GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning

## Abstract

Abstract Glycans are basic biomolecules and perform essential functions within living organisms. The rapid increase of functional glycan data provides a good opportunity for machine learning solutions to glycan understanding. However, there still lacks a standard machine learning benchmark for glycan property and function prediction. In this work, we fill this blank by building a comprehensive benchmark for Glycan M achine L earning ( GlycanML ). The GlycanML benchmark consists of diverse types of tasks including glycan taxonomy prediction, glycan immunogenicity prediction, glycosylation type prediction, and protein-glycan interaction prediction. Glycans can be represented by both sequences and graphs in GlycanML , which enables us to extensively evaluate sequence-based models and graph neural networks (GNNs) on benchmark tasks. Furthermore, by concurrently performing eight glycan taxonomy prediction tasks, we introduce the GlycanML-MTL testbed for multi-task learning (MTL) algorithms. Also, we evaluate how taxonomy prediction can boost other three function prediction tasks by MTL. Experimental results show the superiority of modeling glycans with multi-relational GNNs, and suitable MTL methods can further boost model performance. We provide all datasets and source codes at https://github.com/GlycanML/GlycanML and maintain a leaderboard at https://GlycanML.github.io/project .

## 1 Introduction

Glycans are fundamental biomolecules that play crucial roles in maintaining the normal physiological functions and health status of living organisms. They can regulate inflammatory responses [], enable the recognition and communication between cells [], preserve stable blood sugar levels [], *etc.* Thanks to the advance of high-throughput sequencing techniques of glycans [, ], a large number of glycan data are accessible, *e.g.*, the more than 240 thousand glycans stored in the GlyTouCan database []. This progress enables glycan function analysis by machine learning methods which are essentially data-driven.

There are some existing works that employ machine learning models to predict the species origins of glycans [, ], glycosylation phenomenon [, ] and the ability of glycans to induce immune response [, ].
These works mainly aim to solve one or several related glycan understanding problems.
However, there still lacks *a comprehensive benchmark studying the general effectiveness of various machine learning models on predicting diverse glycan properties and functions*, which hinders the progress of machine learning for glycan understanding. As a matter of fact, comprehensive benchmark studies greatly facilitate the machine learning research of other biomolecules like small molecules [, ], proteins [, ] and nucleic acids [, ].

Therefore, in this work, we take the initiative of building a Glycan Machine Learning (GlycanML) benchmark featured with diverse types of tasks and multiple glycan representation structures. The GlycanML benchmark consists of 11 benchmark tasks for understanding important glycan properties and functions, including glycan taxonomy prediction, glycan immunogenicity prediction, glycosylation type prediction, and protein-glycan interaction prediction. For each task, we carefully split the benchmark dataset to evaluate the generalization ability of machine learning models in real-world scenarios. For example, in glycan taxonomy prediction, we leave out the glycans with unseen structural motifs during training for validation and test, which simulates the classification of newly discovered glycans in nature with novel molecular structures.

The GlycanML benchmark accommodates two glycan representation structures, *i.e.*, glycan tokenized sequences and glycan planar graphs. For each structure, we adopt suitable machine learning models for representation learning, where sequence encoders such as CNN [], LSTM [] and Transformer [] are employed for glycan sequence encoding, and both homogeneous GNNs [, , ] and heterogeneous GNNs [, , ] are used to encode glycan graphs. In addition, two state-of-the-art small molecule encoders [, ] are also studied for all-atom glycan modeling. We evaluate each model on all benchmark tasks to study its general effectiveness.

The GlycanML benchmark also provides a testbed, namely GlycanML-MTL, for multi-task learning (MTL) algorithms, where an MTL method is asked to simultaneously solve eight glycan taxonomy prediction problems which are highly correlated. The performance on this testbed measures how well an MTL method can transfer the knowledge learned from different glycan taxonomies, *e.g.*, transferring between species-level classification and genus-level classification. Taking a step further, we study the effect of MTL on boosting three function prediction tasks (*i.e.*, immunogenicity, glycosylation and interaction prediction) by concurrently performing taxonomy prediction.

Benchmark results show that the RGCN model [], a typical heterogeneous GNN, performs best on most benchmark tasks, and a simple two-layer CNN can surprisingly achieve competitive performance by using only condensed sequential information of glycan structures. The MTL methods with elaborate task-reweighting strategies can further enhance the performance of taxonomy prediction, and learning the immunogenicity and glycosylation type of glycans jointly with their taxonomies also achieves benefits, showing the potential of MTL on boosting glycan understanding. We hope the GlycanML benchmark will spark the interest of studying glycoscience with machine learning.

