2 reports the distribution of (a) for all abstract odor families and several levels a. Most of the families have a consistent combinatorial code as illustrated by the density of Q& (Fig. 2b. highlighting
There are 205 abstract odor families and 385 human ORs.
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(a)
0.04
(b)
Percentage of clusters
1.0
0.03
0.8
Density
0.02
0.4
505
0.01
0.2
100%
0.00
50
200
350
0.0
100
150
250 350
Figure 2: (a) Density and (b) cumulative distribution of agreement scores (a(a) for several levels a.
an agreement between the results of the two independent models: odor prediction and OR activation. For & 75%, more than 60% of all families differ by less than 25 ORs and 84% by less than 50. Fig. 5 provides examples of families with different a-quantile values. Although the odor prediction model outperforms a median panelist, it is still prone to error. This may influence the quality of the clustering and consequently lead to outliers in the families, illustrated in Fig. (b).
previous vitro study on 125 odorants suggested that molecules recognized by subset ORs share the same odor quality (Nara et al., 2011). The consistency of activation patterns observed in the odor families allows us to generalize this experimental observation to all known odorants. Nara al. (2011) found that most ORs narrowly tuned with the exception of few broadly tuned ORs. Our predictions are consistent with this observation, as the results conclude that the vast majority of ORs have very narrow recognition spectrum (Fig. 4 In addition,
model is able identify
specialized receptors for some specific odor families (e.g. Fig. 66. In agreement with Nara et al. (201T), odor generally encoded in combination of multiple active receptors and the size of the code varies among odorants.
7 CONCLUSION
In this study, we transfer the biological problem of protein molecule interaction to a graph-level binary classification. We combine [CLS] token from the protein language model with graph neural networks and multi-head attention to predict active compounds for ORs. We design tailored GNN architecture by incorporating inductive biases that are observed in receptor-ligand interactions and we demonstrate that this architecture outperforms other baselines and previous works. Our results show that [CLS] token contains valuable information to characterize proteins and at the same time lowers computational and memory demands. In addition, by taking into account non-bonded interactions, the model is suited to deal with mixtures of compounds.
Although [CLS] token reduces computational costs, it comes with the limitation that cannot infer the importance of individual amino acids. In the future, we plan to explore the possibility to MSA-based protein language models explicitly model amino acids. This may lead to better interpretability with the drawbace bawk of higher computational demands. While our predictions are in good agreement with experimental results, our model does not take into account the influence of the concentration on either the perceived odor or the pattern of activated ORs. Recent experiments have shown that inhibition of ORs by odorants also part of the combinatorial code of odors and, more specifically, that the interaction between odorants in mixture modulates the activation of ORs relative that of isolated molecules (Kurian al. 202T). However, data on OR inhibition remain scarce and do not allow training a model predicting molecular inhibition.
To date, 43% of the ORs are still orphans. The prediction of the combinatorial code for all ORs and odorant molecules opens the way to the generalization of experimental results obtained on subsets of ORs or limited number of odorants. By linking activity patterns to olfactory perception, we confirm that odorants are recognized by unique subset of ORs and that these subsets specific to an olfactory characteristic. Finally, we expect this work to have an impact beyond the olfaction research. ORs also expressed in non-nat non-nasal tissues and are involved in the regulation of different metabolic functions or found in cancerous cells. Thus, they are becoming a promising drug targets, and matching ligand to specific ORs is of major importance.

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ACKNOWLEDGMENTS
We thank Jana Bocková and Jody Pacalon for helpful feedback on drafts of this paper and for insightful discussions on the activation mechanism of ORs. This work was funded by the French National Research Agency (ANR) under reference number ANR- 19- CE07-0044 (PhD fellowship to Matej Hladiš). by the Fondation Roudnitska under the aegis of Fondation de France (PhD fellowship to Maxence Lalis), by GIRACT, and has also received financial support from the CNRS through the MITI interdisciplinary program. This work was granted access to the HPC resources of IDRIS under the allocation 2022-IA 3580] made by GENCI and was also supported by the French government, through the UCAJEDI Investments in the Future project managed by the ANR under reference number ANR-15-IDEX-00 Université Côte d' Azur and the Université Côte d' Azur's Center for High- High-Peeformance Computing for providing resources and support.
