Data Decontamination Breakthrough Transforms Drug Discovery Predictions
The Hidden Flaw in Drug Discovery AI For years, the pharmaceutical industry has relied on binding affinity prediction models to…
Tag: Ligand (biochemistry)
Computational Breakthroughs Reshape Cancer Neoantigen Discovery Pipeline
New computational pipelines combining genomic, transcriptomic and proteomic data are transforming neoantigen discovery for cancer immunotherapy. Researchers report significant advances in predicting which tumor-specific peptides can trigger effective immune responses. The integration of artificial intelligence and deep learning models is addressing long-standing challenges in immunogenicity prediction.
Revolutionizing Cancer Immunotherapy Through Computational Prediction
Computational approaches to neoantigen discovery are rapidly advancing cancer immunotherapy, with new tools and methodologies improving the identification of tumor-specific targets, according to recent analysis in Genes & Immunity. The process begins with detecting tumor-specific genetic alterations through next-generation sequencing technologies including RNA-Seq and whole exome or genome sequencing. Sources indicate that sequencing DNA from peripheral blood mononuclear cells provides a crucial normal reference for comparison and enables haplotype determination.