Philippe Schwaller

According to our database, Philippe Schwaller authored at least 31 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Leveraging large language models for predictive chemistry.

Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, Berend Smit

Nat. Mac. Intell., 2024

Machine learning-aided generative molecular design.

Yuanqi Du, Arian R. Jamasb, Jeff Guo, Tianfan Fu, Charles Harris, Yingheng Wang, Chenru Duan, Pietro Liò, Philippe Schwaller, Tom L. Blundell

Nat. Mac. Intell., 2024

Augmenting large language models with chemistry tools.

Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D. White, Philippe Schwaller

Nat. Mac. Intell., 2024

It Takes Two to Tango: Directly Optimizing for Constrained Synthesizability in Generative Molecular Design.

Jeff Guo, Philippe Schwaller

CoRR, 2024

Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research.

Victor Sabanza Gil, Riccardo Barbano, Daniel Pacheco Gutiérrez, Jeremy S. Luterbacher, José Miguel Hernández-Lobato, Philippe Schwaller, Loïc Roch

CoRR, 2024

Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants.

Beatriz Borges, Negar Foroutan, Deniz Bayazit, Anna Sotnikova, Syrielle Montariol, Tanya Nazaretzky, Mohammadreza Banaei, Alireza Sakhaeirad, Philippe Servant, Seyed Parsa Neshaei, Jibril Frej, Angelika Romanou, Gail Weiss, Sepideh Mamooler, Zeming Chen, Simin Fan, Silin Gao, Mete Ismayilzada, Debjit Paul, Alexandre Schöpfer, Andrej Janchevski, Anja Tiede, Clarence Linden, Emanuele Troiani, Francesco Salvi, Freya Behrens, Giacomo Orsi, Giovanni Piccioli, Hadrien Sevel, Louis Coulon, Manuela Pineros-Rodriguez, Marin Bonnassies, Pierre Hellich, Puck van Gerwen, Sankalp Gambhir, Solal Pirelli, Thomas Blanchard, Timothée Callens, Toni Abi Aoun, Yannick Calvino Alonso, Yuri Cho, Alberto Silvio Chiappa, Antonio Sclocchi, Étienne Bruno, Florian Hofhammer, Gabriel Pescia, Geovani Rizk, Leello Dadi, Lucas Stoffl, Manoel Horta Ribeiro, Matthieu Bovel, Yueyang Pan, Aleksandra Radenovic, Alexandre Alahi, Alexander Mathis, Anne-Florence Bitbol, Boi Faltings, Cécile Hébert, Devis Tuia, François Maréchal, George Candea, Giuseppe Carleo, Jean-Cédric Chappelier, Nicolas Flammarion, Jean-Marie Fürbringer, Jean-Philippe Pellet, Karl Aberer, Lenka Zdeborová, Marcel Salathé, Martin Jaggi, Martin Rajman, Mathias Payer, Matthieu Wyart, Michael Gastpar, Michele Ceriotti, Ola Svensson, Olivier Lévêque, Paolo Ienne, Rachid Guerraoui, Robert West, Sanidhya Kashyap, Valerio Piazza, Viesturs Simanis, Viktor Kuncak, Volkan Cevher, Philippe Schwaller, Sacha Friedli, Patrick Jermann, Tanja Käser, Antoine Bosselut

CoRR, 2024

Gradient Guided Hypotheses: A unified solution to enable machine learning models on scarce and noisy data regimes.

Paulo Neves, Jörg K. Wegner, Philippe Schwaller

CoRR, 2024

Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation.

Jeff Guo, Philippe Schwaller

CoRR, 2024

Are large language models superhuman chemists?

Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu, Benedict Emoekabu, Aswanth Krishnan, Mara Wilhelmi, Macjonathan Okereke, Juliane Eberhardt, Amir Mohammad Elahi, Maximilian Greiner, Caroline T. Holick, Tanya Gupta, Mehrdad Asgari, Christina Glaubitz, Lea C. Klepsch, Yannik Köster, Jakob Meyer, Santiago Miret, Tim Hoffmann, Fabian Alexander Kreth, Michael Ringleb, Nicole Roesner, Ulrich S. Schubert, Leanne M. Stafast, Dinga Wonanke, Michael Pieler, Philippe Schwaller, Kevin Maik Jablonka

CoRR, 2024

ODEFormer: Symbolic Regression of Dynamical Systems with Transformers.

Stéphane d'Ascoli, Sören Becker, Philippe Schwaller, Alexander Mathis, Niki Kilbertus

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design.

Jeff Guo, Philippe Schwaller

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

From intuition to AI: evolution of small molecule representations in drug discovery.

Miles McGibbon, Steven R. Shave, Jie Dong, Yumiao Gao, Douglas R. Houston, Jiancong Xie, Yuedong Yang, Philippe Schwaller, Vincent Blay

Briefings Bioinform., November, 2023

Molecular Hypergraph Neural Networks.

Junwu Chen, Philippe Schwaller

CoRR, 2023

FSscore: A Machine Learning-based Synthetic Feasibility Score Leveraging Human Expertise.

Rebecca M. Neeser, Bruno Correia, Philippe Schwaller

CoRR, 2023

Holistic chemical evaluation reveals pitfalls in reaction prediction models.