The authors are grateful to the OPAL infrastructure from
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A MODEL DETAILS
In all our experiments, we consider node and bond features in Tab. 5 Molecular graphs were constructed using RDkit (Landrum 2020) from SMILES obtained from PubChem (Kim et 2020). Atomic number, chiral tag and hybridization were embedded in R22,, and Bond type and Stereo type in R
Table 5: Features of the molecular graph. Features were treated as categorical variables except for formal charge, number of implicit Hs, explicit valence and mass which are continuous variables.
Atom features
Bond features
Atomic number
Bond type
Chiral tag
Stereo type
Hybridization
Is aromatic
Formal charge
Num. of implicit Hs Explicit valence
Mass
Is aromatic
The attention pooling layer in graph to vector operation for graph g is defined as
softmaz(Xxx) x' Σ o;X2
(2)
where e Raxd is a matrix of node embeddings and w e Rdx1 are trainable parameters.
A.1 TRAINING DETAILS
Dropout of 0.1 was applied in each multi-head attention layer and 0.5 just before the output layer.
Padding was set to 32 nodes and 64 edges the first 8000 epochs and 128 and 256, respectively, in the remaining 2000 epochs. The initial learning rate was set to 0.001 and we use 6000 warm-up steps for the scheduler. The model was implemented in JAX (Bradbury et al. 2018) and FLAX (Heek) al. 2020) and trained Nvidia V100 SXM2 32GB Nvidia AT00 M4 80GB GPUs.
B ENANTIOMERS
The olfactory system of primates is sensitive to the chirality of molecules: they can discriminate between enantiomers. By definition, a pair enantiomers" are mirror isomers that are not superim- posable chemical structures. They have the same atomic composition and they share identical physical and chemical properties an achiral environment. However, they will have different properties when interacting with chiral environment (for example, polarized light or the binding site of protein). Two enantiomers can have a completely different odor, for example R-carvone and S-carvone which smell like caraway and mint, respectively. At the molecular level, these two molecules are recognized by distinct OR subsets. The specific selectivity of ORs for a given enantiomer is at the origin of the differences in perception of chiral molecules. Even if chirality has high influence on ORs recognition, the distinction between enantiomers is often overlooked in laboratory experiments. As result, 21% of our gathered data are actually pairs of an OR and a mixture of two (or more) enantiomers. Thus, it appears crucial to inject molecular chirality into our model.
Molecular graphs of enantiomers are identical and the only difference may have clockwise (CW) or counter-clockwise (CCW) chirality and one way of distinguishing them is to put chiral tag property one chiral center to CW and CCW for the other. Since the molecular graphs are identical, mixture of enantiomers graph where chiral centers have "unknown" chirality or a mixture of independent molecules. In the latter case,
their chiral centers. These
be modeled in two ways: as a single
exploit the fact that combining GNN with the multi-head attention is capable
With slight abuse of notation, we refer to both enantiomers and diastereoisomers enantiomers.
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Table 6: Screening quality. Values in each row correspond the conditional probability that the true response is positive/negative (i.e. the outcome of the dose-response measurement) given the result from the primary and secondary screening. These probabilities were estimated based on the data from Mainland et al. (215). 5C denotes the true label from the dose-response measurements.
Primary
Secondary
pos.
neg.
pos.
neg.
pos.
0.40
0.31
0.72
0.23
neg. 0.60
0.69
0.28
0.77
of modeling mixtures. Thus, a mixture of enantiomers can be treated as a graph with 2/chinal centers] identical disconnected components that differ only the chiral tag property of chiral centers.
We test both "single" and "mixture" approaches in Section E. Our experiments indicate that the two strategies have similar performance OR-molecule pairs TTb. 7 Interestingly, when evaluated limited set of complex mixtures (i.e. pairs of OR and mixture containing more than two topologically different compounds), the performance of the model trained in the "mixture" approach is higher even though the only mixtures seen during training were the mixtures of enantiomers (Tab. [). Note that the data on mixtures are limited and further experiments are needed.
