Výsledky vyhledávání - "Guallar, Víctor"

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ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain

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

This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem

Externí odkaz: http://arxiv.org/abs/2409.05211

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

Autor: Ciudad, Álvaro, Morales-Pastor, Adrián, Malo, Laura, Filella-Mercè, Isaac, Guallar, Victor, Molina, Alexis

In this study, we present ScoreFormer, a novel graph transformer model designed to accurately predict molecular docking scores, thereby optimizing high-throughput virtual screening (HTVS) in drug discovery. The architecture integrates Principal Neigh

Externí odkaz: http://arxiv.org/abs/2406.09346

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Optimizing Drug Design by Merging Generative AI With Active Learning Frameworks

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

Traditional drug discovery programs are being transformed by the advent of machine learning methods. Among these, Generative AI methods (GM) have gained attention due to their ability to design new molecules and enhance specific properties of existin

Externí odkaz: http://arxiv.org/abs/2305.06334

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