## 2 Related Work

Glycan machine learning.
With the expanding size of experimental glycomics datasets, the integration of machine learning techniques into glycoinformatics shows considerable promise [, ].
Early approaches use traditional machine learning algorithms (*e.g.*, SVMs) to learn patterns from mass spectrometry data [, ], predict glycosylation sites [, , ], and classify glycans [].
Recently, thanks to the advancements in deep learning and new glycomics datasets, there has been a rise in studies applying deep learning to glycan and glycosylation modeling. These works seek to identify N-glycosylated sequon [], model glycan 3D structures [, ] and predict various glycan properties and functions [, , , , , ].

However, there still lacks a comprehensive benchmark that incorporates diverse types of glycan understanding tasks and different glycan modeling methods like sequence-based and graph-based methods. Also, it is unknown how multi-task learning (MTL) influences the learning of glycan property prediction. In this work, we fill these blanks by introducing the GlycanML benchmark with multiple task types, multiple representation schemes of glycan structures, and an MTL testbed.

Biological machine learning benchmarks.
To evaluate the performance of different machine learning methods in modeling biomolecules, it is necessary to establish large-scale standardized benchmarks.
MoleculeNet [] is a widely-used benchmark of small molecule modeling,
evaluating the efficacy of both traditional machine learning and deep learning on predicting molecular properties.
In the field of protein modeling, the renowned CASP [] competition is dedicated to establishing standards for protein structure prediction.
Also, benchmark datasets are constructed for machine learning guided protein engineering [, ], protein design [] and protein function annotation [, ].
Benchmark datasets are also established for other biomolecules like DNAs [, ] and RNAs [].
In this work, we take the initiative of building a glycan machine learning benchmark for comprehensive glycan understanding.

## 3 Benchmark Tasks

The GlycanML benchmark consists of 11 benchmark tasks, including glycan taxonomy prediction, glycan immunogenicity prediction, glycosylation type prediction, and protein-glycan interaction prediction, as illustrated in Figure 1. We summarize the information of all tasks in Table 1.

Figure: Figure 1: *Illustration of benchmark tasks.* (a) Predicting the biological taxonomy of glycans at eight levels. (b) Judging whether a glycan is immunogenic or not in organisms. (c) Analyzing how a glycan glycosylates its target protein. (d) Given a protein and a glycan, predicting their binding affinity.
Refer to caption: x1.png

### 3.1 Glycan Taxonomy Prediction

Scientific significance.
The taxonomy of glycans lays the foundation for glycomics research []. Biologists commonly classify glycans based on their origin under the hierarchical system of domain, kingdom, phylum, class, order, family, genus and species. Such a systematic classification helps us compare the similarities and differences between glycans, which further facilitates the study of glycan structures and functions. Also, the glycan taxonomy helps us understand the process of biological evolution. By comparing the structures of glycans in different organisms, we can infer their phylogenetic relationships and possible changes that may occur during evolution. Therefore, it is very helpful to have an accurate glycan taxonomy predictor based on machine learning.

Task definition.
We study glycan taxonomy prediction on domain, kingdom, phylum, class, order, family, genus and species levels, leading to eight individual tasks. These tasks are formulated as classification problems with 4, 11, 39, 101, 210, 415, 922 and 1,737 biological categories, respectively. Taking the class imbalance into consideration, we report Macro-F1 score for each task.

Benchmark dataset.
We collect the glycans in the SugarBase database [] that are fully annotated with domain, kingdom, phylum, class, order, family, genus and species labels, with 13,209 glycans in total. We then represent each glycan with the frequencies of popular motifs (*i.e.*, those frequently occurring substructures in glycans), where the motif list proposed by Thomès et al. [] is employed. Based on such representations, we cluster all glycans in the dataset by K-means ($K=10$), where 8 clusters are assigned to training, and the remaining two clusters are respectively utilized for validation and test. By using such dataset splits, this set of tasks evaluate how well a machine learning model can generalize across structurally distinct glycans.

Table: Table 1: Benchmark task descriptions. We list each task along with its type, the average number of monosaccharides in each glycan for this task (in mean(std) format), dataset statistics, and evaluation metric. *Abbr.*, Mono.: Monosaccharides.

