Development of polymer information systems to support accelerated materials development via AI-enabled automated experimentation
- 2022
- ACS Spring 2022
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
Active site sequence representation of human kinases outperforms full sequence for affinity prediction and inhibitor generation: 3D effects in a 1D model
- Jannis Born
- Tien Huynh
- et al.
- 2022
- ACS Spring 2022
Identification of enzymatic active sites with unsupervised language modelling
- Loic Kwate Dassi
- Matteo Manica
- et al.
- 2022
- ACS Spring 2022
Biocatalysed synthesis planning using data-driven learning
- Daniel Probst
- Matteo Manica
- et al.
- 2022
- Nature Communications
Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model
- Jannis Born
- Tien Huynh
- et al.
- 2021
- J. Chem. Inf. Model.
Identification of Enzymatic Active Sites with Unsupervised Language Modelling
- Loic Kwate Dassi
- Matteo Manica
- et al.
- 2021
- NeurIPS 2021
Active site sequence representation of human kinases outperforms full sequence for affinity prediction
- Jannis Born
- Tien Huynh
- et al.
- 2021
- NeurIPS 2021
On the automation of de-novo molecular design and chemical synthesis planning: A case study on SARS-CoV-2
- Jannis Born
- Matteo Manica
- et al.
- 2021
- ACS Fall 2021
On the Feasibility of Deep Learning Applications Using Raw Mass Spectrometry Data
- Maria Rodriguez Martinez
- Joris Cadow
- et al.
- 2021
- ISMB 2021
On the role of artificial intelligence in medical imaging of COVID-19
- Jannis Born
- David Beymer
- et al.
- 2021
- Patterns
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 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