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2020 – today
- 2024
[j8]Kevin Maik Jablonka, Philippe Schwaller
, Andres Ortega-Guerrero, Berend Smit:
Leveraging large language models for predictive chemistry. Nat. Mac. Intell. 6(2): 161-169 (2024)- [j7]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): 525-535 (2024) - [j6]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): 589-604 (2024) - [c3]Jeff Guo, Philippe Schwaller:
Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design. ICLR 2024 - [c2]Stéphane d'Ascoli, Sören Becker, Philippe Schwaller, Alexander Mathis, Niki Kilbertus:
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers. ICLR 2024 - [i25]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 abs/2404.01475 (2024) - [i24]Jeff Guo, Philippe Schwaller:
Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation. CoRR abs/2405.17066 (2024) - [i23]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 abs/2405.19210 (2024) - [i22]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:
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants. CoRR abs/2408.11841 (2024) - [i21]Victor Sabanza Gil, Riccardo Barbano, Daniel Pacheco Gutiérrez, Jeremy S. Luterbacher, José Miguel Hernández-Lobato, Philippe Schwaller, Loïc Roch:
Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research. CoRR abs/2410.00544 (2024) - [i20]Jeff Guo, Philippe Schwaller:
It Takes Two to Tango: Directly Optimizing for Constrained Synthesizability in Generative Molecular Design. CoRR abs/2410.11527 (2024) - [i19]Daniel Armstrong, Zlatko Joncev, Jeff Guo, Philippe Schwaller:
Tango*: Constrained synthesis planning using chemically informed value functions. CoRR abs/2412.03424 (2024) - 2023
- [j5]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) - [c1]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 - [i18]Jeff Guo, Philippe Schwaller:
Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design. CoRR abs/2305.16160 (2023) - [i17]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 abs/2306.06283 (2023) - [i16]Jeff Guo, Philippe Schwaller:
Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design. CoRR abs/2309.13957 (2023) - [i15]Stéphane d'Ascoli, Sören Becker, Alexander Mathis, Philippe Schwaller, Niki Kilbertus:
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers. CoRR abs/2310.05573 (2023) - [i14]Andres M. Bran, Philippe Schwaller:
Transformers and Large Language Models for Chemistry and Drug Discovery. CoRR abs/2310.06083 (2023) - [i13]Geemi P. Wellawatte, Philippe Schwaller:
Extracting human interpretable structure-property relationships in chemistry using XAI and large language models. CoRR abs/2311.04047 (2023) - [i12]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 abs/2312.09004 (2023) - [i11]Rebecca M. Neeser, Bruno Correia, Philippe Schwaller:
FSscore: A Machine Learning-based Synthetic Feasibility Score Leveraging Human Expertise. CoRR abs/2312.12737 (2023) - [i10]Junwu Chen, Philippe Schwaller:
Molecular Hypergraph Neural Networks. CoRR abs/2312.13136 (2023) - 2022
- [j4]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): 100588 (2022) - [i9]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 abs/2204.00056 (2022) - [i8]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:
GAUCHE: A Library for Gaussian Processes in Chemistry. CoRR abs/2212.04450 (2022) - 2021
- [j3]Philippe Schwaller, Alain C. Vaucher, Teodoro Laino, Jean-Louis Reymond:
Prediction of chemical reaction yields using deep learning. Mach. Learn. Sci. Technol. 2(1): 15016 (2021) - [j2]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): 144-152 (2021) - [j1]Alessandra Toniato, Philippe Schwaller, Antonio Cardinale, Joppe Geluykens, Teodoro Laino:
Unassisted noise reduction of chemical reaction datasets. Nat. Mach. Intell. 3(6): 485-494 (2021) - [i7]Alessandra Toniato, Philippe Schwaller, Antonio Cardinale, Joppe Geluykens, Teodoro Laino:
Unassisted Noise Reduction of Chemical Reaction Data Sets. CoRR abs/2102.01399 (2021) - [i6]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 abs/2105.02637 (2021) - 2020
- [i5]Hakime Öztürk, Arzucan Özgür, Philippe Schwaller, Teodoro Laino, Elif Özkirimli Ölmez:
Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery. CoRR abs/2002.06053 (2020) - [i4]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 abs/2012.06051 (2020)
2010 – 2019
- 2019
- [i3]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 abs/1910.08036 (2019) - 2018
- [i2]Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Costas Bekas, Alpha A. Lee:
Molecular Transformer for Chemical Reaction Prediction and Uncertainty Estimation. CoRR abs/1811.02633 (2018) - 2017
- [i1]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 abs/1711.04810 (2017)
Coauthor Index
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