Andrea Zaliani

The paper identifies host-targeting antivirals against SARS-CoV-2 using morphological profiling, finding compounds that reverse infected cell phenotypes and validate host processes hijacked by viruses.

The paper identifies compounds that restore morphology in Parkinson's disease neurons by targeting mitochondrial uncoupling.

The paper optimizes and evaluates two cell-based assays for degrader discovery, finding that the signal rescue assay reliably identifies active degraders and filters out non-specific compounds.

The paper develops a machine learning model to predict drug-induced phospholipidosis using a large dataset of repurposed drugs tested across multiple cell lines.

The paper develops ML models to predict antimicrobial activity of small molecules using a knowledge graph, achieving 75.9% accuracy in identifying active compounds.

The paper develops two annotation pipelines and dashboards to integrate chemical and bioactivity data for drug repurposing, validated on a compound library with over 5000 compounds.

The paper identifies repurposable drugs for mitochondrial disease by using deep learning to screen for compounds that improve neuronal commitment and neurogenesis in Leigh syndrome organoids.

The paper analyzes patent data from SureChEMBL to assess the drug-likeness and clinical development stage of compounds, providing insights into their relevance for pharmaceutical discovery.

The paper identifies CPI-169 as a competitive inhibitor of SARS-CoV-2 PLpro that targets the same binding site as GRL-0617, through a drug repurposing screening approach that accounts for redox-sensitivity.

This paper introduces MultiGML, a multimodal graph machine learning approach that predicts adverse drug events by integrating diverse data sources and provides explainable biological insights.

This study identifies ADAMTS-7 as a risk factor for atherosclerosis by degrading TIMP-1, reducing its inhibition of MMP-9 and promoting collagen degradation.

PEMT extracts patent information linked to chemical structures and gene names from public databases to support drug discovery and research.

This paper constructs a Monkeypox knowledge graph to represent chemical entities and biological aspects of MPXV, integrating data for comprehensive analysis.

MICHA is a pipeline that FAIRifies chemosensitivity assays by automatically annotating compounds, samples, reagents, and data processing methods, enabling interoperable and reusable drug screening data.

The paper identifies 62 new inhibitors of SARS-CoV-2 3CL-Pro through a compound repurposing screen and validates their activity using X-ray crystallography.

The paper presents a drug-target-mechanism graph to identify synergistic drug combinations for SARS-CoV-2 treatment, validated through experimental testing.