Research

A full list is available from the following Google Scholar profile.

Journal articles, reviews, workshop papers, and preprints

  1. J Chen, X Huang, C Hua, Y He, P Schwaller. AdsMT: A multi-modal transformer for predicting global minimum adsorption energy. ChemRxiv:10.26434/chemrxiv-2024-g4b60-v2, 2024.
  2. Y Du, C Duan, A Bran, A Sotnikova, Y Qu, H Kulik, A Bosselut, J Xu, P Schwaller. Large Language Models are Catalyzing Chemistry Education. ChemRxiv:10.26434/chemrxiv-2024-h722v, 2024.
  3. A Bran, Z Joncev, P Schwaller. Knowledge Graph Extraction from Total Synthesis Documents. Proceedings of the 1st Workshop on Language+ Molecules (L+ M 2024), 74-84, 2024.
  4. J Guo, P Schwaller. Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis Models. arXiv preprint arXiv:2407.12186, 2024.
  5. Y Du, AR Jamasb, J Guo, T Fu, C Harris, Y Wang, C Duan, P Liò, P Schwaller T Blundell. Machine learning-aided generative molecular design. Nature Machine Intelligence, 1-16, 2024.
  6. P Neves , JK Wegner, P Schwaller. Gradient Guided Hypotheses: A unified solution to enable machine learning models on scarce and noisy data regimes. arXiv preprint arXiv:2405.19210, 2024.
  7. J Guo, P Schwaller. Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation. arXiv preprint arXiv:2405.17066, 2024.
  8. A Bran, S Cox, O Schilter, C Baldassari, AD White, P Schwaller. ChemCrow: Augmenting large-language models with chemistry tools. arXiv:2304.05376, 2023. Featured in Chemistry World, MarkTechPost, New Scientist.
  9. J Chen, P Schwaller. Molecular hypergraph neural networks. The Journal of Chemical Physics 160 (14), JCP Emerging Investigators Special Collection, 2024.
  10. J Guo, P Schwaller. Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design. JACS Au 2024, 4, 6, 2160–2172, 2024.
  11. A Mirza, N Alampara, S Kunchapu, …, T Gupta, …, P Schwaller, …, KM Jablonka. Are large language models superhuman chemists?. arXiv preprint arXiv:2404.01475, 2024.
  12. Z Li, C Zhao, H Wang, Y Ding, Y Chen, P Schwaller, K Yang, C Hua, Y He . Interpreting chemisorption strength with AutoML-based feature deletion experiments. Proceedings of the National Academy of Sciences 121 (12), e2320232121, 2024.
  13. O Schilter, T Laino, P Schwaller. CMD+ V for chemistry: Image to chemical structure conversion directly done in the clipboard. Applied AI Letters 5 (1), e91, 2024.
  14. KM Jablonka, P Schwaller, A Ortega-Guerrero, B Smit. Leveraging Large Language Models for Predictive Chemistry. Nature Machine Intelligence 6 (2), 161-169, 2024.
  15. O Schilter, P Schwaller, T Laino. Balancing computational chemistry’s potential with its environmental impact. Green Chem., 2024, 26, 8669-8679, 2024.
  16. A Marchand, S Buckley, A Schneuing, M Pacesa, P Gainza, E Elizarova, R Neeser, …, P Schwaller, …, B Correia. Targeting protein-ligand neosurfaces using a generalizable deep learning approach. bioRxiv, 2024.03. 25.585721, 2024.
  17. S Back, A Aspuru-Guzik, M Ceriotti, G Gryn’ova, B Grzybowski, …, P Schwaller, …, A Walsh. Accelerated chemical science with AI. Digital Discovery 3 (1), 23-33, 2024.
