The Computational Drug Discovery Hub

Based at Politecnico di Torino, our Computational Drug Discovery Hub leverages cutting-edge artificial intelligence, machine learning, and molecular modeling to accelerate the development of next-generation antivirals and therapeutics for post-viral syndromes.

The computational approaches developed in Turin work in close partnership with experimental research in Edmonton, creating a seamless pipeline from in silico prediction to experimental validation and clinical translation.

Our Computational Advantage

By combining deep learning, molecular dynamics, and systems biology approaches, we can screen millions of compounds virtually, predict drug-target interactions, and optimize lead compounds—dramatically accelerating the drug discovery process while reducing costs.

Core Capabilities

AI-Driven Drug Design

We use deep learning and generative AI models to design novel therapeutic molecules:

  • Generative models for de novo molecular design
  • Deep learning-based binding affinity prediction
  • Multi-objective optimization for drug-like properties
  • Transfer learning from large chemical databases

Virtual Screening

High-throughput computational screening of compound libraries:

  • Structure-based virtual screening
  • Ligand-based similarity searching
  • Pharmacophore modeling
  • Integration with experimental HTS data

Molecular Dynamics Simulations

Understanding protein-ligand interactions at atomic resolution:

  • Free energy calculations for binding affinity
  • Protein conformational dynamics
  • Drug resistance mutation analysis
  • Membrane protein simulations

Systems Modeling

Multi-scale modeling of viral-host metabolism:

  • Metabolic network modeling
  • Integration of multi-omics data
  • Pathway analysis and target identification
  • Predictive models of drug response

Infrastructure

The Turin hub maintains state-of-the-art computational infrastructure:

  • High-performance computing cluster with GPU acceleration
  • Cloud computing resources for scalable workflows
  • Proprietary and open-source software pipelines
  • Secure data infrastructure for collaborative research

Current Projects

Active

AI Antiviral Discovery Platform

Development of an integrated AI platform for discovery of broad-spectrum antivirals targeting viral metabolic enzymes.

Deep Learning
Active

Host-Directed Therapy Screening

Virtual screening campaign to identify compounds that modulate host metabolic pathways exploited by viruses.

Drug Repurposing
Active

Metabolic Network Modeling

Systems-level modeling of viral-induced metabolic reprogramming for therapeutic target identification.

Systems Biology

Computational Collaboration

We offer computational services and collaboration opportunities for academic and industry partners.