Accelerating Material Design with the Generative Toolkit for Scientific Discovery (GT4SD)
- Matteo Manica
- Joris Cadow
- et al.
- 2022
- MRS Fall Meeting 2022
Jannis Born
Pronouns
He/Him/His
Title
Research Scientist in AI for Scientific Discovery
Bio
Jannis is a research scientist at IBM Research in Zurich, Switzerland. His current research interests are on AI4Science, Language Models and Quantum Machine Learning. Jannis obtained his PhD from ETH Zurich (D-BSSE) in 2022, for his work on language models for molecular design performed at IBM Research. His PhD was advised by Matteo Manica (IBM), Prof. Karsten Borgwardt (ETH) and Prof. Alan Aspuru Guzik. Before that he completed a M.Sc. at ETH/UZH Zurich (with distinction) in Neural Systems & Computation in 2019 and a B.Sc. in Cognitive Science. He has received the FXH Scientific Excellence Award in 2019 and studied and researched in Germany, England, Singapore and Switzerland.
Publications
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
MPEGO: A toolkit for multi-level performance evaluation of generative models for material discovery domains
- Girmaw Abebe Tadesse
- Jannis Born
- et al.
- 2022
- KDD 2022
Regression Transformer - Blending Numerical and Textual Tokens for Concurrent Property Predicition and Conditional Generation
- 2022
- MRS Spring Meeting 2022
Regression Transformer: Concurrent Conditional Generation and Regression by Blending Numerical and Textual Tokens
- 2022
- ICLR 2022
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
Active site sequence representation of human kinases outperforms full sequence for affinity prediction
- Jannis Born
- Tien Huynh
- et al.
- 2021
- NeurIPS 2021
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.
AI for Scientific Discovery
Creating the AI-enabled lab for a new era of reproducible and collaborative experimentation
Blog posts
Neural networks take TITAN-sized step toward T cell specificity prediction
ResearchAnna Weber, Jannis Born, and María Rodríguez Martínez5 minute read
Top collaborators
MM
Matteo Manica
Senior Research Scientist
TL
Teodoro Laino
Distinguished RSM, Manager
FT
Francesco Tacchino
Research Staff Member
CB
Carlo Baldassari
RoboRXN Lab Chemist