C SAMPLE WEIGHTS
Data quality. Conducting an experiment with high confidence on responsiveness is costly and time-consuming. The standard procedure for testing molecule-receptor pairs is to first perform noisy primary screening where many different pairs are tested with just one injection for a single concentration. Then, promising pairs from the primary screening are further tested at several concentrations in a secondary screening. Finally, the pairs that are considered responsive from the secondary screening are tested in the most precise tertiary screening in multiple concentrations and multiple injections per concentration (i.e. dose-response curve). If neglected, varying sample quality could lead to fitting corrupted labels and impairing the model performance and generalization. Therefore, during training we employ a simple strategy to counteract this label noise. For each type of screening, we estimate probability plype that the observed label corresponds to the true label
c.c
By treating dose-response label as true we estimate plype based on (Mainland et al. 2015). where several pairs were tested in all three types of experiments (Tab. 66) Then we the diagonal elements as data quality weights for a given type and a given class y
Wquality (v, type) ptype
We the weight for dose-response samples 1. In the future, more elaborated label noise treatment could be used (see (Song et al. 2022) for a non-exhaustive list of label noise strategies).
Class imbalance. Naturally, most of the molecule-recepel pairs are non-responsive and only 6.2% of the samples in our data are responsive. To account for this class imbalance, we use standard class weights based on the imbalance ratio
Inon rresponsiie Iresponsive]
if if =
Wetass (1))
(4)
Pair imbalance. On top of the class imbalance, some olfactory receptors have been tested with many molecules, whereas others are to be explored and were tested only on few specific compounds (Fig. 77. We refer to this type of imbalance as pair imbalance and to avoid the bias resulting from the uneven distribution of experiments, we adopt a heuristic weighting scheme to down-weight extensively tested receptors and up- less-known ones. For each molecule-receptor pair, set pair imbalance weight the logarithm of the harmonic mean
pair (m, r) = In (1
K
(5)
2
[M(]]]
[R(m)
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where (r) is the number of molecules tested for given receptor r, (OR(m)) is the number of receptors tested for a given molecule m and K a constant. the experiments, we set K = 100.
The final sample weight is then given by
w(m, Y, Jype) Wquattity (V, Wypaa (pprppaamm (m,,
(6)
D EXPERIMENTAL DETAILS
D.1 GENERALIZATION
Deorphanization. To test the generalization to unseen receptors we consider two scenarios. The first one (referred as random Tab. 3). where the model predicts active compounds for unseen receptors chosen randomly, and the second, more complex scenario, where entire groups of similar receptors are unseen during the training (cluster in Tab. 3).
We test the random scenario by constructing a test set for each cross-validation run in the following way. From pairs tested in dose-response experiments, we filter receptors with at least 3 known active compounds. Then from these filtered receptors randomly choose 35% and place all rese-response pairs containing these into the test set. We then discard remaining primary and secondary screening pairs containing the selected receptors from the training (OR in Tab. responsive pairs containing these receptors (OR keep in Tab. 3 The reasoning behind keeping negative examples is that, reality, we have access to non-responsive molecules from primary screenings for all human olfactory receptors. Following this procedure, the model has never seen the selected receptors and needs extrapolate their nolecule-receptor interaction from the remaining proteins.
discard only the
Test set construction in the cluster scenario is similar to the random case, but we do not choose receptors for the test set uniformly. Instead, we cluster receptors based on their sequence similarity and then place dose-response pairs for all receptors from 9 randomly picked clusters into the test set. We then follow the same procedure of discarding either all only the responsive primary and secondary screening pairs containing these receptors from the training set. This scenario more demanding than the first setting because the model would need to extrapolate to entirely dissimilar proteins than the ones in the training set.
Expanding chemical space. Extrapolation to the unseen molecules is tested by an identical procedure as the deorphanization. The only differences are that we select molecules instead of receptors (25% molecules with at least 2 responsive pairs in random) and in the cluster scenario we cluster molecules based on their Tanimoto coefficient (i.e. structural similarity). We take compounds from randomly selected clusters. both scenarios, we then discard all other pairs containing the selected compounds from the training set.