  18. M McGibbon, S Shave, J Dong, Y Gao, DR Houston, J Xie, Y Yang, P Schwaller, V Blay. From intuition to AI: evolution of small molecule representations in drug discovery. Briefings in bioinformatics 25 (1), bbad422, 2024.
  19. B Ranković, RR Griffiths, HB Moss, P Schwaller*. Bayesian optimisation for additive screening and yield improvements in chemical reactions–beyond one-hot encodings. Digital Discovery, 2023, Advance Article, 3 (4), 654-666, 2024.
  20. VS Gil, AM Bran, M Franke, R Schlama, JS Luterbacher, P Schwaller. Holistic chemical evaluation reveals pitfalls in reaction prediction models. AI for Science: from Theory to Practice, NeurIPS, 2023
  21. O Schilter, M Alberts, F Zipoli, AC Vaucher, P Schwaller, T Laino. Unveiling the Secrets of H-NMR Spectroscopy: A Novel Approach Utilizing Attention Mechanisms. AI for Accelerated Materials Design, NeurIPS, 2023
  22. R Neeser, B Correia, P Schwaller. FSscore: A Machine Learning-based Synthetic Feasibility Score Leveraging Human Expertise. AI for Science: from Theory to Practice, NeurIPS, 2023
  23. GP Wellawatte, P Schwaller. Extracting human interpretable structure-property relationships in chemistry using XAI and large language models. XAI in Action: Past, Present, and Future Applications workshop, NeurIPS, 2023
  24. AM Bran, P Schwaller. Transformers and Large Language Models for Chemistry and Drug Discovery. arXiv preprint arXiv:2310.06083
  25. S d’Ascoli, S Becker, A Mathis, P Schwaller, N Kilbertus. ODEFormer: Symbolic Regression of Dynamical Systems with Transformers. ICLR Spotlight, 2024
  27. MW Mullowney, KR Duncan, …, P Schwaller, …, GJP Westen, AKH Hirsch, RG Linington, SL Robinson, MH Medema. Artificial intelligence for natural product drug discovery. Nature Rev. Drug Discovery, 2023.
  28. O Schilter, C Baldassari, T Laino, P Schwaller. Predicting solvents with the help of Artificial Intelligence. 10.26434/chemrxiv-2023-hmml5, 2023.
  29. O Schilter, A Vaucher, P Schwaller, T Laino. Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions. Digital Discovery, 2023.
  30. J Guo, B Ranković, P Schwaller*. Bayesian Optimization for Chemical Reactions. Chimia, 2023, 77, 31-31.
  31. RR Griffiths, L Klarner, HB Moss, A Ravuri, S Truong, B Rankovic, Y Du, A Jamasb, J Schwartz, A Tripp, G Kell, A Bourached, A Chan, J Moss, C Guo, AA Lee, P Schwaller, J Tang. GAUCHE: A Library for Gaussian Processes in Chemistry. NeurIPS, 2023.
  32. AM Bran, P Schwaller. Differential top-k learning for template-based single-step retrosynthesis. AI for Accelerated Materials Design NeurIPS 2022 workshop, 2022.
  33. S Barthel, M Krenn, Qi Ai, N Carson, A Frei, NC Frey, P Friederich, T Gaudin, AA Gayle, KM Jablonka, RF Lameiro, D Lemm, A Lo, SM Moosavi, JM Napoles-Duarte, AK Nigam, R Pollice, K Rajan, U Schatzschneider, P Schwaller, M Skreta, B Smit, F Strieth-Kalthoff, C Sun, G Tom, GF von Rudolf, A Wang, A White, A Young, R Yu, A Aspuru-Guzik. SELFIES and the future of molecular string representations. Patterns, 2022, 3, 10, 100588.

