Merzliakov, S. | bioRxiv Science

bioRxiv2026

A Unified Agent-Enabled Platform for Drug Repurposing across Molecular, Phenotypic, and Clinical Scales

Drug repurposing offers an effective path to new therapies, yet existing computational approaches rely on a single line of evidence and are rarely validated across biological scales. We present LinkD, an integrated framework that unifies diffusion-based affinity prediction, proteome-wide selectivity scoring, phenotypic validation, and population-scale clinical evidence. LinkD-Bind predicts binding across 14,981 drugs and 20,385 human targets, ranking first in 8 of 9 BindingDB, Davis, and KIBA evaluations, with the largest gains under cold-start conditions. LinkD-Select recovers 95.3% of known drug-target pairs by combining selectivity scoring and molecular docking. LinkD-Pheno integrates drug-sensitivity and CRISPR dependency data across 960 cancer cell lines, identifying 34 novel drug-gene pairs and recovering [~]85% of known targets among the top 50 candidates. Across 11.5 million individuals from Mount Sinai and UK Biobank, LinkD-prioritized {beta}-blockers propranolol (HR 0.82) and carvedilol (HR 0.92) reduced 5-year prostate cancer incidence relative to metoprolol, corroborated by ADRB2 docking and LNCaP growth inhibition. LinkD-Agent, which can effectively orchestrate all evidence layers, is served on a publicly available web platform (https://linkd-agent.onrender.com/), enabling a wide range of users to derive new drug repurposing opportunities through natural language queries.

bioinformatics↗

bioRxiv2025

Widespread Epistasis between Cancer Driver Mutations and Allele-Specific Copy Number Variations

Cancer driver mutations alone are often insufficient to fully explain tumorigenesis. We demonstrate that these mutations cooperate with somatic copy number variations (CNVs) in a tissue-specific pattern of genomic epistasis. Analyzing 93,462 tumors, we identified 54 gene-cancer type pairs with significant co-occurrence of somatic mutations and CNVs. Our new Binoculars algorithm, which resolved phased DNA/RNA reads, revealed frequent preferential amplification in oncogenic mutation alleles, including AKT1 p.E17K, BRAF p.V600E, KRAS p.G12C/D/V, NRAS p.Q61K, and a fraction of gain-of-function TP53 p.R175H. Conversely, deletions selectively targeted the reference alleles, leading to loss of heterozygosity of IDH1 p.R132H and tumor suppressor mutations, including CDKN2A and TP53 truncations. Lung cancer patients carrying co-occurrences of somatic mutation-CNVs in TP53 and KRAS showed poorer survival than those carrying the same gene mutations. These findings reveal epistasis of cancer mutations and CNVs at an allelic resolution, suggesting specific genomic events to enhance patient stratification and therapeutic targeting.

genomics↗