### 3.2 Glycan Immunogenicity Prediction

Scientific significance.
Predicting the immunogenicity of glycans is of great significance for vaccine design and disease treatment. (1) Glycans are key components in many vaccines, especially in bacterial vaccines []. By predicting the immunogenicity of glycans, researchers can design more effective vaccine formulations.
(2) In addition, certain glycans can inhibit tumor growth by activating the immune system [], and therefore accurately predicting glycan immunogenicity can help optimize tumor treatment strategies.

Task definition.
We formulate this task as a binary classification problem, *i.e.*, predicting whether a glycan is immunogenic or not. We evaluate with the AUPRC metric to measure the trade-off between precision and recall of a model on immunogenic glycans.

Benchmark dataset.
We select out all glycans in the SugarBase [] whose immunogenicity is annotated based on evidences in literature, summing up to 1,320 glycans. As in glycan taxonomy prediction, we use the motif-based dataset splitting scheme to derive training, validation and test splits with an 8:1:1 ratio. In this way, we evaluate models’ generalization ability across structurally distinct glycans.

### 3.3 Glycosylation Type Prediction

Scientific significance.
Glycans are a class of macromolecules with diverse biological activities, including immune system regulation, antitumor effects, antiviral effects, *etc.* By predicting the type of glycosylation, researchers can better understand the relationship between glycan structure and its functions. Understanding the structure-function relationship is crucial for designing and synthesizing glycan derivatives with specific biological activities [].

Task definition.
Given a glycan, we aim at predicting whether it forms N-glycosylation, O-glycosylation or maintains a free state, formulated as a three-way classification problem. The Macro-F1 score is used for evaluation.

Benchmark dataset.
We traverse the GlyConnect database [] and select out all glycans with glycosylation annotations, with 1,683 glycans in total. Upon these data, we again employ the motif-based dataset splitting scheme (introduced in Section 3.1) to construct training, validation and test splits with an 8:1:1 ratio. This task again assesses the generalization ability across the glycans with distinct structures.

### 3.4 Protein-Glycan Interaction Prediction

Scientific significance.
The interactions between proteins and glycans play a crucial role in cellular signaling, affecting cell growth, differentiation, and apoptosis []. For example, glycans are one of the main components of the extracellular matrix (ECM), which interact with proteins such as collagen, laminin, and fibronectin to form the structural framework of ECM, providing physical support and passing biochemical signals to cells []. Understanding these interactions helps reveal how cells respond to external signals.

Task definition.
Given a protein and a glycan, this task aims to regress their binding affinity, where the Z-score transformed relative fluorescence unit represents binding affinity. For this task, we adopt the Spearman’s correlation coefficient as the evaluation metric to measure how well a model ranks a set of protein-glycan pairs with different binding affinities.

Benchmark dataset.
This benchmark dataset is built upon 564,647 protein-glycan interactions deposited in the LectinOracle database []. It is desired to have a model that can well generalize to new proteins against training ones, considering the continuous discovery of new proteins by sequencing techniques. Therefore, we split the dataset based on protein sequence similarity. Specifically, we first cluster all protein sequences using MMseqs2 [] (minimum sequence identity within each cluster: 0.5), and then we derive training, validation and test proteins by splitting all clusters with an 8:1:1 ratio. Finally, samples of protein-glycan pairs are split according to the protein splits, deriving the benchmark dataset for protein-glycan interaction prediction.
This splitting scheme is consistent with practical vaccine design applications where newly discovered lectins (proteins) could be used as the target for glycan binding.

## 4 Methods

Figure: Figure 2: *Illustration of glycan representations.* (a) The glycan tokenized sequence is derived by tokenizing the IUPAC-condensed sequence. (b) The glycan planar graph is constructed by transforming the IUPAC-condensed sequence to graph.
Refer to caption: x2.png

### 4.1 Representations

In the GlycanML benchmark, we adopt two glycan-specific representation structures, *i.e.*, glycan tokenized sequence and glycan planar graph, as illustrated in Figure 2.

Glycan tokenized sequence.
A glycan is commonly represented by an IUPAC-condensed sequence. For example, in the sequence “Glc(a1-4)Glc”, two glucoses are connected by an alpha-1,4-glycosidic bond, and this structure is the basic component of starch, a typical glycan. To process such sequences with machine learning models, a straightforward way is to tokenize the IUPAC-condensed sequence. Specifically, we regard each monosaccharide (*e.g.*, Glc), each glycosidic bond (*e.g.*, a1-4), and each bracket that indicates glycan branching (*i.e.*, “[” and “]”) as a single token, which derives the glycan tokenized sequence, denoted as $x_{s}=\{s_{i}\}_{i=1}^{N}$. Various sequence encoders like Transformers [] can then be applied to such tokenized sequences for glycan representation learning.

