Active site sequence representation of human kinases outperforms full sequence for affinity prediction
- Jannis Born
- Tien Huynh
- et al.
- 2021
- NeurIPS 2021
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
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
PaccMannRL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning
- Jannis Born
- Matteo Manica
- et al.
- 2021
- iScience
Data-driven molecular design for discovery and synthesis of novel ligands: A case study on SARS-CoV-2
- Jannis Born
- Matteo Manica
- et al.
- 2021
- Machine Learning: Science and Tech.
Understood in translation: transformers for domain understanding
- 2021
- AAAI 2021
Trends in deep learning for property-driven drug design
- Jannis Born
- Matteo Manica
- 2021
- Current Medicinal Chemistry
Convergent network effects along the axis of gene expression during prostate cancer progression
- Konstantina Charmpi
- Tiannan Guo
- et al.
- 2020
- Genome Biology
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
- Vijil Chenthamarakshan
- Payel Das
- et al.
- 2020
- NeurIPS 2020
Patents
- 25 Mar 2024
- US
- 11942189
- 28 Sep 2023
- JP
- 7358005
- 15 May 2023
- US
- 11651860
- 15 May 2023
- US
- 11651841
- 28 Nov 2022
- US
- 11515005
- 27 Sep 2022
- GB
- 2595994
- 18 Oct 2021
- US
- 11150926
- 16 Mar 2020
- 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- Accelerated Discovery
- AI
- Generative AI
- Materials Discovery
Top collaborators
PR
Patrick Ruch
Senior Research Scientist, AI for Scientific Discovery
TL
Teodoro Laino
Distinguished RSM, Manager
GG
Gianmarco Gabrieli
Pre-Doctoral Researcher
JB
Jannis Born
Research Scientist in AI for Scientific Discovery