Prior to EPFL

  1. YGN Teukam, LK Dassi, M Manica, D Probst, P Schwaller, T Laino. Language models can identify enzymatic binding sites in protein sequences. Computational and Structural Biotechnology Journal 23, 1929-1937, 2024.
  2. F Zipoli, Z Ayadi, P Schwaller, T Laino, AC Vaucher. Completion of Partial Chemical Equations. Mach. Learn.: Sci. Technol. 5 025071, 2024.
  3. A Cardinale, A Castrogiovanni, T Gaudin, J Geluykens, T Laino, M Manica, D Probst, P Schwaller, A Sobczyk, A Toniato, AC Vaucher, H Wolf, F Zipoli , T Laino. Fuelling the Digital Chemistry Revolution with Language Models: Sandmeyer Award 2022. CHIMIA , 77 (7/8), 484-484, 2023.
  4. A Thakkar, AC Vaucher, A Byekwaso, P Schwaller, A Toniato, T Laino. Unbiasing Retrosynthesis Language Models with Disconnection Prompts. ACS Central Science, 2023.
  5. A Toniato, AC Vaucher, P Schwaller, T Laino. Enhancing diversity in language based models for single-step retrosynthesis. Digital Discovery, 2023, Accepted manuscript.
  6. D Probst, P Schwaller, JL Reymond. Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital Discovery, 2022, 1, 91-97.
  7. P Schwaller*, AC Vaucher, R Laplaza, C Bunne, A Krause, C Corminboeuf, and T Laino. Machine intelligence for chemical reaction space. WIREs Comput. Mol. Sci., 2022, e1604.
  8. A Thakkar, P Schwaller*. How AI for Synthesis Can Help Tackle Challenges in Molecular Discovery. CHIMIA, 2021, 75, 7-8, 677-678.
  9. D Kreutter, P Schwaller, JL Reymond. Predicting Enzymatic Reactions with a Molecular Transformer. Chem. Sci., 2021, 12, 8648-8659. Chemical Science cover. Most popular 2021 physical and theoretical chemistry articles.
  10. AC Vaucher, P Schwaller, J Geluykens, VH Nair, A Iuliano, T Laino. Inferring experimental procedures from text-based representations of chemical reactions. Nature Comm., 2021, 12 (1), 1-11. Editors’ highlight in Computation and Machine Learning for Chemistry. [Blog]. [Code].
  11. P Schwaller*, B Hoover, JL Reymond, H Strobelt, T Laino. Extraction of organic chemistry grammar from unsupervised learning of chemical reactions. Sci. Adv., 2021, 7, 15, abe416. Featured in TechTalks, C&EN. [Code].
  12. P Schwaller*, AC Vaucher, T Laino, JL Reymond. Prediction of Chemical Reaction Yields using Deep Learning. Mach. Learn.: Sci. Technol., 2021, 2, 015016. Invited article for “Machine Learning for Chemical Reactions” focus collection. [Code].
  13. A Toniato, P Schwaller, A Cardinale, J Geluykens, T Laino. Unassisted Noise-Reduction of Chemical Reactions Data Sets. Nat. Mach. Int., 2021, 3, 485–494. [Code].
  14. P Schwaller*, D Probst, AC Vaucher, VH Nair, D Kreutter, T Laino, JL Reymond. Mapping the Space of Chemical Reactions using Attention-Based Neural Networks. Nat. Mach. Int., 2021, 3, 144–152. [News & Views]. [Matters Arising]. Synthesis and enabling technologies collection. Featured in C&EN. [Code].
  15. G Pesciullesi•, P Schwaller•, T Laino, JL Reymond. Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates. Nature Comm., 2020, 11, 4874. Editors’ highlight in Organic Chemistry and Chemical Biology. [Blog]. [Code].
  16. AC Vaucher, F Zipoli, J Geluykens, VH Nair, P Schwaller, T Laino. Automated extraction of chemical synthesis actions from experimental procedures. Nature Comm., 2020, 11 (1), 2041-1723. Editors’ highlight in Computation and Machine Learning for Chemistry. Featured in C&EN. [Blog]. [Code].
  17. H Öztürk, A Özgür, P Schwaller, T Laino, E Ozkirimli. Exploring chemical space using natural language processing methodologies for drug discovery. Drug Discovery Today, 2020, 25 (4), 689-705.
  18. P Schwaller*, R Petraglia, V Zullo, V H Nair, R A Haeuselmann, R Pisoni, C Bekas, A Iuliano, T Laino. Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy. Chem. Sci., 2020,11, 3316-3325. [Blog].
  19. VH Nair, P Schwaller, T Laino. Data-driven Chemical Reaction Prediction and Retrosynthesis. Chimia, 2020, 73 (12), 997-1000. Invited article for special “Artificial Intelligence in Swiss Chemical Research” issue.
  20. P Schwaller*, T Laino, T Gaudin, P Bolgar, CA Hunter, C Bekas, AA Lee. Molecular Transformer – A Model for Uncertainty-Calibrated Chemical Reaction Prediction. ACS Cent. Sci. 2019, 5, 9, 1572–1583. ACS Central Science Top-10 Most Read Articles in 2020. Featured in C&EN, Chemistry World. [Blog]. [Code].
  21. P Schwaller•*, T Gaudin•, D Lanyi, C Bekas, T Laino. Found in Translation: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models. Chem. Sci., 2018, 9, 6091-609. Chemical Science: Most popular articles 2018-2019. Featured in Chemistry World, IEEE Spectrum, MIT Technology Review, phys.org. [Blog].
  22. M Makha, P Schwaller, K Strassel, SB Anantharaman, F Nüesch, R Hany, J Heier. Insights into photovoltaic properties of ternary organic solar cells from phase diagrams. Sci. Technol. Adv. Mater., 2018, 19, 1, 669–682.
  23. N Mounet, M Gibertini, P Schwaller, D Campi, A Merkys, A Marrazzo, T Sohier, IE Castelli, A Cepellotti, G Pizzi, N Marzari. Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds. Nature Nanotech., 2018, 13, 246–252. HPC Max Prize 2017. Nature Nanotechnology cover. [News & Views]. Featured in arstechnica, phys.org. [Blog]. [Code].