A Unified Framework for Certificate and Compilation for QBF. - Igor Stéphan; Benoit Da Mota (ICLA, 2009)

From (Quantified) Boolean Formulae to Answer Set Programming. - Igor Stéphan; Benoit Da Mota; Pascal Nicolas (J. Log. Comput., 2009)

A new parallel architecture for QBF tools. - Benoit Da Mota; Pascal Nicolas; Igor Stéphan (HPCS, 2010)

Formules booléennes quantifiées : transformations formelles et calculs parallèles. (Quantified boolean formulae : formal processings and parallel computations). - Benoit Da Mota (2010)

Équivalences et forme prénexe pour les formules booléennes quantifiées. - Benoit Da Mota (Rev. d'Intelligence Artif., 2011)

A-Brain: Using the Cloud to Understand the Impact of Genetic Variability on the Brain. - Gabriel Antoniu; Alexandru Costan; Benoit Da Mota; Bertrand Thirion; Radu Tudoran (ERCIM News, 2012)

Enhancing the Reproducibility of Group Analysis with Randomized Brain Parcellations. - Benoit Da Mota; Virgile Fritsch; Gaël Varoquaux; Vincent Frouin; Jean-Baptiste Poline; Bertrand Thirion (MICCAI (2), 2013)

Robust Group-Level Inference in Neuroimaging Genetic Studies. - Virgile Fritsch; Benoit Da Mota; Gaël Varoquaux; Vincent Frouin; Eva Loth; Jean-Baptiste Poline; Bertrand Thirion (PRNI, 2013)

Generic Machine Learning Pattern for Neuroimaging-Genetic Studies in the Cloud. - Benoit Da Mota; Radu Tudoran; Alexandru Costan; Gaël Varoquaux; Goetz Brasche; Patricia J. Conrod; Hervé Lemaître; Tomás Paus; Marcella Rietschel; Vincent Frouin; Jean-Baptiste Poline; Gabriel Antoniu; et al. (Frontiers Neuroinformatics, 2014)

Randomized parcellation based inference. - Benoit Da Mota; Virgile Fritsch; Gaël Varoquaux; Tobias Banaschewski; Gareth J. Barker; Arun L. W. Bokde; Uli Bromberg; Patricia J. Conrod; Jürgen Gallinat; Hugh Garavan; Jean-Luc Martinot; Frauke Nees; et al. (NeuroImage, 2014)

Robust regression for large-scale neuroimaging studies. - Virgile Fritsch; Benoit Da Mota; Eva Loth; Gaël Varoquaux; Tobias Banaschewski; Gareth J. Barker; Arun L. W. Bokde; Rüdiger Brühl; Brigitte Butzek; Patricia J. Conrod; Herta Flor; Hugh Garavan; et al. (NeuroImage, 2015)

Des réseaux de neurones pour prédire des distances interatomiques extraites d'une base de données ouverte de calculs en chimie quantique. - Jules Leguy; Thomas Cauchy; Béatrice Duval; Benoit Da Mota (EGC, 2019)

Predicting Interatomic Distances of Molecular Quantum Chemistry Calculations. - Jules Leguy; Thomas Cauchy; Béatrice Duval; Benoit Da Mota (EGC (best of volume), 2019)

Dataset's chemical diversity limits the generalizability of machine learning predictions. - Marta Glavatskikh; Jules Leguy; Gilles Hunault; Thomas Cauchy; Benoit Da Mota (J. Cheminformatics, 2019)

EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation. - Jules Leguy; Thomas Cauchy; Marta Glavatskikh; Béatrice Duval; Benoit Da Mota (J. Cheminformatics, 2020)

Surrogate-Based Black-Box Optimization Method for Costly Molecular Properties. - Jules Leguy; Béatrice Duval; Benoit Da Mota; Thomas Cauchy (ICTAI, 2021)

Scalable estimator of the diversity for de novo molecular generation resulting in a more robust QM dataset (OD9) and a more efficient molecular optimization. - Jules Leguy; Marta Glavatskikh; Thomas Cauchy; Benoit Da Mota (J. Cheminformatics, 2021)

A Transformer Model for Predicting Chemical Reaction Products from Generic Templates. - Derin Ozer; Sylvain Lamprier; Thomas Cauchy; Nicolas Gutowski; Benoit Da Mota (CoRR, 2025)