ISSN 2186-7437

NII Shonan Meeting Report

No. 179

Computational metabolomics andmachine learning
Sebastian Böcker
Hiroshi Mamitsuka
Juho Rousu
May 8–11, 2023

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan

Organizers
Sebastian Böcker (Friedrich Schiller University, Jena, Germany)
Hiroshi Mamitsuka (Kyoto University, Japan)
Juho Rousu (Aalto University, Finland)

Attendees
Wout Bittremieux (University of Antwerp, Belgium)
Sebastian Böcker (Friedrich Schiller University, Jena, Germany)
Roman Bushuiev (The Czech Academy of Sciences, Prague, Czech Republic)
Jacques Corbeil (Université Laval, Québec, Canada)Sašo Džeroski (Jozef Stefan Institute, Ljubljana, Slovenia)
Kai Dührkop (Friedrich Schiller University, Jena, Germany)
Markus Heinonen (Aalto University, Finland)
Lukas Käll (Science for Life Laboratory, Sweden)
Fleming Kretschmer (Friedrich Schiller University, Jena, Germany)
Hiroshi Mamitsuka (Kyoto University, Japan)
Lennart Martens (Ghent University and VIB, Belgium)Grégoire Montavon (FU Berlin, Germany)
Shohei Nakamukai (RIKEN CSRS, Yokohama, Japan)
Dai Hai Nguyen (University of Tsukuba, Japan)
Tomas Pluskal (The Czech Academy of Sciences, Prague, Czech Republic)
Juho Rousu (Aalto University, Finland)
Hiroto Saigo (Kyushu University, Japan)
Michael Stravs (Eawag, Dübendorf, Switzerland; ETH Zürich, Zürich, Switzerland)1Abstract
Machine learning is crucial for small molecule research, aiding in structure annotation, understanding protein and small molecule interactions, and uncoveringcellular mechanisms. Metabolomics, the study of small molecules, is sometimes
considered the pinnacle of omics-sciences as it closely relates to biological phenotypes. Mass spectrometry is the primary method for detecting and identifying
metabolites and small molecules in high-throughput experiments. Technological advancements have enabled novel investigations but also led to a significant
increase in data complexity, posing challenges in data interpretation. Machinelearning has made significant progress in small molecule identification, although
the problem is far from being solved. In drug discovery, machine learning has
been instrumental in high-throughput anticancer drug screening, guiding precision medicine and drug repurposing efforts. The interpretation of metabolomics
data is highly challenging, surpassing methods used in genomics or proteomics.
Representing metabolites as graphs for machine learning algorithms requires
special considerations, and available training data is often non-representativeand lacks uniformity. Generalization beyond existing data is difficult, and overfitting is a common issue. The seminar facilitated idea exchange between bioinformatics and machine learning experts in the small molecule field. A diverse
group of scientists met at the workshop, including established researchers andpromising talent from machine learning, bioinformatics, metabolomics, and drug
discovery fields. Short presentations highlighted the techniques and challenges,
while brainstorming sessions and break-out groups encouraged detailed discussions and fostered collaborations among participants. This report has been
written in cooperation with all attendees.