E
ADDITIONAL EXPERIMENTS
E. DATA ABLATIONS
Enantiomers. As we argued previously, olfaction is sensitive to the chirality and modeling enan- tiomers poses a great challenge. Here, we test the two approaches to incorporate mixtures of enantiomers and their performance is summarized in Tab. 7 We report two settings where either test the model on single molecules only (i.e. discarding mixtures of enantiomers from the test set) or on the full test set. We also train a separate model the training set with mixtures of enantiomers discarded. As can be seen in the results, modeling mixtures of enantiomers explicitly as a mix of independent molecules slightly outperforms the implicit "single" approach. In both cases keeping mixtures of enantiomers is beneficial for modeling single molecules.
Complex mixtures. Several lab experiments have been performed to test a receptor with a mixture of topologically different compounds. In Tab. g we report the performance of our model on a limited dataset of complex mixtures. In particular, we investigate impact of the two strategies for treating mixtures of enantiomers on the performance complex mixtures. Although the approaches
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Table 7: Summary of results for strategies to treat mixtures of enantiomers. Mixtures of enantiomers are either modeled as mixture of distinct molecules (mixture) or as single graph with chiral centers having "unknown" chirality (single). Discard corresponds to the model trained only on data without the mixtures. Discard mix of isomers denotes a test set without the mixtures and full test set is test set containing all pairs, including mixtures of enantiomers.
Isomers
Num data
AveP
Precision
Recall
F-score
MCC
Discard mix of isomers
Discard
1425,0
0.773 (0.01)
0.666 (0.03)
0.689 (0.03)
0.677 (0.02)
0.589 (0.03)
Single
1425.0
0.759 (0.02)
0.656 (0.01)
0.715 (0.03)
0.685 (0.02)
0.597 (0.03)
Mixture
1425.0
0.778 (0.01)
0.681 (0.02)
0.704 (0.04)
0.691 (0.02)
0.608 (0.02)
Full test set
Single
1565.0
0.765 (0.02)
0.665 (0.01)
0.711 (0.02)
0.687 (0.02)
0.595 (0.02)
Mixture
1565.0
0.780 (0.01)
0.689 (0.02)
0.698 (0.04)
0.693 (0.02) 0.605 (0.02)
Table Summary of results for a small dataset of complex mixture-receptor pairs for dose-response
pairs only (ECsg) and all available data (All).
Isomers
Num data
AveP
Precision
Recall
F-score
MCC
Single Mixture
37.0
0.938 (0.05)
0.895 (0.04)
0.836 (0.02)
0.864 (0.11)
0.494 (0.10)
37.0
0.994 (0.01)
0.915 (0.07)
0.836 (0.07)
0.869 (0.02)
0.538 (0.10)
Single
112.0
0.799 (0.08)
0.716 (0.08)
0.774 (0.03)
0.741 (0.03)
0.638 (0.05)
Mixture
112.0
0.940 (0.04)
0.789 (0.10)
0.755 (0.06)
0.768 (0.04)
0.684 (0.07)
have identical training sets, the results suggest that the model trained with the "mixture" strategy outperforms the "single" approach.
Data quality weights. Label noise originating from different quality of experiments can bias the model and negatively impact its generalization. To test the label noise treatment, we experiment with discarding primary screening data (the most noisy ones in Tab. (6). with weights presented in Section C and with ignoring the label noise (i.e. a naive approach). Note that we use only dose-response data for test sets and these are assumed to have no noise.
According to the results in Tab. 2 neglecting the label noise lowers the performance. It can be observed that when using weights and when discarding the primary screening data the results are on par. This is due to the fact that most of the corrupted labels are coming from the primary screening (Teb. 6 and so discarding these cures a large portion of the label noise. Nevertheless, we hypothesize that keeping the primary screening data while taking the noise into account could be beneficial for model generalization. Primary screening experiments have relatively low cost and can cover large number of different pairs. Thus, the model can have access to large sample of the data distribution.
Table 9: Summary of results for data quality weights. Naive corresponds to equal weights. Mixtures of enantiomers are treated as a "mixture".
