Automated patent classification for crop protection via domain adaptation
- Dimitrios Christofidellis
- Marzena Maria Lehmann
- et al.
- 2023
- Appl. AI Lett.
Bio
Matteo is a Senior Research Scientist in Accelerated Discovery at IBM Research Europe - Zürich. His research interests are AI-driven autonomous labs and multimodal generative models for material science. He received his degree in Mathematical Engineering from Politecnico di Milano in 2013. After getting his MSc, he worked in a startup, Moxoff spa, as a software engineer and analyst for scientific computing. In 2019 he obtained his doctoral degree at the end of a joint Ph.D. program between IBM Research and the Institute of Molecular Systems Biology, ETH Zürich, with a thesis on multimodal learning approaches for precision medicine.
Matteo's full publication record is available on Google Scholar.
Publications
Accelerating Material Design with the Generative Toolkit for Scientific Discovery (GT4SD)
- Matteo Manica
- Joris Cadow
- et al.
- 2022
- MRS Fall Meeting 2022
A Data-driven Discovery Pipeline for Flue Gas Separation Homopolymer Membranes
- Hsianghan Hsu
- Matteo Manica
- et al.
- 2022
- MRS Fall Meeting 2022
Enzeptional—Enzyme Optimization via a Generative Language Modeling-Based Evolutionary Algorithm
- 2022
- MRS Fall Meeting 2022
On the Choice of Active Site Sequences for Kinase-Ligand Affinity Prediction
- Jannis Born
- Yoel Shoshan
- et al.
- 2022
- J. Chem. Inf. Model.
Accelerating Material Design with the Generative Toolkit for Scientific Discovery (GT4SD)
- Matteo Manica
- Joris Cadow
- et al.
- 2022
- ACS Fall 2022
Comparing active site sequence representations for kinase-ligand affinity prediction
- Jannis Born
- Tien Huynh
- et al.
- 2022
- ACS Fall 2022
Guiding multistep retrosynthesis planning with continuous pathway representations
- Jannis Born
- Matteo Manica
- et al.
- 2022
- ACS Fall 2022
Regression transformer: Concurrent property prediction and conditional molecular generation
- 2022
- ACS Fall 2022
Identification of enzymatic active sites with unsupervised language modelling
- Loic Kwate Dassi
- Matteo Manica
- et al.
- 2022
- ACS Fall 2022
Patents
- US
- 12124958
- US
- 11942189
- JP
- 7358005
- US
- 11651860
- US
- 11651841
- US
- 11515005
- GB
- 2595994
- US
- 11150926
- US
- 10593422
Projects
Accelerator Technologies
We're developing technological solutions to assist subject matter experts with their scientific workflows by enabling the Human-AI co-creation process.
AI for Scientific Discovery
Creating the AI-enabled lab for a new era of reproducible and collaborative experimentation
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.
Blog posts
How generative AI models can fuel scientific discovery
ExplainerJohn R Smith and Matteo Manica6 minute read
Top collaborators
TL
Teodoro Laino
Distinguished RSM, Manager
GG
Gianmarco Gabrieli
Pre-Doctoral Researcher
JB
Jannis Born
Research Scientist in AI for Scientific Discovery
FZ
Federico Zipoli
Research Scientist