Glycan planar graph.
Essentially, an IUPAC-condensed sequence describes the branching structure of a glycan, in which the part between brackets “[” and “]” denotes a side branch of the main branch, as illustrated in Figure 2. This structure is well represented by a planar graph $x_{g}=(\mathcal{V},\mathcal{E})$, in which nodes $\mathcal{V}$ denotes monosaccharides, and edges $\mathcal{E}$ denotes glycosidic bonds. In this way, graph neural networks (GNNs) are readily used for glycan modeling.

### 4.2 Baselines

We include four types of models in our benchmark, *i.e.*, sequence encoders for modeling glycan tokenized sequences, homogeneous and heterogeneous GNNs for modeling glycan planar graphs, and all-atom molecular encoders for modeling the all-atom molecular graphs of glycans, with 12 baseline models in total. Their detailed architectures are provided in Appendix A.

Sequence encoders.
We study the performance of four typical sequence encoders. Inspired by the success of shallow CNNs in modeling biological sequences like protein sequences [, ], we investigate (1) a 2-layer shallow CNN along with (2) a deep residual network (ResNet) [] with 8 hidden layers. These two CNN models mainly focus on capturing local information in glycan sequences. To investigate the importance of long context modeling for glycan understanding, we also include (3) a 3-layer bidirectional LSTM [] and (4) a 4-layer Transformer encoder [].

Homogeneous GNNs.
Upon glycan planar graphs, standard GNNs designed for homogeneous graph modeling can be readily used to learn glycan representations. In our benchmark, three typical homogeneous GNNs, *i.e.*, GCN [], GAT [] and GIN [], serve as baselines, and they are all configured with 3 message passing layers.

Heterogeneous GNNs. As a matter of fact, modeling glycans as homogeneous graphs is suboptimal, in which the rich information within glycosidic bonds is fully ignored. To capture the complete information in glycan graphs, it is more proper to view them as heterogeneous graphs and employ heterogeneous GNNs for representation learning. Therefore, we adapt three popular heterogeneous GNNs, *i.e.*, MPNN [], RGCN [] and CompGCN [], to model glycan graphs, where each model is equipped with 3 message passing layers.

All-atom molecular encoders. Essentially, glycans are a kind of macromolecules with hundreds of atoms, and thus we can directly use all-atom molecular encoders for small molecules to model glycans. Among existing small molecule encoders, Graphormer [] and GraphGPS [] achieve prominent performance, and we include them in this benchmark.

### 4.3 Model Pipelines

Depending on inputs, the benchmark tasks of GlycanML can be solved with two model pipelines.

Single-glycan prediction.
This pipeline handles the tasks that predict the properties of individual glycans, including glycan taxonomy prediction, glycan immunogenicity prediction, and glycosylation type prediction. For each task, the glycan representation vector is first extracted by a glycan encoder and then passed to an MLP head for task-specific prediction.

Protein-glycan interaction prediction.
Because of the additional input of protein, protein-glycan interaction prediction requires a different pipeline. Given a protein and a glycan, we first extract the protein representation with a protein encoder (*e.g.*, the ESM-1b protein language model [] used in this work) and extract the glycan representation with a glycan encoder, and these two representations are then concatenated and sent to an MLP head for interaction prediction.

### 4.4 Multi-Task Learning

In GlycanML, the glycan taxonomy prediction tasks classify glycans under the hierarchical system from domain to species. These tasks are highly correlated and well-suited for multi-task learning (MTL) where related tasks are learned together for better generalization performance []. Therefore, we integrate eight glycan taxonomy prediction tasks in GlycanML as a testbed for MTL algorithms, named as the GlycanML-MTL benchmark.

On this benchmark, we study 6 representative MTL methods for MTL loss design and MTL model optimization.
All these methods use the network architecture with hard parameter sharing [], where all tasks share a common backbone encoder, and each task owns its individual prediction head. We introduce these methods below, with an abbreviation after each one.

