Alexis Molina

According to our database, Alexis Molina authored at least 14 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Addressing model overcomplexity in drug-drug interaction prediction with molecular fingerprints.

Manel Gil-Sorribes, Alexis Molina

J. Cheminformatics, December, 2026

Effective Resistance Rewiring: A Simple Topological Correction for Over-Squashing.

Bertran Miquel-Oliver, Manel Gil-Sorribes, Victor Guallar, Alexis Molina

CoRR, March, 2026

Tensor-DTI: Enhancing Biomolecular Interaction Prediction with Contrastive Embedding Learning.

Manel Gil-Sorribes, Julia Vilalta Mor, Isaac Filella-Merce, Robert Soliva, Álvaro Ciudad, Victor Guallar, Alexis Molina

CoRR, January, 2026

2025

Sesame: Opening the door to protein pockets.

Raúl Miñán, Carles Perez-Lopez, Javier Iglesias, Álvaro Ciudad, Alexis Molina

CoRR, September, 2025

Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation.

Julia Vilalta Mor, Alexis Molina, Laura Ortega Varga, Isaac Filella-Merce, Victor Guallar

CoRR, June, 2025

sHGCN: Simplified hyperbolic graph convolutional neural networks.

Pol Arévalo, Alexis Molina, Álvaro Ciudad

CoRR, June, 2025

How Feasible Is Docking of PROTACs to POI-E3L Complexes? Testing Physics-Based and ML-Based Docking Tools.

Roberto Jiménez-Boi, Raúl Miñán, Chiara Pallara, Estrella Lozoya, Victor Guallar, Alexis Molina, Robert Soliva

J. Chem. Inf. Model., 2025

TopoBench: A Framework for Benchmarking Topological Deep Learning.

Lev Telyatnikov, Guillermo Bernárdez, Marco Montagna, Mustafa Hajij, Martin Carrasco, Pavlo Vasylenko, Mathilde Papillon, Ghada Zamzmi, Michael T. Schaub, Jonas Verhellen, Pavel Snopov, Bertran Miquel-Oliver, Manel Gil-Sorribes, Alexis Molina, Victor Guallar, Theodore Long, Julian Suk, Patryk Rygiel, Alexander V. Nikitin, Giordan Escalona, Michael Banf, Dominik Filipiak, Liliya Imasheva, Max Schattauer, Alvaro L. Martinez, Halley Fritze, Marissa Masden, Valentina Sánchez, Manuel Lecha, Andrea Cavallo, Claudio Battiloro, Matthew Piekenbrock, Mauricio Tec, George Dasoulas, Nina Miolane, Simone Scardapane, Theodore Papamarkou

J. Data-centric Mach. Learn. Res., 2025

2024

Character-level Tokenizations as Powerful Inductive Biases for RNA Foundational Models.

Adrián Morales-Pastor, Raquel Vázquez-Reza, Milosz Wieczór, Clàudia Valverde, Manel Gil-Sorribes, Bertran Miquel-Oliver, Álvaro Ciudad, Alexis Molina

CoRR, 2024

Scoreformer: A Surrogate Model For Large-Scale Prediction of Docking Scores.

Álvaro Ciudad, Adrián Morales-Pastor, Laura Malo, Isaac Filella-Merce, Victor Guallar, Alexis Molina

CoRR, 2024

Are Protein Language Models Compute Optimal?

Yaiza Serrano, Álvaro Ciudad, Alexis Molina

CoRR, 2024

GeoDirDock: Guiding Docking Along Geodesic Paths.

Raúl Miñán, Javier Gallardo, Álvaro Ciudad, Alexis Molina

CoRR, 2024

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain.

Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna, Federica Baccini, Mathilde Papillon, Miquel Ferriol-Galmés, Mustafa Hajij, Theodore Papamarkou, Maria Sofia Bucarelli, Olga Zaghen, Johan Mathe, Audun Myers, Scott Mahan, Hansen Lillemark, Sharvaree Vadgama, Erik J. Bekkers, Tim Doster, Tegan Emerson, Henry Kvinge, Katrina Agate, Nesreen K. Ahmed, Pengfei Bai, Michael Banf, Claudio Battiloro, Maxim Beketov, Paul Bogdan, Martin Carrasco, Andrea Cavallo, Yun Young Choi, George Dasoulas, Matous Elphick, Giordan Escalona, Dominik Filipiak, Halley Fritze, Thomas Gebhart, Manel Gil-Sorribes, Salvish Goomanee, Victor Guallar, Liliya Imasheva, Andrei Irimia, Hongwei Jin, Graham Johnson, Nikos Kanakaris, Boshko Koloski, Veljko Kovac, Manuel Lecha, Minho Lee, Pierrick Leroy, Theodore Long, German Magai, Alvaro Martinez, Marissa Masden, Sebastian Meznar, Bertran Miquel-Oliver, Alexis Molina, Alexander Nikitin, Marco Nurisso, Matt Piekenbrock, Yu Qin, Patryk Rygiel, Alessandro Salatiello, Max Schattauer, Pavel Snopov, Julian Suk, Valentina Sánchez, Mauricio Tec, Francesco Vaccarino, Jonas Verhellen, Frédéric Wantiez, Alexander Weers, Patrik Zajec, Blaz Skrlj, Nina Miolane

Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) at ICML 2024, 2024

2023

Optimizing Drug Design by Merging Generative AI With Active Learning Frameworks.

Isaac Filella-Merce, Alexis Molina, Marek Orzechowski, Lucía Díaz, Yang Ming Zhu, Julia Vilalta Mor, Laura Malo, Ajay S. Yekkirala, Soumya S. Ray, Victor Guallar

CoRR, 2023