Graph ML for Science

Sun, 25 Jan 2026 00:00:00 +0000

Overview

Biology and Chemistry are fundamentally relational—molecules are graphs of atoms, and cellular functions rely on complex interaction networks. We develop geometric deep learning methods to model, generate, and understand these structures.

Active Projects

1. Generative Biology & Drug Discovery

Goal: Move from “In Silico” generation to “In Vitro” validation. Details: We are building generative models (like MiDi and LGDC) that can design novel molecules with specific 3D geometries and chemical properties. A key focus is bridging the gap between computational metrics and actual wet-lab success rates.

2. Transcriptomics & Interaction Networks

Goal: Decode the language of the cell. Details: Using Graph Neural Networks (GNNs) and Graph Signal Processing, we model gene regulatory networks and protein-protein interactions. Our aim is to infer causal relationships in transcriptomic data to identify potential therapeutic targets.

From In Silico to In Vitro: Evaluating Molecule Generative Models
N Osman, V Lembo, G Bottegoni, L Toni. NeurIPS 2025 AI4Science Workshop.

LGDC: Latent Graph Diffusion via Spectrum-Preserving Coarsening
N Osman, K Jiang, D Buffelli, X Dong, L Toni. NeurIPS 2025 Workshop.

Heterogeneous Graph Structure Learning through the Lens of Data-generating Processes
K Jiang, B Tang, X Dong, L Toni. AISTATS 2025.

AI4Science | Learning And Signal Processing

Graph ML for Science

Overview

Active Projects

1. Generative Biology & Drug Discovery

2. Transcriptomics & Interaction Networks

Related Publications