- •
Naive MTL (N-MTL):
The most straightforward way to perform MTL is to sum up the losses of all tasks with equal weights and optimize the model with this loss summation. Denoting the losses of GlycanML-MTL tasks as $\mathcal{L}_{i}$ ($i=1,\cdots,8$), the naive MTL loss is defined as: $\mathcal{L}_{\mathrm{N-MTL}}=\sum_{i=1}^{8}\mathcal{L}_{i}$.
- •
Gradient Normalization (GN) []:
However, regarding all tasks equally is suboptimal, considering the varying difficulties of different tasks. Therefore, this method employs a weighted loss summation $\mathcal{L}_{\mathrm{GN}}=\sum_{i=1}^{8}w_{i}\mathcal{L}_{i}$, where the weights satisfy: $\sum_{i=1}^{8}w_{i}=8$. The main idea of gradient normalization is that different tasks should be trained at similar rates (*i.e.*, similar speed of convergence). To achieve this goal, authors first deem the L2 norm of per-task gradient as the training rate of the task: $r_{i}=||\nabla_{\theta}w_{i}\mathcal{L}_{i}||_{2}$ ($\theta$ denotes model parameters), and all tasks are then pushed to have similar training rates by optimizing the loss $\mathcal{L}(w_{1},\cdots,w_{8})=\sum_{i=1}^{8}||r_{i}-\bar{r}||_{1}$ ($\bar{r}=(\sum_{i=1}^{8}r_{i})/8$). For each training step, this loss is first optimized *w.r.t.* loss weights $\{w_{i}\}_{i=1}^{8}$, and, using the updated loss weights,
the whole model is optimized by $\mathcal{L}_{\mathrm{GN}}$.
- •
Temperature Scaling (TS) []:
For classification tasks, the sharpness of categorical distribution represents prediction uncertainty, further implying task difficulty. Inspired by this fact, the TS method weighs different tasks by scaling their classification logits. In this way, each task loss is defined as $\mathcal{L}^{\mathrm{TS}}_{i}=-\log(\mathrm{Softmax}(f_{\theta}(y|x)/\sigma_{i
}^{2}))$ ($i=1,\cdots,8$), where $f_{\theta}(y|x)$ is the classification logit of sample $x$ at class $y$, and $\sigma_{i}$ denotes the task-specific temperature parameter for scaling. The temperature parameters are learned along with the whole model.
- •
Uncertainty Weighting (UW) []:
Kendall et al. [] shows that the temperature-scaled losses above can be approximated by a weighted summation of unscaled losses: $\mathcal{L}_{\mathrm{UW}}=\sum_{i=1}^{8}\mathcal{L}_{i}/\sigma_{i}^{2}+\log
\sigma_{i}$, where the weighting parameters $\{\sigma_{i}\}_{i=1}^{8}$ are learnable. This method also weighs different tasks based on the uncertainty of task predictions.
- •
Dynamic Weight Averaging (DWA) []:
The loss scales along training can well indicate task convergence. Therefore, this method employs the ratio of consecutive losses to weigh different tasks: $w_{i}(t)=8\cdot\mathrm{Softmax}(\mathcal{L}_{i}(t)/\mathcal{L}_{i}(t-1))$, where $\mathcal{L}_{i}(t)$ denotes the loss of task $i$ at training step $t$. In this way, more weights are assigned to the tasks with slower convergence.
- •
Dynamic Task Prioritization (DTP) []:
This method maintains a key performance indicator (KPI) $\kappa_{i}(t)$ for each task along training (moving average of classification accuracy on our benchmark) and weighs different tasks in a focal loss [] manner: $w_{i}(t)=-(1-\kappa_{i}(t))^{\gamma_{i}}\log\kappa_{i}(t)$, where $\gamma_{i}$ is the focusing hyperparameter for task $i$. Such a task reweighting scheme pays more attention to difficult tasks with low KPI.

## 5 Experiments

### 5.1 Experimental Setups

Model setups.
For glycan taxonomy, immunogenicity and glycosylation type prediction, upon the glycan representation extracted by the glycan encoder, we use a 2-layer MLP with ReLU activation to perform prediction. For protein-glycan interaction prediction, we use the ESM-1b protein language model [] to extract protein representation, and, upon the concatenation of protein and glycan representations, the binding affinity is predicted by a 2-layer MLP with ReLU activation.

