Andrea Anelli · Basel, Switzerland
Machine learning for molecular structure.
I develop machine-learning methods for atomistic simulation and pharmaceutical development, with a focus on interatomic potentials, crystal-structure prediction, and scientific workflow automation.
ML Research Scientist at Roche · PhD, EPFL
12 publications · 1,123+ citations · verified 2026-07-26Google Scholar ↗
§ 01
All research →Selected research
010203
APPA · Agentic Preformulation Pathway Assistant
An LLM-based system that combines scientific reasoning with experimental databases and ML predictions to support preformulation evidence gathering.
Divide-and-Conquer Potentials for Scalable Atomistic Simulation
A committee-of-experts architecture that weights specialised ML potentials by their domain of expertise across heterogeneous chemical space.
The Seventh Blind Test of Crystal Structure Prediction
A community benchmark of 21 groups generating crystal structures for pharmaceutical targets, including an assessment of ML-guided generation for rigid molecules.
82citations
§ 02
Research areas
The work connects model architecture, molecular properties, solid-form prediction, and tools for pharmaceutical development.
01
ML Potential Architectures
Building the models that simulate atoms.
02
Structure–Property Learning
From atomic coordinates to macroscopic behaviour.
03
Crystal Structure Prediction
Which polymorph will a drug molecule form?
04
Agentic AI for Drug Discovery
LLM-based systems for evidence retrieval and scientific workflows.