2Introduction
In recent years, machine learning has emerged an important tool in the researchof small molecules, aiding their identification from measurement data, deciphering their interactions with proteins and other small molecules, and helping toelucidate the inner workings of cellular machinery. The research on the small
molecule complement of the genome, metabolomics, has been referred to as
the apogee of the omics-sciences, as it is closest to the biological phenotype.
Metabolites are not only responsible for tasks such as growth, development, andreproduction, but also directly relevant to structure, signaling, and chemical interactions with other organisms. Most pharmaceuticals are small molecules thatbind to their targets, thus altering their behavior. In small molecule identification, mass spectrometry is the predominant analytical technique for detecting
and identifying metabolites and other small molecules in high-throughput experiments. Huge technological advances in mass spectrometers and experimental
workflows during the last decades enable novel investigations of biological systems on the metabolite level. But these advances also resulted in a tremendous
increase of both amount and complexity of the experimental data, and “making
sense” of the data is among the most pressing issues in high-throughput settings. Machine learning methods for small molecule identification have madegreat progress during the last decade, however, the identification problem isstill far from being “solved”. In drug discovery, several high-throughput anticancer drug screening efforts have been conducted, providing drug interaction
and response measurements that allow for the identification of compounds thatshow increased efficacy in specific human cancer types or individual cell lines,
therefore guiding both the precision medicine efforts as well as drug repurposing applications. Machine learning methods have shown their potential in thesetasks, in e.g. several recent DREAM challenges organized around the theme.
Similarly, in functional genomics, the prediction of biosynthetic gene clusters
through machine learning is an active topic, concerned with the elucidation of
the metabolites associated with a biosynthetic pathway of an organism. During
the last decade, metabolomics has seen numerous cooperations between experimental and computational scientists. It turns out that the interpretation of
the data is highly challenging, and as soon as one goes beyond the presence orabsence of peaks in MS1 experiments, methods which have been developed ingenomics or proteomics cannot be applied to metabolomics data. In particular, the application of machine learning techniques is impeded by several issues:
For example, metabolites are graphs and representing them for machine/deeplearning algorithms requires special care. (In truth, the situation is even worse,
as even the representation of molecular structures as graphs is too restricted
and often plainly wrong.) Also, available training data for small molecules isusually very far from being a representative, even less so a uniform subsampleof the complete space of molecules. Thus, generalization outside the current
data is challenging and machine learning methods are prone to overfit. The key
goal of this seminar was to foster the exchange of ideas between bioinformatics and machine learning for small molecules. We invited to the workshop adiverse group of scientists working in the fields of machine learning, bioinformatics, metabolomics and drug discovery, including both leading names in theirfields as well as young rising talent. State-of-the-art methods from computerscience, statistics, analytical and biological experiments were presented through3

short presentations, along with problems arising from these techniques. Brainstorming sessions and break-out groups were used to discuss individual topics
in greater detail, to initiate new collaborations between participants who have
not yet worked together. The organizers are confident that the exchange ofexpertise in the seminar will result in several scientific advances in the coming
years, which will push forward computational metabolomics as a field.

Schedule Of The Seminar
On Monday morning (Sessions 1 and 2) and Tuesday morning (Sessions 5 and6), participants gave short introductory talks, either about their work or about
open questions and future directions. The other sessions were used for open
discussions, with topics chosen by the participants before and during the seminar. Some sessions were held in parallel, in case not all of the participants wereinterested in the same topic; this also allowed us to cover a more comprehensive selection of topics. An outing on Wednesday complemented the scientificprogram. The two sessions on Thursday were used for some short scientific discussions,a general
wrapupof theseminar, a discussion of future seminars, as
Schedule
for Shonan
seminar179
well
a discussion
on the general direction the field is heading and its perspecWeek asof: May
7
tives.
May 7

May 8

May 9

May 10

May 11SUNDAYMONDAYTUESDAYWEDNESDAY

THURSDAY

7:00 AM
7:30 AM

Breakfast

Breakfast

Breakfast

Breakfast

8:45 AM

Session 1:

Session 5:

Session 9:

Session 11:

9:30 AM

Seminar opening

6 short presentations

Joint disussion

Wednesday breakout
group debrief

10:00 AM

6 short presentations

Joint discussion

Joint discussion

10:30 AM

Break

Break

Break

Break

11:00 AM

Session 2:

Session 6:

Session 10:

Session 12:

11:30 AM

3 short presentations

3 short presentations

Joint discussion or
breakout groups

Joint discussion

12:00 PM

Lunch break

Lunch break

Lunch break

Lunch break

12:30 PMlunch + ad hoc
discussions

lunch + ad hoc
discussions

lunch + ad hoc
discussions

lunch + ad hoc
discussions

Group photo

Excursion/Outdoor
activity

Departure

8:00 AM
8:30 AM

1:00 PM
1:30 PM
2:00 PM

Session 3:

Session 7:

2:30 PM

Joint discussion: Fromembeddings tostructures

Joint discussion:
Interpretable AI &inductive biases

3:30 PM

Break

Break

4:00 PM

Session 4:

Session 8:

4:30 PM

Joint discussion:
Unlabeled data

Breakout groups inparallel: 1) Multi-omics
2) Standards

DinnerDinnerFree time

Free time

3:00 PM Check-in

5:00 PM
5:30 PM
6:00 PM

Main Banquet

6:30 PM
7:00 PM Welcome Banquet
7:30 PM
8:00 PM
8:30 PM
9:00 PM4

Session Abstracts
Monday 14:00-15:30: From embeddings to structures
We first discussed different types of embeddings: Namely, spectrum to embedding to molecular structure, protein to embedding to molecular structure, or
two embedding layers for mass spectra and structure information. The various
generative modeling approaches were discussed at length. The utility of stablediffusion for generating molecules from spectra was discussed (e.g. DiffLinker).
The validity of the generated molecules is an important issue (proper bonds,
valencies etc.). Various variants such as conditional diffusion and classifier-free
guidance were discussed as potential methods. The (lack of) invariance withrespect to permutations of the adjacency matrix was noted as an issue for discrete diffusion models which is not present in the coordinate-based diffusion
models. Modular flows were mentioned as a good method to generate highvalidity molecules, but not good for molecular property optimization. Methods
for assessing the aptness of a molecule generation was also discussed at length.
Another discussion thread centered around the use of SMILES representations,which allow the use of the generative models developed in the NLP camp. The
limitations of SMILES and their practical relevance were discussed, their observed tendency to frequently generate molecules that don’t look realistic tochemists and the lack of smoothness of the embedding space being the issues.
How to quantify the “unhappiness” of the chemists to the generated molecules
was also brought forward as a question. Beyond the validity of the structures,
also their stability could be taken into account (e.g. through Group Contribution Theory).

Monday 16:00-18:00: Using unlabeled data for training
This session covered the possibilities of using large unlabeled datasets of small
molecules and mass spectra. We discussed our experience and the most promising future directions based on results from other domains such as natural language processing (NLP) and protein representation learning. We agreed thatthe most valuable source of raw LC-MS/MS data is the GNPS part of MassIVE.
More precisely, Kai’s filtering of GNPS resulted in 500K to millions of unlabeledmolecules, while Roman’s filtering produced datasets containing 2M to 700M
spectra with varying degrees of quality and redundancy. Comparatively, parsing
the MetaboLights repository proved to be challenging. Kai investigated weaksupervision with labels from COSMIC for subsequent structure prediction, but
it didn’t work. Roman experimented with self-supervised masking of peaks(m/z ratios, intensities, or both values), and the masking of two m/z ratios
seems to be a reasonable pre-training objective. By validating the training after
each epoch, the neural network gradually derives structural properties of small
molecules. However, in Wout’s analysis of a similar approach for proteomics,
masking objectives were too simplistic for the model. Furthermore, we aimed
to understand why self-supervised pre-training does not work as effectively forsmall molecules as for protein sequences. Our overall conclusion was that while
protein space is sparse when deleting an amino acid, replacing a functional groupor an atom in a small molecule can be achieved through various options. Although this conclusion does not hold true consistently for all molecular classes,

5

on average, it may be a severe bottleneck for self-supervision. To address thisissue, ”state-of-the-art” methods experiment with augmenting masking objectives with various other tasks, such as predicting molecular properties derived
from molecular structures. However, the prediction of more complex properties(yielding richer representations of molecules) often relies on experimental annotations and, therefore, has limitations for pre-training. Finally, we briefly discussed practical considerations for experimental scientists when collecting new
LC-MS/MS data. Our first observation highlighted the homogeneity of GNPS
in terms of molecules, such as the prevalence of bile acids and the insufficient
representation of other compound classes. Ideally, the GNPS dataset should
include a more balanced distribution of underrepresented metabolites. Our second observation emphasized the difficulty for machine learning to learn from“dirty” data, such as chimeric spectra from dissolved organic matter (DOM)
samples. Therefore, it is important to explicitly mark or perform algorithmic
analysis on such datasets, simplifying their utilization as pre-training data.

Tuesday 14:00-15:30: Inductive bias & explainable AI
The session covered different ways of introducing prior knowledge into themodel (inductive bias), how this can be done a priori (by particular choicesof data/models/objectives) or a posteriori (via Explainable AI). The section
also covered more generally the question of explainability of ML models. The
meaning of inductive bias was first discussed, and we adopted a broad interpretation covering prior knowledge in the Bayesian sense, knowledge introducedby the human into the model, regularization, bias in the sense of bias/variance
tradeoff. Distinctions have also been made, e.g. between language bias andpreference bias. Afterwards, the practical motivations for introducing bias werediscussed, as well as some potential disadvantages compared to fully relying on
the data. A primary reason for introducing bias is to resolve limitations intrinsic
to the available data (e.g. imposing a preference between different spuriously
correlated features). When data coverage is comprehensive, however, the inductive bias can become a burden. Then, various forms of biases/prior knowledge
have been described for specific types of data and applications. This includes
biases specific to SMILES and SELFIES data, inductive biases on the level ofthe molecule (e.g. MSNovelist, feature engineering to specify a molecular formula) and inductive biases on the level of spectra. These biases on the spectra
include replacing mass with formulas, isotopes in the spectra, forcing representation similarity of different spectra from the sample, including features from
related spectra (e.g. adding chemical structure of the nearest neighbor spectrain the similarity group, possibly including other spectra types). Additionally,
possible refinements of the similarity measure between different spectra was discussed, for example, based on a chain of instances with strong mutual similarity.
Further examples of inductive biases have been discussed for the task of protein
prediction, where it was mentioned that a huge boost of performance is obtained
when including evolutionary structure (e.g. flavonoids). A second part of thesession has focused on the way bias should be introduced into the model for a
maximum benefit. The presence of spurious correlations in medical data (e.g.
caused by parameters of the data acquisition being correlated to the task) andthe resulting “Clever Hans” classifiers was stressed. The fact that not only thedata but also the ML algorithm may cause or influence the emergence of such
6

flaws was mentioned. Among methods for model improvement, a distinction
was made between proactive approaches (e.g. feature selection, regularization)and reactive approaches (e.g. based on explainable AI). A potential advantageof the latter approach is that it allows to only introduce prior knowledge whenthe model needs it (thereby not overriding what can be learned readily fromthe data). On the other hand, it requires a user-in-the-loop, which would work
well only in certain scenarios (e.g. with willing cooperating partners). The usefulness of interpretability for finding flaws in the model was discussed. It was
mentioned that different types of interpretability (e.g. attribution vs. counterfactuals, vs. support vectors) may be needed for different types of data in orderfor the explanation to be informative for the user. Lastly, the benefit of interpretability for enhancing acceptability of a ML model (independent on whether
it allows to improve the model or not) was mentioned.

Tuesday 16:00-18:00: Where is the data?
datasets, competitions

Standardized

One of the primary issues highlighted was the dispersed nature of the available
data. Numerous online repositories and datasets house relevant information,
making it difficult for researchers to identify relevant data resources. Various
data types were identified, including MS data, retention time data, ion mobility
data, and molecular data. Noteworthy online resources for these data types werementioned, such as GNPS, MoNA, MassBank, MetaboLights, NIST, METLIN
SMRT, RepoRT, PredRET, CCSbase, COCONUT, HMDB, PubChem, LOTUS, CheBI, and the boeckerlab biodatabase. To address this issue, we propose
collecting representative data and presenting it in a format that is easily usable
by ML practitioners without requiring extensive domain expertise. A previously compiled dataset from GNPS, which was employed in the development of
CANOPUS, will be updated and shared with the scientific community througha dedicated website. This dataset will be divided into appropriate training,validation, and testing data subsets. This approach aims to enable researchers
to develop and evaluate novel bioinformatics tools consistently, ensuring comparable results across studies. In addition to the MS data, the dataset will also
incorporate a biomolecule dataset serving as a structural database for searching purposes. Drawing inspiration from other fields that have greatly benefited
from structured competitions, such as ImageNet and CASP, we will organize a
new CASMI challenge, led by Tomas Pluskal. This competition will be divided
into two categories: (i) cases where the correct answer is available in PubChemand (ii) cases where the correct answer is missing from PubChem. In the latter category, participants will be evaluated based on the structural similarityof their proposed molecule with the ground truth molecule, rather than a binary 0/1 evaluation solely based on an exact match. This discussion highlighted
the urgent need to address the challenges associated with data accessibility andintegration in the field of computational metabolomics and machine learning.
By streamlining data access and establishing evaluation benchmarks, we aim
to facilitate innovation and drive progress in computational metabolomics andmachine learning.

7

Tuesday 16:00-18:00: Multi-OMICS
The session covered two specific multi-omics use cases: spatial omics, andmetabolic rare diseases. The first topic, spatial omics, concerns the combination of two relatively recent innovations: imaging mass spectrometry (iMS),and spatial transcriptomics . Both approaches create a very high dimensional‘pixel map’ of a slice of a tissue, an organism, or (a set of) bacterial colonies.
An interesting approach to combine the different data modalities is likely CCA.
CCA enables time courses, which could be adapted to a spatial dimension rather
than a time dimension. Moreover, sparse-nonlinear CCA holds the promise ofpicking up small numbers of features which might induce non-linear correlations(e.g. gradKCCA, SCCA-HSIC). Another suggestion was spatial mixture models, to capture and elucidate a possible genotype or phenotype mixture acrossthe image. This approach appears to mesh well with the diversity use cases (e.g.,differential drug uptake across cells in a tissue). The second topic concerned aconnection between proteomics data-driven protein-functional association networks, genomic sequencing data, and identified metabolites, as together thesecan reveal fine-grained information on the dysregulation of metabolic pathways
in metabolic rare diseases. These diseases, which often remain poorly or incompletely diagnosed, tend to have very few cases and several matched controls,
yet genomic sequencing (which is the standard approach to investigate these
diseases) usually reveals far too many variations to elucidate a clear point of
failure in the patient’s metabolism. Suggestions to cope with this situation
included explainable AI to visualize the induced ‘explanations’ for different individuals, and anomaly detection to find deviations from the norm across theavailable data. L1 or group LASSO regularization was also seen as relevant, as
the disease cause tends to be a single, or very few, mutation(s). Flux balance
analysis could help pick up metabolic fluxes through the pathways, but notconcentrations as enzyme kinetics are likely not known.

Wednesday 8:45-10:30: Hammers in search for nails
This session was an attempt to brainstorm novel applications for interestingalgorithms. We discussed the application of Explainable AI (XAI), which uses
different strategies to highlight particular features in a model input that are
the main determinants particular model prediction. Potential applications incomputational metabolomics include highlighting peaks in an input that are
responsible for the ranking of two candidates. Similarly, counterfactual explanations may contrast two input spectra and explain why there is a model
response in one but not another spectrum. Frequently, the input data to sucha model is not directly interpretable. A solution for this is an additional invertible transform (autoencoder) as first layers of the model, which maps themodel input to an interpretable counterpart. For proper results, it is mandatory that this transform have negligible reconstruction loss. This is particularly
important if trying to use XAI to find flaws in a model, possibly less relevant
if using XAI as a user-facing interpretability tool. Finding interpretable explanations on surrogate models should be avoided, since those might in fact
learn different algorithms to arrive at the same solution; for the case of “cleverHans” phenomena, they may be present in both, neither, or only one of thefull versus the surrogate model. We discussed autoencoders to detect anomalies8

in time series, where an increasing reconstruction loss shows an overall drift.
In clinical settings, where mass spectrometry will increasingly monitor larger
numbers of metabolites, such anomalies of interest could be similar to drift, as
in slowly deteriorating towards a medical problem. Similarly, in laboratory settings such models may indicate the need for preventive maintenance. Finally,
we considered Bloom filters, a probabilistic data structure to test for set membership. In the context of mass spectrometry, this could have applications in a
database search setting if combined with an appropriate discrete representationof spectra, such as locality-sensitive hashing methods.

Wednesday 8:45-10:30: Retention Time
Fleming and Sebastian, together with the group of Michael Witting (Munich),have created a repository for retention times(https://github.com/michaelwitting/RepoRT). The important point is that for
retention time (RT) and order predictions, you need to know several things
beyond the molecular structure itself: This includes the stationary phase (column), the mobile phase (eluents, gradient) and even the temperature. All ofthese things do affect retention time and even retention order. There was a short
discussion that even columns of the same type (e.g, C18) columns can have a(very) different retention order. The repository contains fewer than 10k uniquestructures, but Tomas offered to measure about 15k compounds. This would
substantially increase the covered chemical space. In the best case, multiple
measurements on different columns, but that would require major amounts oftime. We then had a longer discussion about stereochemistry: Much informationis still missing, so the way to go might be to first drop stereochemistry information altogether and only consider it later on when a foundation has been laid.
Other cases like mesomerism and tautomerism were also discussed; tautomers
could create two peaks in the chromatogram (so two RTs) but it’s also possible
to just have a broader peak. There is an unresolved issue that in some cases,
there are multiple RT values for a single compound. It is possible that these
are actually mixtures of different stereoisomers, or possibly something else. We
discussed how to handle those, and the feeling was: If those are indeed different stereoisomers, then take the average. Predicting the average would alreadybe helpful. We also discussed special purpose columns and how a model could
help users to select a column best suited for their application. Moving to mass
spectra in the context of stereochemistry, we decided that it is probably not an
issue right now (fragmentation similar; what happens in the gas stage is reallydifferent than what happens in solution anyways). There was a short discussion
on ClassyFire vs. NPClassifier regarding pros and cons; it looks like there is no
optimal solution right now.

Wednesday 11:00-12:00: Large scientific models“Large language models” (LLMs), Transformer-based models pre-trained withvast amounts of natural language data and a very large parameter space, havefound widespread application for any task related to natural language. LLMs
serve catch-all foundation model which can be fine-tuned for specific tasks withlimited amounts of data, frequently outperforming models designed for a specificpurpose. Herein, we explored the concept of a “large chemical model” or “chem9

ical foundation model”, pre-trained on very large chemical datasets, as a basis
for fine-tuning for tasks of interest specific for computational metabolomics. In
proteomics, protein sequence data has been used to train LLMs analogously
to natural language LLMs, e.g. by masking parts of the sequence. The latent representation of the resulting foundation model (ProteinBERT) was used
as an input to train peptide MS2 prediction, outperforming the state of theart, and showed promising performance in other protein-related tasks seemingly unrelated to the input data. An analogous attempt for small molecules
would entail masking parts of a molecule, e.g. subgraphs. However, masking inmolecules is not a promising choice, as there are hardly any meaningful restrictions on valid subgraphs. Possibly, masking bonds rather than atoms could bea more suitable task. More suited tasks for a base model could be translation
tasks (between chemical representations) or property prediction tasks. Some
properties (such as fingerprints) are probably insufficiently complex to learn afoundation model. Promising features could be results from density-functional
theory (DFT) calculations, for which multiple datasets are available. Further
discussion revolved around suitable datasets, e.g. MD17 and QM7-X. We suggest avoiding experimental data related to biology, such as toxicity. Finally,
since chemical property prediction is a key topic in drug development, we discussed whether existing models in these communities might be unknown to thecomputational mass spectrometry community. A quick literature survey reveals multiple BERT-inspired chemical models, though with unclear scope andperformance.

Wednesday 11:00-12:00: Metabolomics responses of drugs
We discussed the problem of learning from metabolomic time-series that result
as a response of administering a drug. Discussions focused on the discovery
of change points (e.g. the onset and probable path of a disease) and event
sequences from multiple time series, as well as feature extraction for predictivemodels. A major bottleneck is the lack of datasets in the public domain that
contain this type of information.

Thursday 11:00-11:30: Do current spectral libraries cover
the metabolite space well or poorly?
Different participants have come to different conclusions on whether the available mass spectrometry data is covering the “space of biomolecular structures”
well or poorly. We discussed that Tanimoto distances are not well-suited tomeasure chemical similarity or dissimilarity, despite their undoubted advantage
of allowing ultrafast screening. A better choice might be MCES distances (Maximum Common Edge Subgraph distance, graph edit distance). See the MCES
paper which claims that MS data are actually covering the space rather well.
There, also Jupyter Notebooks are provided, and one can interactively explore
the UMAP embeddings. Disadvantage of MCES is that a single instance requires between 0.1 and 1 seconds; so, downsampling is necessary. Roman’s
thesis (available soon) claims mostly the opposite: namely, that current MS
training data form small cluttered islands within the otherwise rather empty“sea of biomolecules”. We will have to see what statement is closer to the truth;
potentially both claims are true at the same time.
10Participant Abstracts
Bittremieux
There is a key need to develop novel bioinformatics tools that better communicate uncertainty in the data analysis results of untargeted metabolomics experiments. For example, currently isomeric alternatives for spectrum annotations are not or not sufficiently considered, even though these can likely not
be differentiated using mass spectrometry. Similarly, there is an urgent needto provide statistical confidence estimation of the spectrum annotation results.
Additionally, this should be combined with a community education effort. Acknowledgements: Wout Bittremieux was supported by a travel grant from the
Research Organization – Flanders (FWO).

Böcker
Large-scale datasets for small molecule structure may pose severe problems formachine learning models: This includes bias in the selection of training data, as
well as incompletely labeled training data due to mesomerism, tautomerism, andracemates. It is easy for a machine learning model to pick up this bias, resulting
in evaluation statistics much better than what we will observe in practice.

Bushuiev
Mass spectral libraries are limited to known molecules that can be easily acquired or isolated. To address this limitation, we are developing a self-supervised
deep learning model capable of extracting knowledge from millions of raw, unannotated mass spectra. We collected a dataset of 700 million experimental mass
spectra from diverse LC-MS/MS measurements and used it to train a large
neural network in a self-supervised manner. By training the network on artificial tasks, such as predicting masked sections of the input spectra, we observed
the emergence of rich molecular features derived directly from the experimental
mass spectra.

CorbeilOur team identifies disease signatures using metabolomics to inform diagnostics,
monitor treatments and develop new drugs. We combined high-throughput
mass spectrometry and machine learning to identify these signatures. For the
drug development approach, we use the signature as an objective function to
inject biological knowledge into a generative flow network to drive the processof finding new compounds of interest.

DührkopMy research involves developing machine learning methods for small molecule
annotation. Next to model architecture, the choice of input/output features is
crucial. While many methods use binned vectors for spectral input representation, they fail to capture high mass accuracy details and the combinatorial
nature of small molecule fragmentation. Fourier features combined with transformer methods show promise, but their ability to model mass deltas and mass11

defects remains uncertain. Kernel methods are well-suited for combinatorial
data like trees and currently outperform transformer models in small molecule
annotation. However, training times for kernel methods increase cubically withtraining data size. The Nyström approximation offers a solution by enabling
linear scaling of running times with additional training data and facilitating the
combination of deep neural network architectures with kernel methods.

DzeroskiMy research is best described by the keyphrase “Artificial Intelligence for Science”. It covers topics such as the analysis of complex data (relational learning,
structured output prediction, semi-supervised learning), automated modelingof dynamic systems, and ontologies for (computer) science. The connection tometabolomics is through existing work on the analysis of spectrometry data, including mass spectrometry and Raman spectrometry data, and planned work on
relating mass spectrometry data (MALDI-TOF, FTIR) to pathogen information(identification, predicting antibiotic resistance profiles).

Heinonen
My research interests are in probabilistic deep learning, dynamical systems andin generative modeling, with examples in molecular and protein complex generative models. Current molecular generative models are largely isolated for one
task and still struggle to understand simple concepts such as valencies. There isa need for chemistry-aligned embeddings that understand chemical structures,
energy landscapes, molecular properties and interactions; conceptually similar
to the ‘foundation’ models of language and images.

Käll
My group is developing methods for interpreting high-throughput experiments,
particularly for mass spectrometry-based proteomics. In my talk I discussed anovel type of multiomics experiment I have been obtaining data from, a spatial
co-analysis of transcripts and metabolites in tissue. The data is obtained byfirst applying MALDI Imaging mass spectrometry, and subsequently performing
Visium analysis for the same tissue. We see this as a promising resource fordetermining covariation between metabolites and transcripts (or lack thereof).

Kretschmer
A generalizable model for retention time (better: order) prediction requires botha large collection of diverse datasets in terms of chromatographic setups usedand structures measured. A repository of suitable datasets with a numeric description of the chromatographic setup, easily pluggable into machine learning,
is now available (RepoRT), but important challenges to make the most out of
training data still have to be overcome. How to handle wrongly labeled data,stereoisomers and tautomers are among the most pressing issues.

12Mamitsuka
Machine learning on graphs, higher-dimensional data (tensor and hypergraph)and data integration, and bioinformatics on sequences, networks, biomedical
text mining and a variety of drug-related issues are the current major research
topics of my group. A recent topic, a hypergraph neural network-based sparse
stochastic block model for predicting drug-drug interactions (adverse drug effects caused by drug pairs) was briefly presented.

Martens
My group is looking into the interface between metabolomics and proteomics,
as evidenced by metabolites (or metabolic intermediates) that are found as
modifications on proteins. This promising avenue of research, for which wealready have some first indications of biological and biomedical relevance, islikely to prove to be a fruitful endeavor over the next years. But it is clear thatany decent effort in this area will benefit greatly from interactive involvementof both the proteomics and the metabolomics communities.

MontavonMy research interests are in the development of Explainable AI approaches
targeted for practical applications. This includes methods to systematicallyinspect a ML model, e.g. to detect the potential use of spurious features (aka.
Clever Hans effects) by the model, and remove them from the model. Anotherset of applications are to gain novel insights into complex systems of interest(seen through a ML model), for example, for predicting proteomic networks.

Nakamukai
The retention time in LC/MS is one of the information for compound identification. However, different measurement conditions result in giving different
retention time values. Therefore, the objective of my research is to explore theuse of retention order in LC/MS for compound identification, especially peptide
natural products whose database information is limited. To address the issue oflimited training data in the database, I plan to use peptide data derived fromthe peptidomics to train the amino acid portion and to use small molecules fromthe databases to train modifications.

NguyenWith the rapid development of machine learning (ML), there is much potential for data-driven biological knowledge discovery. However, it is not straightforward due to the complexity of the domain knowledge inherent in the data
and also there are rich interdependencies among biological components such asatoms, molecules, cells or organisms. ML approaches in this domain usually
involve analyzing such interdependence structures encoded by graphs. My research focuses on developing new ML methods for such kinds of graph structured
data with theoretical guarantees and improved performances compared to thestate-of-the-art methods.

13Pluskal
Although plants are an incredibly rich source of pharmaceutically relevant specialized metabolites, biosynthetic pathway elucidation in plants has proven challenging. My lab is developing workflows for connecting biosynthetic gene sequences (RNAseq data) to their downstream metabolites (LC-MS data). For
this, we designed a “top-down” approach based on correlating expression levels of enzymes with metabolite abundance across a large plant family, and a“bottom-up” approach based on predicting the substrate specificity and catalytic function of individual biosynthetic enzymes directly from their sequencesusing self-supervised deep learning. We are also developing large-scale foundational deep learning models for mass spectra that could be used for predictionsof chemical structures and for assessing the novelty of detected natural products.

Rousu
The goal of my research group is to develop principled machine learning methods
for predicting structured, non-tabular data arising in biomedicine, drug discovery and systems biology. Our current methodological focus includes tensorbased models, sparse kernel models and representation learning for structured
objects. Applications include drug combination prediction, complex biomarker
discovery, and enzyme function prediction.

Saigo
My research interests are in machine learning and its application to bio/chem
informatics. My first topic was about supervised learning of small compounds,
in which kernel methods and GNN are the popular choices, I have introduced
an interpretable alternative that makes a linear model out of the space of all
the subgraphs. My second topic was about prediction of chemical reactions, inwhich the metabolic network is regarded as a graph, where nodes and edges arecompounds and reactions, respectively, and the task is to fill missing reaction
categories.

StravsCurrent methods for structure elucidation of small molecules rely on finding
similarity with spectra of known compounds, but do not predict structures denovo for unknown compound classes. Existing methods (CSI:FingerID) predict molecular fingerprints from MS2 spectra, and search for matching chemical
structures in databases. MSNovelist uses the rich structural information inpredicted molecular fingerprints as an input for molecule generation. For thispurpose, a LSTM neural network was enhanced with feature engineering to favor
the formation of compounds with specified molecular formulas, including a selfsupervised LSTM to learn implicit hydrogen atoms on SMILES. The resulting
model is able to reproduce 60

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