Philippe Schwaller

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 32

Top Topics

Materials Science

Chemical Reaction

Top Venues

Nat. Mac. Intell.

Nat. Mach. Intell.

Publications

Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, Berend Smit
Leveraging large language models for predictive chemistry. Nat. Mac. Intell. 6 (2) (2024)
Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D. White, Philippe Schwaller
Augmenting large language models with chemistry tools. Nat. Mac. Intell. 6 (5) (2024)
Paulo Neves, Jörg K. Wegner, Philippe Schwaller
Gradient Guided Hypotheses: A unified solution to enable machine learning models on scarce and noisy data regimes. CoRR (2024)
Yuanqi Du, Arian R. Jamasb, Jeff Guo, Tianfan Fu, Charles Harris, Yingheng Wang, Chenru Duan, Pietro Liò, Philippe Schwaller, Tom L. Blundell
Machine learning-aided generative molecular design. Nat. Mac. Intell. 6 (6) (2024)
Jeff Guo, Philippe Schwaller
Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation. CoRR (2024)
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
Are large language models superhuman chemists? CoRR (2024)
Jeff Guo, Philippe Schwaller
Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design. ICLR (2024)
Stéphane d'Ascoli, Sören Becker, Philippe Schwaller, Alexander Mathis, Niki Kilbertus
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers. ICLR (2024)
Jeff Guo, Philippe Schwaller
Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design. CoRR (2023)
Geemi P. Wellawatte, Philippe Schwaller
Extracting human interpretable structure-property relationships in chemistry using XAI and large language models. CoRR (2023)
Victor Sabanza Gil, Andres M. Bran, Malte Franke, Remi Schlama, Jeremy S. Luterbacher, Philippe Schwaller
Holistic chemical evaluation reveals pitfalls in reaction prediction models. CoRR (2023)
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
GAUCHE: A Library for Gaussian Processes in Chemistry. NeurIPS (2023)
Stéphane d'Ascoli, Sören Becker, Alexander Mathis, Philippe Schwaller, Niki Kilbertus
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers. CoRR (2023)
Junwu Chen, Philippe Schwaller
Molecular Hypergraph Neural Networks. CoRR (2023)
Jeff Guo, Philippe Schwaller
Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design. CoRR (2023)
Andres M. Bran, Philippe Schwaller
Transformers and Large Language Models for Chemistry and Drug Discovery. CoRR (2023)
Rebecca M. Neeser, Bruno Correia, Philippe Schwaller
FSscore: A Machine Learning-based Synthetic Feasibility Score Leveraging Human Expertise. CoRR (2023)
Miles McGibbon, Steven R. Shave, Jie Dong, Yumiao Gao, Douglas R. Houston, Jiancong Xie, Yuedong Yang, Philippe Schwaller, Vincent Blay
From intuition to AI: evolution of small molecule representations in drug discovery. Briefings Bioinform. 25 (1) (2023)
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
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon. CoRR (2023)
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
SELFIES and the future of molecular string representations. CoRR (2022)
Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Bojana Rankovic, Yuanqi Du, Arian Jamasb, Julius Schwartz, Austin Tripp, Gregory Kell, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Alpha A. Lee, Philippe Schwaller, Jian Tang
GAUCHE: A Library for Gaussian Processes in Chemistry. CoRR (2022)
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
SELFIES and the future of molecular string representations. Patterns 3 (10) (2022)
Alessandra Toniato, Philippe Schwaller, Antonio Cardinale, Joppe Geluykens, Teodoro Laino
Unassisted noise reduction of chemical reaction datasets. Nat. Mach. Intell. 3 (6) (2021)
Ryan-Rhys Griffiths, Philippe Schwaller, Alpha A. Lee
Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design. CoRR (2021)
Philippe Schwaller, Daniel Probst, Alain C. Vaucher, Vishnu H. Nair, David Kreutter, Teodoro Laino, Jean-Louis Reymond
Mapping the space of chemical reactions using attention-based neural networks. Nat. Mach. Intell. 3 (2) (2021)
Alessandra Toniato, Philippe Schwaller, Antonio Cardinale, Joppe Geluykens, Teodoro Laino
Unassisted Noise Reduction of Chemical Reaction Data Sets. CoRR (2021)
Philippe Schwaller, Alain C. Vaucher, Teodoro Laino, Jean-Louis Reymond
Prediction of chemical reaction yields using deep learning. Mach. Learn. Sci. Technol. 2 (1) (2021)
Hakime Öztürk, Arzucan Özgür, Philippe Schwaller, Teodoro Laino, Elif Ozkirimli Olmez
Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery. CoRR (2020)
Philippe Schwaller, Daniel Probst, Alain C. Vaucher, Vishnu H. Nair, David Kreutter, Teodoro Laino, Jean-Louis Reymond
Mapping the Space of Chemical Reactions Using Attention-Based Neural Networks. CoRR (2020)
Philippe Schwaller, Riccardo Petraglia, Valerio Zullo, Vishnu H. Nair, Rico Andreas Haeuselmann, Riccardo Pisoni, Costas Bekas, Anna Iuliano, Teodoro Laino
Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy. CoRR (2019)
Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Costas Bekas, Alpha A. Lee
Molecular Transformer for Chemical Reaction Prediction and Uncertainty Estimation. CoRR (2018)
Philippe Schwaller, Théophile Gaudin, David Lanyi, Costas Bekas, Teodoro Laino
"Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models. CoRR (2017)