Philippe Schwaller
Philippe Schwaller
Assistant Professor, Laboratory of Artificial Chemical Intelligence - EPFL
Verified email at - Homepage
Cited by
Cited by
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
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
“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
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
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
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
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
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
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
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
Predicting enzymatic reactions with a molecular transformer
D Kreutter, P Schwaller, JL Reymond
Chemical science 12 (25), 8648-8659, 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), 1-11, 2021
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
Data-driven chemical reaction prediction and retrosynthesis
VH Nair, P Schwaller, T Laino
CHIMIA International Journal for Chemistry 73 (12), 997-1000, 2019
Reaction classification and yield prediction using the differential reaction fingerprint DRFP
D Probst, P Schwaller, JL Reymond
Digital discovery 1 (2), 91-97, 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
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
Machine intelligence for chemical reaction space
P Schwaller, AC Vaucher, R Laplaza, C Bunne, A Krause, C Corminboeuf, ...
Wiley Interdisciplinary Reviews: Computational Molecular Science, e1604, 2022
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
Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design
RR Griffiths, P Schwaller, AA Lee
2018 NeurIPS Workshop on Machine Learning for Molecules and Materials, 2018
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