Automated patent classification for crop protection via domain adaptation
- Dimitrios Christofidellis
- Marzena Maria Lehmann
- et al.
- 2023
- Appl. AI Lett.
Matteo Manica
Pronouns
He/Him/His
Title
Senior Research Scientist
Bio
Matteo is a Senior Research Scientist at IBM Research Europe - Zürich. His research focuses on AI applications to science and engineering: from AI-driven autonomous labs and multimodal generative models for material science to modelling energy storage devices. 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
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.
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
Blog posts
How generative AI models can fuel scientific discovery
ExplainerJohn R Smith and Matteo Manica6 minute readNeuNetS: Automating Neural Network Model Synthesis for Broader Adoption of AI
ResearchCristiano Malossi
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