Training setups.
We conduct every experiment on three seeds (0, 1 and 2) and report the mean and standard deviation of results. We train with an Adam optimizer (learning rate: $5\times 10^{-4}$, weight decay: $1\times 10^{-3}$) for 50 epochs on taxonomy, immunogenicity and glycosylation type prediction and for 10 epochs on interaction prediction. The batch size is set as 32 for interaction prediction and 256 for other tasks. For model training, we use cross entropy loss to train taxonomy and glycosylation type prediction tasks, use binary cross entropy loss to train immunogenicity prediction, and adopt mean squared error to train interaction prediction. For model selection, 10 times of validation are uniformly performed along the training process, and the checkpoint with the best validation performance is selected for test. For multi-task learning (MTL), the focusing parameter $\gamma$ of the dynamic task prioritization (DTP) method is set as 2.0, and the model selection of all MTL methods is based on the mean accuracy over all tasks on the validation set. We conduct all experiments on a local server with 100 CPU cores and 4 NVIDIA GeForce RTX 4090 GPUs (24GB). Our implementation is based on the PyTorch [] deep learning library (BSD-style license) and TorchDrug [] drug discovery platform (Apache-2.0 license).

Table: Table 2: Benchmark results on single-task learning. We report *mean (std)* for each experiment. Three color scales of blue denote the first, second and third best performance. *Abbr.*, Immuno: Immunogenicity; Glycos: Glycosylation.

### 5.2 Benchmark Results on Single-Task Learning

In Table 2, we report the single-task performance of 12 representative glycan encoders.
We measure the comprehensive performance of a model with its *weighted mean rank* over all tasks, where each taxonomy prediction task weighs $1/8$ and each of the other three tasks weighs 1, so as to balance between different types of tasks.
Based on these results, we highlight the following findings:

- •
Multi-relational GNNs show superiority in glycan modeling.
Two typical multi-relational GNNs, *i.e.*, RGCN and CompGCN, respectively rank first and second place in terms of weighted mean rank. Especially, RGCN achieves the best performance on 6 out of 11 benchmark tasks. Therefore, it is beneficial to model a glycan as a multi-relational graph, where different types of glycosidic bonds are deemed as different relations between monosaccharides.
- •
A simple shallow CNN is surprisingly effective.
It is observed that the 2-layer shallow CNN ranks among the top three on 5 out of 11 tasks, and it ranks fifth place in terms of weighted mean rank.
Therefore, such a shallow CNN model is sufficient to produce informative glycan representations and achieve competitive performance, aligning with previous findings that shallow CNNs can well model biological sequences [, ].
- •
It is important to utilize glycosidic bond information.
We can observe clear performance gains of heterogeneous GNNs over homogeneous GNNs on glycan modeling, where in terms of weighted mean rank, three heterogeneous GNNs rank 1st, 2nd and 10th places, while three homogeneous GNNs rank 6th, 7th and 8th places. Compared to homogeneous GNNs that regard all glycosidic bonds as the same, heterogeneous GNNs fully utilize glycosidic bond information by individually treating each type of bonds, leading to obvious benefits.
- •
Small molecule encoders can hardly handle glycan modeling.
Though performing well on small molecule modeling, Graphormer and GraphGPS perform poorly on the GlycanML benchmark, where they rank last two places in terms of weighted mean rank. These results illustrate that the small molecule encoders originally designed to model tens of atoms cannot well model a macromolecule like a glycan with hundreds of atoms, calling for specifically designed models for all-atom glycan modeling.

### 5.3 Benchmark Results on Multi-Task Learning

In Table 3, we report the benchmark results of different MTL methods against single-task learning. We select shallow CNN and RGCN as the backbone encoder, and all MTL methods are evaluated on each of them. According to benchmark results, we have the findings below:

- •
The temperature scaling (TS) approach performs best.
On both shallow CNN and RGCN, the TS approach achieves the highest mean Macro-F1 score, and it outperforms single-task learning with a clear margin (*i.e.*, 2.75% improvement in mean Macro-F1 score) when using shallow CNN as backbone encoder. Therefore, the TS approach can well balance the learning signals from different glycan taxonomy prediction tasks, leading to stable performance gains.
- •
MTL methods are not always beneficial.
On shallow CNN, only the TS and UW methods outperform single-task learning in terms of mean Macro-F1 score; on RGCN, only the TS and DTP methods outperform single-task learning in terms of mean Macro-F1 score. Actually, most MTL methods lead to performance decrease compared to single-task learning. These results suggest the high difficulty of balancing between different glycan taxonomy prediction tasks. More efforts are thus required to boost the MTL performance on the GlycanML-MTL testbed, which we leave as one of our major future works.