Data quality
AveP
Precision
Recall
F-score
MCC
Discard primary
0.754 (0.02)
0.667 (0.03)
0.724 (0.04)
0.694 (0.01)
0.603 (0.02)
Naive
0.740 (0.04)
0.631 (0.05)
0.715 (0.02)
0.669 (0.03)
0.570 (0.04)
Weight
0.780 (0.01)
0.689 (0.02)
0.698 (0.04)
0.693 (0.02)
0.605 (0.02)
E.2 PERFORMANCE ON OTHER DATASETS
In addition to -o-oecuee data, we conduct experiments on two drug-target interaction prediction datasets, namely KIBA (Tang et al. 2014) and DAVIS (Davis et al. 2011). and we compare perfor- mance of our approach with two state-of of- the-ahnn art drug-target interaction models Hyper (Zhao et al., 2021) and MolTrans (Huang al. 2020), which based on Zhao et al. (2021) perform best on the two datasets. Both KIBA and DAVIS record kinase inhibition binding affinities measured in laboratory assays. We follow data preprocessing and binarization scheme from (Zhao et al. 2021).
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Table 10: Statistics of DAVIS Davis al. 2011). KIBA Tang al. 2014) and M2OR (ours) datasets. Seqs. and Mols. respectively refer to unique number of sequences and moleucles in the datasets. Orphans is a number of sequences without any known active compound and % mols. tested per sequence and Ligands per sequence are, respectively, average portion of molecules tested given sequence and average number of known active compounds for a sequence.
Dataset
Num. pairs
Seqs.
Mols.
Orphans
% mols. tested per sequence
Ligands per sequence
DAVIS KIBA
25 772
379
68
0
100.0%
19.3
116 350
225
2068
25.0%
98.5
M2OR
46 717
1237
596
527
6.3%
2.3
(a)
(b)
Agonists Non-Agonists
Agonists Non-Agonists
Number of seqs, tested
Number mols. tested
60
300
40
200
20
0
Molecules
Sequences
(d)
1500
Number seqs. testeo
150
Agonists
Non-Agonists
Number or mois tested
1250
Non-Agonists
Agonists
1000
100
750
50
300
25
Apl pi un mr
Molecules
Sequences
Figure 3: Distributions of the number of laboratory experiments: (a) per molecule and (b) per sequence for DAVIS, (c) per molecule and (d) per sequence for KIBA. Note that DAVIS dataset contains results on all possible pairs of 68 molecules and 379 proteins.
In Tab. 10 Fig. 3 and Fig. 7 comparison between distributions of the two datasets and of our data (M2OR in Tab. TOp is reported. As be observed by shapes of the gray areas in Fig. 3 and Fig. 7 OR data have a more searce distribution of tests per sequence and per molecule and a more imbalanced class distribution than what can be observed in KIBA or DAVIS.
Table 11: Performance of HyperAttennionDT11 (Zhao et al. 2021). MolTrans (Huang et al. 2020) and our model on KIBA (Tang al. 2014). DAVIS (Davis et al. (201T) and M20R (ours) data. Results for MolTrans and HyperAttemtionTTT on KIBA and DAVIS are taken from (Zhao et al. 2021
Model
AveP
Precision
Recall
MCC
DAVIS
MolTrans
0.784 (0.002)
0.782 (0.003)
0.617 (0.004)
n/a
HyperAttentionDTII
0.839 (0.002)
0.754 (0.002)
0.780 (0.001)
n/a 0.564 (0.011)
Ours mixture
0.744 (0.015)
0.699 (0.013)
0.666 (0.016)
KIBA
MolTrans
0.708 (0.003)
0.710 (0.003)
0.645 (0.003)
n/a n/a 0.555 (0.012)
HyperAttentionDIT
0.814 (0.002)
0.689 (0.003)
0.798 (0.002)
Ours mixture
0.704 (0.010)
0.610 (0.014)
0.683 (0.005)
M2OR
MolTrans
0.638 (0.066)
0.402 (0.053)
0.822 (0.027)
0.476 (0.02)
HyperAttentionDDTT
0.737 (0.015)
0.609 (0.028)
0.773 (0.020)
0.584 (0.022)
Ours mixture
0.780 (0.012)
0.689 (0.016)
0.698 (0.042)
0.605 (0.017)
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Published as a conference paper at ICLR 2023
The performance all three datasets is summarized in Tab. I1 Overall, DAVIS HyperAtten- tionDTI and MolTrans have higher AveP value than our model. and on KIBA HyperAttentionDII performs best and approach is par with MolTrans. Our model outperforms other approaches on the OR-molecule dataset.
