ICBcDrug: An online resource and tool for screening and predicting immunotherapy combination drugs.

A Combined Chemoinformatics- and Machine Learning-Based Approach Identifies Chlormidazole as a Drug Repurposing Candidate against Aggressive Prostate Cancer

Despite recent therapeutic advances, treatment options for advanced, therapy-resistant, and metastatic prostate cancer (PCa) remain limited. Here, we developed and prospectively validated an integrated chemoinformatics and machine learning (ML) workflow with ligand-based similarity filtering. Validation on independent external data sets showed that this applicability-domain-guided integration strategy can reduce false positives and improve virtual screening performance. