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Researcher profile

Yves Moreau

Yves Moreau contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine LearningInformation Theorymath.ATmath.ITmath.PR

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Published work

7 published item(s)

preprint2026arXiv

A Comparative Study of QSPR Methods on a Unique Multitask PAMPA dataset

We present a unique, multitask dataset comprising 143 drug and drug candidate molecules, each evaluated on in vitro, parallel artificial-membrane permeability assays (PAMPA) using six different model membranes. Using this resource, we systematically assess the effectiveness of various molecular descriptors and regression models in predicting passive membrane permeability. The studied models range from simple linear regression to a modern pre-trained transformer architecture. Particular attention is given to the trade-off between predictive performance and model interpretability, highlighting the challenges introduced by machine learning approaches. To our knowledge, this is the most comprehensive study on simultaneous modeling of multiple organ-specific PAMPA membranes to date, offering novel insights into membrane-specific permeability profiles. We found that expert-designed physico-chemical property descriptors are more fitting for a limited sample size permeabilty study than deep learning based representations.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Central Limit Theorems for Martin-Löf Random Numbers

We prove two theorems related to the Central Limit Theorem (CLT) for Martin-Löf Random (MLR) sequences. Martin-Löf randomness attempts to capture what it means for a sequence of bits to be "truly random". By contrast, CLTs do not make assertions about the behavior of a single random sequence, but only on the distributional behavior of a sequence of random variables. Semantically, we usually interpret CLTs as assertions about the collective behavior of infinitely many sequences. Yet, our intuition is that if a sequence of bits is "truly random", then it should provide a "source of randomness" for which CLT-type results should hold. We tackle this difficulty by using a sampling scheme that generates an infinite number of samples from a single binary sequence. We show that when we apply this scheme to a Martin-Löf random sequence, the empirical moments and cumulative density functions (CDF) of these samples tend to their corresponding counterparts for the normal distribution. We also prove the well known almost sure central limit theorem (ASCLT), which provides an alternative, albeit less intuitive, answer to this question. Both results are also generalized for Schnorr random sequences.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SparseChem: Fast and accurate machine learning model for small molecules

SparseChem provides fast and accurate machine learning models for biochemical applications. Especially, the package supports very high-dimensional sparse inputs, e.g., millions of features and millions of compounds. It is possible to train classification, regression and censored regression models, or combination of them from command line. Additionally, the library can be accessed directly from Python. Source code and documentation is freely available under MIT License on GitHub.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Topological Graph Neural Networks

Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles. We present TOGL, a novel layer that incorporates global topological information of a graph using persistent homology. TOGL can be easily integrated into any type of GNN and is strictly more expressive (in terms the Weisfeiler--Lehman graph isomorphism test) than message-passing GNNs. Augmenting GNNs with TOGL leads to improved predictive performance for graph and node classification tasks, both on synthetic data sets, which can be classified by humans using their topology but not by ordinary GNNs, and on real-world data.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Self-Labeling of Fully Mediating Representations by Graph Alignment

To be able to predict a molecular graph structure ($W$) given a 2D image of a chemical compound ($U$) is a challenging problem in machine learning. We are interested to learn $f: U \rightarrow W$ where we have a fully mediating representation $V$ such that $f$ factors into $U \rightarrow V \rightarrow W$. However, observing V requires detailed and expensive labels. We propose graph aligning approach that generates rich or detailed labels given normal labels $W$. In this paper we investigate the scenario of domain adaptation from the source domain where we have access to the expensive labels $V$ to the target domain where only normal labels W are available. Focusing on the problem of predicting chemical compound graphs from 2D images the fully mediating layer is represented using the planar embedding of the chemical graph structure we are predicting. The use of a fully mediating layer implies some assumptions on the mechanism of the underlying process. However if the assumptions are correct it should allow the machine learning model to be more interpretable, generalize better and be more data efficient at training time. The empirical results show that, using only 4000 data points, we obtain up to 4x improvement of performance after domain adaptation to target domain compared to pretrained model only on the source domain. After domain adaptation, the model is even able to detect atom types that were never seen in the original source domain. Finally, on the Maybridge data set the proposed self-labeling approach reached higher performance than the current state of the art.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning

In drug discovery, knowledge of the graph structure of chemical compounds is essential. Many thousands of scientific articles in chemistry and pharmaceutical sciences have investigated chemical compounds, but in cases the details of the structure of these chemical compounds is published only as an images. A tool to analyze these images automatically and convert them into a chemical graph structure would be useful for many applications, such drug discovery. A few such tools are available and they are mostly derived from optical character recognition. However, our evaluation of the performance of those tools reveals that they make often mistakes in detecting the correct bond multiplicity and stereochemical information. In addition, errors sometimes even lead to missing atoms in the resulting graph. In our work, we address these issues by developing a compound recognition method based on machine learning. More specifically, we develop a deep neural network model for optical compound recognition. The deep learning solution presented here consists of a segmentation model, followed by three classification models that predict atom locations, bonds and charges. Furthermore, this model not only predicts the graph structure of the molecule but also produces all information necessary to relate each component of the resulting graph to the source image. This solution is scalable and could rapidly process thousands of images. Finally, we compare empirically the proposed method to a well-established tool and observe significant error reductions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Expressive Graph Informer Networks

Applying machine learning to molecules is challenging because of their natural representation as graphs rather than vectors.Several architectures have been recently proposed for deep learning from molecular graphs, but they suffer from informationbottlenecks because they only pass information from a graph node to its direct neighbors. Here, we introduce a more expressiveroute-based multi-attention mechanism that incorporates features from routes between node pairs. We call the resulting methodGraph Informer. A single network layer can therefore attend to nodes several steps away. We show empirically that the proposedmethod compares favorably against existing approaches in two prediction tasks: (1) 13C Nuclear Magnetic Resonance (NMR)spectra, improving the state-of-the-art with an MAE of 1.35 ppm and (2) predicting drug bioactivity and toxicity. Additionally, wedevelop a variant called injective Graph Informer that isprovablyas powerful as the Weisfeiler-Lehman test for graph isomorphism.Furthermore, we demonstrate that the route information allows the method to be informed about thenonlocal topologyof the graphand, thus, even go beyond the capabilities of the Weisfeiler-Lehman test.

0 citations0 reviewsNo code linkedOpen access