F ANALYSIS OF THE COMBINATORIAL CODE
0.10 0.15
0.20
0.25
Mean Tanimoto similarity
0.5
09022
Broadness
0.4
085201
GRIGG
DRS2NNS
0.3
ORIAL
GR300Y
02212
0.2
0.1
0.0
10
1334 55
Olfactory receptors
Figure 4: Distribution of the estimated broadness of human ORs. Broadness for each OR is defined as a portion of its predicted active compounds out of all known odorant molecules as given by the pyrfume database (Castro al., 2022). The olfactory receptors grouped receptor families (Olender et al. 2008) and colored by mean Tanimoto similarity between their active compounds.
(a) Balsamic, floral and spicy. a(a) ((0.5, 0.9, 1.0)) (13, 19, 32).
bb) Citrus, waxy and farty. Qui (0.5, 0.75, 0.9, 1.0)) (20, 29, 52)-
(c) Floral, woody and minty. Q (0.5, 0.75, 0.9, 1.0)) {25, 30, 58, 129).
(d) Nutry, Roasted and Meary. Q ((0.5, 0.75, 0.9. 1.0)) (48, 79, 127, 330).
Figure 5: Examples of the predicted combinatorial codes for 4 abstract odor families and 385 human F Each row is an odorant molecule and each column an olfactory receptor. Each molecule-OP pair colored by the probability of activation ranging from dark blue for no activation yellow for an active pair. The ORs are sorted in the figures in descending order from the left to right by their predicted broadness. Captions under the figures correspond to the most frequent descriptors in given abstract odor family, ordered by frequency.
FFigures for all families available here: httpss//stthbbbocm///aaaaaaatatp
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Published as a conference paper at ICLR 2023
ORZW/1
OR1OW1
ORSK1
Nutty
Nutty
Nutty 3
Woody 1
Woody 2
Woody
Figure 6: UMAP MMelnnes et al., 2018 projection of the odor embedding with estimated densities of active compounds. Each point is an embedded odorant from pyrfume database (Castro et al.) (2022) and highlighted points are molecules that belong to the abstract odor families with the given smell descriptor being 1. the most occurring one, 2. the second most occurring. and 3. the third most occurring in the family. The contour regions are estimated densities of active compounds for a given receptor. We identify specific receptors OR BRIOW1 (green) and ORSKI (blue). ORIOWI is activated by molecules with the woody descriptor whereas OR5KI by nutty compounds. OR2W1 (grey) an example of broad receptor activated by odorants with all kinds of different descriptors.
G DATASET
Our newly gathered dataset of OR-molecule pairs called M2OR for Molecule to Olfactory Receptor,
currently contains 46 700 unique pairs that were collected from 31 scientific papers (Malnic et al.
1999
Wetzel al. 1999: Kajiya al. 2001 Spehr al.
2003 Saito al. [0044] Araneda al.
2004
Sanz et al. 20055] Neuhaus et al.] 2006 Jacquier et al.
2006 Schmiedeherg et al. [200 Keller
al.
2007| Fujita al. [2007] Saito et al. 2009 Repicky & Luetje| 2009 Shirasu et al. 2014
Mainland et al. 20155] Gonzalez- Kristeller et al. [2015] Geithe et al. 2015 2016, 2017 Sato-Akuhar
al.
2016 Noe al. 2016 2017 Jones al. 2019 Yasi al. 2019 Frey et al.
2020 Jabeen
et al.
2021 Marcinek et al. 2021 Haag et al. [021] 2022 Cong et al. 2022). It combines 596
different molecules with 1237 unique sequences and in total consists of 68 837 experiments. Wild
type, variant and mutant receptors are assembled across 11 different mammal species. For each experiment, several types of information are available:
Receptor: Species, Mutation, Gene Name, Uniprot ID, Sequence. When available, primary sequence Uniprot ID is obtained from the publication or else Uniprot ID) is inferred from the gene name. We keep original gene name as was stated in the article. For mutants, Uniprot ID/Sequence corresponds to the reference sequence and the mutation is annotated in the dedicated column. Then mutated sequence should be deduced by combining the reference sequence and the mutation.
