PaccMannRL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning
- Jannis Born
- Matteo Manica
- et al.
- 2021
- iScience
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
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
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