Philippe Schwaller
Researcher / author (OpenAlex)
Facts
2 properties- Affiliation
- Institute of Catalysis and Petrochemistry, Beijing Academy of Artificial Intelligence, Formation Continue UNIL-EPFL, IBM Research – Zurich, NCCR Chemical Biology - Visualisation and Control of Biological Processes Using Chemistry, Artificial Intelligence Research Institute, Centre d'Imagerie BioMedicale, Artificial Intelligence in Medicine (Canada), Swiss Federal Institute of Technology in Lausanneas of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
🔖 Identifiers · 2
- ORCID iD
- 0000-0003-3046-6576
- OpenAlex ID
- A5028051805, A5125552670, A5126447174, A5127920869, A5134998742as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 29
Alchemical analysis of FDA approved drugs,
Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning,
Automated extraction of chemical synthesis actions from experimental procedures,
Bayesian optimisation for additive screening and yield improvements – beyond one-hot encoding,
Best practices for multi-fidelity Bayesian optimization in materials and molecular research,
Data-driven Chemical Reaction Prediction and Retrosynthesis,
Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions,
Directly optimizing for synthesizability in generative molecular design using retrosynthesis models
+21 more
← Authored by 29
Alchemical analysis of FDA approved drugs,
Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning,
Automated extraction of chemical synthesis actions from experimental procedures,
Bayesian optimisation for additive screening and yield improvements – beyond one-hot encoding,
Best practices for multi-fidelity Bayesian optimization in materials and molecular research,
Data-driven Chemical Reaction Prediction and Retrosynthesis,
Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions,
Directly optimizing for synthesizability in generative molecular design using retrosynthesis models
+21 more
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Summary
Philippe Schwaller is a human[1].