Molecule: Name, CID, CAS, InChI Key, canonicalSMILES Mixture. Compounds identified by InChI Key based on the Pubchem database (Kim et al., 2020). Distinction between mono-molecue molecula compounds, mixture, and sum of isomers made, and is derived
Data available here: https://github: httpt/chwww..ccccccceecceecc www.ch//m//cccccccocc//ootcccccc 1
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Published as a conference paper at ICLR 2023
(a)
Number edorants tested
300
(b)
Number odorants tested
300
Agonists
Secondary
Non-Agonists
250
Primary
250
200
200
100
100
50
0
Olfactory receptors
Olfactory receptors
(d)
EC50
Agonists
Number ORs tested
Secondary
Number DRs tasted
Non-Agonists
Primary
400
400
100
100
Odorants
Odorants
Figure 7: Distributions of the number of lab experiments in the M2OR dataset: (a-b) per receptor, (c-d) per molecule. Sever imbalance in the quality, number of experiments, and class distribution can be observed.
using EnumeruteSterennssomers function form RDKit (Landrum) 2020). When no InChl Key is available, canonicalSMILES is inferred from the chemical structure.
Response: Value, Unit, Value Screen, Unit_ Screen, Responsive, Nbr measurements. Value corresponds to the experiment's raw response if available and Value Screen is a tested concentration. If sample is non-responsive in dose-response measurement, Value_Scree is set the maximum tested concentration. tbr_mmeauuremnts corresponds to the number of injections for a given experiment. Responsiveness is encoded for agonists and 0 for non-agonists.
Type, Cell line, Delivery, Assay, G-protein, Co_ transfection, Assay System, Tag. Information about the type of assay done, the cell line used, the delivery method of the odorant and about the system such as the presence of G protein, tags or co-transfection of other proteins.
Bioassay:
Reference: Reference, DOI, Reference Position. Article name, authors, DOI and location where the information was found.
Table 12: Dataset statistics.
Experiments
68 110
Unique pairs
46 717
Unique sequences
1237
Unique molecules
596
Species
11
Data preparation. Some OR-molecule pairs may be tested in several different experiments with possibly different outcomes. In order to train and evaluate our model, we extracted unique pairs from the dataset follows: If given pair has dose-response experiment, ignore others. Otherwise, if a pair has multiple measurements but not full dose-response curve, we consider it as a secondary screening and we take this pair and its response only if there is no conflict between the experiments. If there is a conflict, we discard the pair entirely (in total 166 pairs were discarded).
take its result and
25


Reviews:
[{"review_id": "KEI70o0NNy", "reviewer": "Reviewer_21XM", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "Predicting protein-molecule interaction by combining a pre-trained protein language model and a GNN. The method was evaluated empirically in the context of odor perception.", "summary_of_the_review": "Interesting method for predicting protein-molecule interaction, with empirical evaluation very focused on a subdomain.", "strength_and_weaknesses": "Strengths:\n+ Protein-molecule interaction is generally a problem of interest to many communities.\n+ The proposed method is an interesting way to combine a protein language model (for the protein) and a GNN (for the molecule), and this method might benefit other problems outside of odor perception.\n\nWeaknesses:\n+ The empirical evaluation is focused on odor perception.\n+ While the proposed method is more general according to the authors, the empirical performance does much some of the existing methods (Table 4).", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "8nA1WjK-4J", "reviewer": "Reviewer_zko6", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The authors present a novel approach for identifying which small molecules will be recognized by different mammalian olfactory receptors. While similar approaches have been presented previously, this study introduces a novel combination of neural network architectures to model the protein-ligand interaction, relying on the learned representation of protein sequences in the ProtBERT language model paired with a graph neural network representation of the small molecule with an attention mechanism to learn non-bonded interactions. They show that this architecture is able to outperform pre-existing techniques at predicting interactions within a newly collected database of 46,450 odorant-OR pairs and dissect how different variations on the network leaving out aspects of the model lead to worse performance at these benchmarks.", "summary_of_the_review": "This paper is overall an impressive piece of work and I think that it would be a valuable addition to ICLR. The architecture used in particular is quite novel and an important increase in sophistication and predictive power over other architectures used in the field, although some elements of its design and testing could be made somewhat more clear in the paper. Especially if these aspects are stated more explicitly in the paper, I would strongly recommend this paper for acceptance.