| Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds N Mounet, M Gibertini, P Schwaller, D Campi, A Merkys, A Marrazzo, ... Nature nanotechnology 13 (3), 246-252, 2018 | 1347 | 2018 |
| Molecular Transformer – A Model for Uncertainty-Calibrated Chemical Reaction Prediction P Schwaller, T Laino, T Gaudin, P Bolgar, C Bekas, AA Lee 2019 ACS central science / 2018 NeurIPS Workshop on Machine Learning for …, 2019 | 479 | 2019 |
| “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models P Schwaller*, T Gaudin*, D Lanyi, C Bekas, T Laino 2018 Chemical science / 2017 NeurIPS Workshop on Machine Learning for …, 2018 | 308 | 2018 |
| Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy P Schwaller, R Petraglia, V Zullo, VH Nair, RA Haeuselmann, R Pisoni, ... Chemical science 11 (12), 3316-3325, 2020 | 197 | 2020 |
| Prediction of chemical reaction yields using deep learning P Schwaller, AC Vaucher, T Laino, JL Reymond Machine Learning: Science and Technology 2 (1), 015016, 2021 | 125 | 2021 |
| Mapping the Space of Chemical Reactions using Attention-Based Neural Networks P Schwaller, D Probst, AC Vaucher, VH Nair, D Kreutter, T Laino, ... 2021 Nature Machine Intelligence / 2019 NeurIPS Workshop on Machine Learning …, 2021 | 113 | 2021 |
| Transfer learning enables the molecular transformer to predict regio-and stereoselective reactions on carbohydrates G Pesciullesi*, P Schwaller*, T Laino, JL Reymond Nature Communications 11 (1), 1-8, 2020 | 110 | 2020 |
| Extraction of organic chemistry grammar from unsupervised learning of chemical reactions P Schwaller, B Hoover, JL Reymond, H Strobelt, T Laino Science Advances 7 (15), eabe4166, 2021 | 99* | 2021 |
| Automated extraction of chemical synthesis actions from experimental procedures AC Vaucher, F Zipoli, J Geluykens, VH Nair, P Schwaller, T Laino Nature Communications 11 (1), 2041-1723, 2020 | 93 | 2020 |
| Exploring chemical space using natural language processing methodologies for drug discovery H Öztürk, A Özgür, P Schwaller, T Laino, E Ozkirimli Drug Discovery Today 25 (4), 689-705, 2020 | 67 | 2020 |
| Predicting enzymatic reactions with a molecular transformer D Kreutter, P Schwaller, JL Reymond Chemical science 12 (25), 8648-8659, 2021 | 35 | 2021 |
| Inferring experimental procedures from text-based representations of chemical reactions AC Vaucher, P Schwaller, J Geluykens, VH Nair, A Iuliano, T Laino Nature communications 12 (1), 2573, 2021 | 33 | 2021 |
| Reaction classification and yield prediction using the differential reaction fingerprint DRFP D Probst, P Schwaller, JL Reymond Digital discovery 1 (2), 91-97, 2022 | 31 | 2022 |
| Unassisted noise reduction of chemical reaction datasets T Alessandra, P Schwaller, A Cardinale, G Joppe, L Teodoro Nature Machine Intelligence 3 (6), 485-494, 2021 | 25* | 2021 |
| Machine intelligence for chemical reaction space P Schwaller, AC Vaucher, R Laplaza, C Bunne, A Krause, C Corminboeuf, ... Wiley Interdisciplinary Reviews: Computational Molecular Science 12 (5), e1604, 2022 | 22 | 2022 |
| Data-driven chemical reaction prediction and retrosynthesis VH Nair, P Schwaller, T Laino CHIMIA International Journal for Chemistry 73 (12), 997-1000, 2019 | 20 | 2019 |
| SELFIES and the future of molecular string representations S Barthel, M Krenn, Q Ai, N Carson, A Frei, NC Frey, P Friederich, ... Patterns, 2022 | 17* | 2022 |
| Insights into photovoltaic properties of ternary organic solar cells from phase diagrams M Makha, P Schwaller, K Strassel, SB Anantharaman, F Nüesch, R Hany, ... Science and technology of advanced materials 19 (1), 669-682, 2018 | 14 | 2018 |
| Data-driven learning systems for chemical reaction prediction: an analysis of recent approaches P Schwaller, T Laino Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and …, 2019 | 11 | 2019 |
| Data augmentation strategies to improve reaction yield predictions and estimate uncertainty P Schwaller, AC Vaucher, T Laino, JL Reymond 2020 NeurIPS Workshop on Machine Learning for Molecules and Materials | ChemRxiv, 2020 | 10 | 2020 |
Teodoro LainoIBM Research ZurichVerified email at zurich.ibm.com
