Martino Bertoni

This paper designs cell-specific small molecule cytotoxics using AI, combining screening, machine learning, and reinforcement learning, resulting in validated compounds with improved hit discovery rates.

The paper identifies inhibitor-induced kinase destabilization as a common phenomenon and reveals that it occurs through endogenous proteolytic mechanisms rather than classical ubiquitination.

The paper introduces a protocol to integrate diverse bioactivity data into the Chemical Checker compound universe, enabling the generation of novel bioactivity signatures for small molecules.

BQsupports assesses the support and novelty of biomedical associations by analyzing descriptors across multiple contexts and providing scores for downstream applications.

The paper introduces Bioteque, a large-scale biomedical knowledge graph resource that provides pre-calculated descriptors for downstream machine learning tasks.

The paper introduces deep neural networks to infer bioactivity signatures for uncharacterized compounds, improving activity prediction across various biological endpoints.

The paper introduces Chemical Checker, a resource that integrates bioactivity data on 800,000 small molecules and demonstrates its use in drug discovery and disease modeling.