Abstract 977: VINI: A multimodal in silico platform for discovering rational drug combinations in KRAS-mutant pancreatic cancer
Abstract
Rational combination therapies are urgently needed for aggressive KRAS-mutant cancers such as pancreatic ductal adenocarcinoma (PDAC), which has a 5-year survival rate below 10%. VINI is a multimodal in silico platform that integrates AI, quantum-informed algorithms, pathway modeling, and structural data to accelerate the discovery of effective drug combinations. Its foundation is based on KEGG cancer pathways, with gene expression and mutation data from the Cancer Cell Line Encyclopedia (CCLE), molecular structures from PubChem and DrugBank, three-dimensional protein structures from RCSB PDB or AlphaFold predictions, and protein sequences from UniProt and DrugBank (for monoclonal antibodies), capturing disease-specific biology. VINI uses AI and semi-empirical quantum-informed virtual screening (planned for implementation in the upcoming Horizon project) to predict intracellular drug efficacy and multi-drug synergy. Classical computational chemistry tools, including Rosetta, AutoDock Vina, UCSF Chimera, and Scripps MGLTools, are used by VINI to generate high-quality predictions, with high-performance computing enabling rapid evaluation. Proof-of-concept studies demonstrated VINI’s ability to predict novel triple-drug combinations targeting ALK, BCL-2, mTOR, DNA repair, and androgen pathways in hormone-sensitive prostate cancer, achieving 79.3% agreement with clinical outcomes across 16 cancer types and 100% for DU-145 and PC3 lines. VINI has also been applied to SARS-CoV-2…
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