Table: Table 3: Benchmark results on multi-task learning. We report *mean (std)* for each experiment. Two color scales of blue denote the first and second best performance.

### 5.4 Effect of Multi-Task Learning on Glycan Function Prediction

Table: Table 4: Benchmark results on multi-task learning for glycan function prediction. We report *mean (std)* for each experiment.

In this part, we study the effect of MTL on three glycan function prediction tasks, *i.e.*, immunogenicity, glycosylation and interaction prediction. Considering that evolutionarily similar glycans tend to have similar functions, we study how glycan function prediction can be benefited by jointly learning the evolutionary taxonomy of glycans. Specifically, we regard each of three function prediction tasks as the center task and eight taxonomy prediction tasks as auxiliary tasks, and the center task is trained along with eight auxiliary tasks to perform MTL (the losses of center and auxiliary tasks are summed up for model training).

In Table 4, we compare this multi-task method with the single-task baseline. Under both Shallow CNN and RGCN encoders, MTL performs better on immunogenicity and glycosylation type prediction, while single-task learning performs better on interaction prediction. These results show the potential of MTL on boosting glycan function prediction, and also inspire future research efforts to further improve its effectiveness.

## 6 Conclusions and Future Work

In this work, we build a comprehensive benchmark GlycanML for glycan machine learning. It consists of diverse types of glycan understanding tasks, including glycan taxonomy prediction, glycan immunogenicity prediction, glycosylation type prediction, and protein-glycan interaction prediction. In GlycanML, we support two representation methods of glycans, *i.e.*, glycan tokenized sequences and glycan planar graphs, enabling glycan modeling with sequence encoders and graph neural networks (GNNs). Additionally, on eight highly correlated glycan taxonomy prediction tasks, we set up a testbed GlycanML-MTL to compare different multi-task learning (MTL) algorithms. Also, we study how taxonomy prediction can boost other three function prediction tasks by MTL. According to the benchmark results, multi-relational GNNs show great promise for glycan modeling, and well-designed MTL methods can further boost model performance.

In the future, we will apply glycan machine learning models to important real-world glycan-related tasks. For example, for vaccine design, we can employ well-performing immunogenicity predictors and protein-glycan interaction predictors to virtually screen glycan candidates that can most effectively induce immune response.

## Appendix A Details of Model Architectures

Table: Table 5: Architectures of baseline models. *Abbr.*, Params.: parameters; dim.: dimension; conv.: convolutional; attn.: attention; concat.: concatenate.

We provide the detailed architectures of baseline models in Table 5. For the input layer of each model, we employ an embedding layer to get the representation of each token/node; an additional positional embedding layer is used by ResNet and Transformer to represent sequential information; MPNN adopts an additional edge embedding layer to represent glycosidic bonds. We follow the implementation of different sequence and graph encoding layers in TorchDrug [] to construct hidden layers. For the output layer of sequence encoders, we follow the implementation in TorchDrug to readout a glycan-level representation from token-level representations. We use the concatenation of mean and max pooling as the output layer of GCN, GAT, GIN, RGCN, CompGCN, Graphormer and GraphGPS, for its superior performance in our experiments. Following the original design of MPNN, a Set2Set pooling [] serves as its output layer. We follow the default architecture of Graphormer and GraphGPS in their original work.