\n", "strength_and_weaknesses": "This work represents an important step toward models that can reliably predict protein-ligand interactions, focused on the very interesting case of odorant receptors and small molecules. The architecture presented is one that is original and sophisticated, and in Section 5.1 the authors make a convincing case that its choices are not arbitrary. While it is not the first paper to use AI to predict olfactory recognition, previous studies have used much simpler models that rely on SMILES encodings of small molecules; I think especially that the attention mechanism learned over the molecular graphs to learn nonbonded interactions strikes me as an important methodological innovation in this study. \n\nThe “Generalization” section of the manuscript is very important, and the authors perform a number of important statistical tests: notably to not only perform random train/test splits of receptors and molecules but also to cluster them, such that members of the test set are relatively different from the training set. Their models perform quite poorly in these situations, and the authors are honest about this case. I personally find it to be a strength of the paper that they did this test, although it obviously opens the door to future improvements of the model. \n\nThe dataset generated of over 45,000 OR-olfactant pairs is also a strength of this paper and will serve as an invaluable reference for future researchers in the field.\n\nBroadly, I feel that the model is stronger at modeling small molecule space than protein space, especially as the protein sequence representation relies entirely on the pretrained [CLS] tokens of ProtBERT. I would personally recommend trying a few variations on this, although I wouldn’t say that they are required because I understand that they can be computationally intensive and challenging to add into such an already large model; I would recommend this as a direction of future research (which the authors also note!) \n\nI also think that some technical parts of the paper are somewhat unclear and could use slightly more description. For example, several aspects of their architecture are not well explained; the message passing neural networks (Q-MPNN and KV-MPNN) are never mentioned in the main text - are these the “two separate identical GNNs for queries and keys/values” mentioned? If, so that should be made clear, along with what the “Q” and “KV” mean. The meaning of “X_old”, “X_new” and “E” in Figure 1 are also not described. I can guess as to their meaning, but it would be better for it to be clearly stated in the legend. Explaining this terminology will also make the “ablations” section more clear, although there are also a few architectures introduced first in that section (e.g. GAT) that could be introduced slightly more clearly and justified in their use.\n\nIn Section 6, the precise definition of “combinatorial code” for each odorant is also somewhat unclear - I am guessing that they mean that they are the predicted recognition of the odorant by each of the 385 human ORs in the model presented earlier, but this is not explicitly stated. Therefore, while they are very mathematically precise about how their clustering score is defined, I initially didn’t understand what actual data they were analyzing.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "80WvG_6oIzW", "reviewer": "Reviewer_pDHK", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper develops a new model for olfactory receptors.", "summary_of_the_review": "Overall the paper looks at an interesting problem however the model falls short in some key aspects as described which can be improved upon.", "strength_and_weaknesses": "Positives:\n- Tackling a new and interesting problem of predicting receptor-odorant binding.\n- Proposing a new model involving GNN and BERT embedding for olfactory receptors.\n- Thorough ablation study of the model.\n- Gathering a new dataset for olfactory receptors.\n\nNegative:\n- The so-called “localization” strategy of adding protein embeddings to the graph, while seemingly improving prediction, does not give any physical interpretation of the underlying binding mechanism of receptors to the odorants. \n- Why should all the codes of the graph have the share of the protein embedding? Intuition says the docking site should play a more significant role than the rest of the molecule.\n- The choice of [CLS] as the embedding is not well-justified. Have the authors done ablation studies over other types of protein embedding for example ESM?\n- Most of the empirical studies are ablation type and comparisons to standard methods are very limited.\n- How does the model compare with the recently developed SE(3) invariant deep learning models of molecules?\n- It is intuitive that the docking site of the protein is more important than the rest. Instead of a global protein embedding can the model incorporate this locality?\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: poster