PaccMannRL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning
- Jannis Born
- Matteo Manica
- et al.
- 2021
- iScience
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
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
Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks
- Modestas Filipavicius
- Matteo Manica
- et al.
- 2020
- NeurIPS 2020
COSIFER: A Python package for the consensus inference of molecular interaction networks
- Matteo Manica
- Charlotte Bunne
- et al.
- 2020
- Bioinformatics
Inferring clonal composition from multiple tumor biopsies
- Matteo Manica
- Hyunjae Ryan Kim
- et al.
- 2020
- npj Systems Biology and Applications
PaccMannRL on SARS-CoV-2: Designing antiviral candidates with conditional generative models
- Jannis Born
- Matteo Manica
- et al.
- 2020
- ICML 2020
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