## Peer Reviews

confidence: 3
contribution: 2
initial_rating: 6
presentation: 3
questions: See above weaknesses.
soundness: 4
strengths: 1. **Having a clear as well as straightforward motivation and contribution**. The benchmark is highly relevant for advancing glycan-related machine learning research, offering a platform to assess various models on tasks that are both scientifically and practically significant in fields like immunology and molecular biology.
2. **Well-written paper; addressing each detail of GLYCANML thoroughly**. The paper is comprehensive, including detailed dataset construction, task definitions, and experimental protocols. It also provides a robust comparison of baseline models, which supports its claims of model performance and suitability.
3. **High benchmark quality: multiple tasks and diverse modalities provided; allowing multitask learning and multimodal training**. The *GLYCANML-MTL* setup for multi-task learning offers a unique opportunity to test MTL methods in a real-world scenario with related biological tasks, demonstrating the potential to improve performance through shared learning.
summary: This paper aims to and succeed in providing a standard machine learning benchmark for **glycans**. It will clearly benefit the future research in studying glycan property and function prediction via application of machine learning models, and it will make positive impacts on bioinformatics and biological intelligence community. The proposed benchmark **GLYCANML** consists of multiple essential task in glycans research, and the benchmark also offers diverse data modalities, mainly sequence representations and graph representations. Additionally, the *GLYCANML-MTL* testbed is designed to support multi-task learning (MTL), where glycan taxonomy tasks are addressed collectively to assess knowledge transfer across related prediction tasks.
weaknesses: 1. **Broader Impact of the Datasets**. From my understanding the dataset is very good resource for the application related to Glycans. However, for machine learning, it is not clear how this dataset is different from previous ones and what new insights it can bring to the domain of graph learning or multi-task learning.
2. **Lack of Multimodal Encoders**. The benchmark has sequence and graph representation, and it is good to show the performance of multimodal molecular encoders like MolFormer [1].
3. **More Pretrained Model Performance**. The paper did not explicitly say whether using the checkpoints for Graphormer or not, and if indeed using the pretrained checkpoint, it will also surpise me that a pretrained model even fail to match the standard sequence models and graph models. Also, other pretrained models, such as MolCLR [2], can be presented.

[1] Ross, Jerret, et al. "Large-scale chemical language representations capture molecular structure and properties." _Nature Machine Intelligence_ 4.12 (2022): 1256-1264.
[2] Wang, Yuyang, et al. "Molecular contrastive learning of representations via graph neural networks." _Nature Machine Intelligence_ 4.3 (2022): 279-287.

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confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: - Why is the sequence identity threshold set to 0.5? A common choice would be 0.3 in protein-related studies [1,2].

[1] Evaluating Representation Learning on the Protein Structure Universe, ICLR2024.

[2] Learning from Protein Structure with Geometric Vector Perceptrons, ICLR2021
soundness: 3
strengths: - The application of machine learning on glycans is interesting and unexplored. The curated datasets can benefit the model development and future studies.
- The studied multi-task learning on glycans demonstrates the strong correlation between different tasks.
summary: This paper presents a benchmark for evaluating different graph encoders and multi-task learning frameworks on glycan data, covering four tasks with corresponding curated datasets. The benchmark results reveal interesting findings, such as the effectiveness of shallow CNNs as strong baselines and all-atom encoders fail to achieve better performance than GNN-based models.
weaknesses: - As a benchmark study, the GNN encoders and multi-task learning methods compared in the paper are not state-of-the-art. More advanced methods should be included in the benchmark for a comprehensive evaluation, such as [2,3,4,5].
- Without context such as cell type or antigen, predicting immunogenicity based solely on glycan input is not practically useful since immunogenicity is heavily influenced by the biological context. Immune recognition often involves complex molecular interactions influenced by surrounding biomolecules and cellular environments.
- One suggestion for Protein-Glycan Interaction Prediction is to conduct the splitting based on the sequence identity of binding sites instead of the whole protein sequence identity[1], which can provide a more solid evaluation protocol.

[1] PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences

[2] Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+, Nature Communications

[3] Principal Neighbourhood Aggregation for Graph Nets, NeurIPS2020

[4] Multi-Task Learning as a Bargaining Game, ICML2022

[5] Conflict-Averse Gradient Descent for Multi-task Learning, NeurIPS2021

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confidence: 2
contribution: 3
initial_rating: 6
presentation: 3
questions: - To my knowledge, glycans have not received much attention at ML conferences compared to proteins. Is there a specific reason for this?
- In a glycan planar graph, nodes are monosaccharides. What feature information is used in this case?
soundness: 3
strengths: Introducing an important problem in the field of biology to the machine learning community and creating a new problem is very significant.
summary: This paper first benchmark paper on ML for Glycans.
weaknesses: - Since biology is not my major field, there needs to be more emphasis on how biologically significant glycans are.
- There needs to be a discussion on how well this paper fits the ICLR conference.
- Collecting the benchmark dataset does not seem to pose significant challenges. What challenges are there?
- All data splits are based on motif-based splits, but there is no discussion on the biological significance of motifs. From the perspective of scientific discovery, is a motif-based approach appropriate for modeling the real world? It seems that data could also be split by methods such as random, monosaccharide composition, or glycosylation type, and there needs to be a discussion on this.

Meta-review: The paper received consistent support from all reviewers. Thus an accept is recommended.

Decision: Accept (Poster)