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

Journal articles, reviews, workshop papers, and preprints

  1. 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
  2. AM Bran, P Schwaller. Transformers and Large Language Models for Chemistry and Drug Discovery. arXiv preprint arXiv:2310.06083
  3. S d’Ascoli, S Becker, A Mathis, P Schwaller, N Kilbertus. ODEFormer: Symbolic Regression of Dynamical Systems with Transformers. arXiv preprint arXiv:2310.05573
  4. 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.
  5. O Schilter, C Baldassari, T Laino, P Schwaller. Predicting solvents with the help of Artificial Intelligence. 10.26434/chemrxiv-2023-hmml5, 2023.
  6. J Guo, P Schwaller. Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design. 10.26434/chemrxiv-2023-qmqmq-v3, 2023.
  7. AM 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.
  8. 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.
  9. KM Jablonka, P Schwaller, A Ortega-Guerrero, B Smit. Leveraging Large Language Models for Predictive Chemistry. 10.26434/chemrxiv-2023-fw8n4, 2023.
  10. J Guo, B Ranković, P Schwaller*. Bayesian Optimization for Chemical Reactions. Chimia, 2023, 77, 31-31.
  11. 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, 2023.
  12. 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.
  13. AM Bran, P Schwaller. Differential top-k learning for template-based single-step retrosynthesis. AI for Accelerated Materials Design NeurIPS 2022 workshop, 2022.
  14. 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. 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.
  2. A Thakkar, AC Vaucher, A Byekwaso, P Schwaller, A Toniato, T Laino. Unbiasing Retrosynthesis Language Models with Disconnection Prompts. ACS Central Science, 2023.
  3. A Toniato, AC Vaucher, P Schwaller, T Laino. Enhancing diversity in language based models for single-step retrosynthesis. Digital Discovery, 2023, Accepted manuscript.
  4. D Probst, P Schwaller, JL Reymond. Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital Discovery, 2022, 1, 91-97.
  5. 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.
  6. A Thakkar, P Schwaller*. How AI for Synthesis Can Help Tackle Challenges in Molecular Discovery. CHIMIA, 2021, 75, 7-8, 677-678.
  7. 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.
  8. 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].
  9. 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].
  10. 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].
  11. 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].
  12. 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].
  13. 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].
  14. 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].
  15. 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.
  16. 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].
  17. 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.
  18. 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].
  19. 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, [Blog].
  20. 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.
  21. 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, [Blog]. [Code].