Bridging the gap between rule-based expert systems and machine learning in computer-aided retrosynthetic design
- Daniel Probst
- Anastasia Sveshnikova
- et al.
- 2022
- ACS Fall 2022
Teodoro Laino
Pronouns
He/Him/His
Title
Distinguished RSM, Manager
Bio
Teo received the Master degree in theoretical chemistry in 2001 (University of Pisa and Scuola Normale Superiore di Pisa, Italy) and the doctorate in computational chemistry in 2006 (Scuola Normale Superiore di Pisa, Italy) defending a thesis on 'Multi-Grid QM/ MM Approaches in ab initio Molecular Dynamics' supervised by Prof. Dr. Michele Parrinello. From 2006 to 2008, Teo worked as a post-doctoral researcher in the research group of Prof. Dr. Jürg Hutter at the University of Zurich, contributing to the development of the CP2K simulation package. In 2008, Teo joined the IBM Research - Zurich Laboratory (ZRL) as Research Scientist. He is currently Distinguished Research Scientist and manager.
His research interests focus on developing machine learning/artificial intelligence technologies to digitalize chemistry and materials science, with IBM RXN for chemistry being an example of a recent community success. In 2022, the team received the Sandmeyer Award of the Swiss Chemical Society for the important contributions to the field of digital chemistry.
Publications
Combining high-quality, humanly curated data with language models: the dawn of on-demand machine learning models for digital chemistry
- Alain Vaucher
- Fiona Shortt de Hernandez
- et al.
- 2022
- ACS Fall 2022
Guiding multistep retrosynthesis planning with continuous pathway representations
- Jannis Born
- Matteo Manica
- et al.
- 2022
- ACS Fall 2022
Deep learning assisted Suzuki cross coupling catalyst design
- Oliver Schilter
- Federico Zipoli
- et al.
- 2022
- ACS Fall 2022
Augmented disconnection aware retrosynthesis to facilitate user interaction
- Amol Thakkar
- Andrea Antonia Byekwaso
- et al.
- 2022
- ACS Fall 2022
Identification of enzymatic active sites with unsupervised language modelling
- Loic Kwate Dassi
- Matteo Manica
- et al.
- 2022
- ACS Fall 2022
Prediction of Phase Diagrams and Associated Phase Structural Properties
- Federico Zipoli
- Victor Viterbo
- et al.
- 2022
- Ind. Eng. Chem. Res.
Identification of Enzymatic Active Sites with Unsupervised Language Modeling
- Matteo Manica
- Loic Kwate Dassi
- et al.
- 2022
- ISGC 2022
Identification of enzymatic active sites with unsupervised language modelling
- Loic Kwate Dassi
- Matteo Manica
- et al.
- 2022
- MRS Spring Meeting 2022
Accelerating materials discovery using artificial intelligence, high performance computing and robotics
- Edward O. Pyzer-Knapp
- Jed W. Pitera
- et al.
- 2022
- npj Computational Materials
Patents
- US
- 11854670
- US
- 11133528
- US
- 11121409
- US
- 11016965
- US
- 10559847
- US
- 10170790
- US
- 9971736
Projects
Lab that Learns
Leveraging AI foundation models and multi-cloud computing to usher in a new era of reproducible and collaborative experimentation for scientific discovery.
AI for Scientific Discovery
Creating the AI-enabled lab for a new era of reproducible and collaborative experimentation
Blog posts
Thieme trains IBM RXN for Chemistry with high-quality data
NewsAlain Vaucher, Fiona Shortt de Hernandez, Sascha Hausberg, and Teodoro Laino6 minute readIBM RXN for Chemistry: Unveiling the grammar of the organic chemistry language
ResearchPhilippe Schwaller, Hendrik Strobelt, Teodoro Laino, and Benjamin Hoover7 minute readIBM RXN for Chemistry: Automatically cleaning chemical reaction datasets
ResearchAlessandra Toniato and Teodoro Laino7 minute readIBM RXN: New AI model boosts mapping of chemical reactions
ResearchPhilippe Schwaller and Teodoro Laino5 minute read- See more blog posts
Top collaborators
MM
Matteo Manica
Senior Research Scientist
FZ
Federico Zipoli
Research Scientist
MA
MG
Mara Graziani
Research Scientist