Victor Sabanza Gil, Andres M. Bran, Malte Franke, Remi Schlama, Jeremy S. Luterbacher, Philippe Schwaller

CoRR, 2023

Extracting human interpretable structure-property relationships in chemistry using XAI and large language models.

Geemi P. Wellawatte, Philippe Schwaller

CoRR, 2023

Transformers and Large Language Models for Chemistry and Drug Discovery.

Andres M. Bran, Philippe Schwaller

CoRR, 2023

14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon.

Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali, Shruti Badhwar, Joshua D. Bocarsly, Andres M. Bran, Stefan Bringuier, L. Catherine Brinson, Kamal Choudhary, Defne Circi, Sam Cox, Wibe A. de Jong, Matthew L. Evans, Nicolas Gastellu, Jerome Genzling, María Victoria Gil, Ankur K. Gupta, Zhi Hong, Alishba Imran, Sabine Kruschwitz, Anne Labarre, Jakub Lála, Tao Liu, Steven Ma, Sauradeep Majumdar, Garrett W. Merz, Nicolas Moitessier, Elias Moubarak, Beatriz Mouriño, Brenden Pelkie, Michael Pieler, Mayk Caldas Ramos, Bojana Rankovic, Samuel G. Rodriques, Jacob N. Sanders, Philippe Schwaller, Marcus Schwarting, Jiale Shi, Berend Smit, Ben E. Smith, Joren Van Heck, Christoph Völker, Logan T. Ward, Sean Warren, Benjamin Weiser, Sylvester Zhang, Xiaoqi Zhang, Ghezal Ahmad Zia, Aristana Scourtas, K. J. Schmidt, Ian T. Foster, Andrew D. White, Ben Blaiszik

CoRR, 2023

Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design.

Jeff Guo, Philippe Schwaller

CoRR, 2023

GAUCHE: A Library for Gaussian Processes in Chemistry.

Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Yuanqi Du, Samuel Stanton, Gary Tom, Bojana Rankovic, Arian Rokkum Jamasb, Aryan Deshwal, Julius Schwartz, Austin Tripp, Gregory Kell, Simon Frieder, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Johannes Peter Dürholt, Saudamini Chaurasia, Ji Won Park, Felix Strieth-Kalthoff, Alpha A. Lee, Bingqing Cheng, Alán Aspuru-Guzik, Philippe Schwaller, Jian Tang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

SELFIES and the future of molecular string representations.

Mario Krenn, Qianxiang Ai, Senja Barthel, Nessa Carson, Angelo Frei, Nathan C. Frey, Pascal Friederich, Théophile Gaudin, Alberto Alexander Gayle, Kevin Maik Jablonka, Rafael F. Lameiro, Dominik Lemm, Alston Lo, Seyed Mohamad Moosavi, José Manuel Nápoles-Duarte, AkshatKumar Nigam, Robert Pollice, Kohulan Rajan, Ulrich Schatzschneider, Philippe Schwaller, Marta Skreta, Berend Smit, Felix Strieth-Kalthoff, Chong Sun, Gary Tom, Guido Falk von Rudorff, Andrew Wang, Andrew D. White, Adamo Young, Rose Yu, Alán Aspuru-Guzik

Patterns, 2022

GAUCHE: A Library for Gaussian Processes in Chemistry.

Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Bojana Rankovic, Yuanqi Du, Arian R. Jamasb, Julius Schwartz, Austin Tripp, Gregory Kell, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Alpha A. Lee, Philippe Schwaller, Jian Tang

CoRR, 2022

2021

Unassisted noise reduction of chemical reaction datasets.

Alessandra Toniato, Philippe Schwaller, Antonio Cardinale, Joppe Geluykens, Teodoro Laino

Nat. Mach. Intell., 2021

Mapping the space of chemical reactions using attention-based neural networks.

Philippe Schwaller, Daniel Probst, Alain C. Vaucher, Vishnu H. Nair, David Kreutter, Teodoro Laino, Jean-Louis Reymond

Nat. Mach. Intell., 2021

Prediction of chemical reaction yields using deep learning.

Philippe Schwaller, Alain C. Vaucher, Teodoro Laino, Jean-Louis Reymond

Mach. Learn. Sci. Technol., 2021

Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design.

Ryan-Rhys Griffiths, Philippe Schwaller, Alpha A. Lee

CoRR, 2021

Unassisted Noise Reduction of Chemical Reaction Data Sets.

Alessandra Toniato, Philippe Schwaller, Antonio Cardinale, Joppe Geluykens, Teodoro Laino

CoRR, 2021

2020

Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery.

Hakime Öztürk, Arzucan Özgür, Philippe Schwaller, Teodoro Laino, Elif Özkirimli Ölmez

CoRR, 2020

2019

Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy.

Philippe Schwaller, Riccardo Petraglia, Valerio Zullo, Vishnu H. Nair, Rico Andreas Haeuselmann, Riccardo Pisoni, Costas Bekas, Anna Iuliano, Teodoro Laino

CoRR, 2019

2018

Molecular Transformer for Chemical Reaction Prediction and Uncertainty Estimation.

Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Costas Bekas, Alpha A. Lee

CoRR, 2018

2017

"Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models.

Philippe Schwaller, Théophile Gaudin, David Lanyi, Costas Bekas, Teodoro Laino

CoRR, 2017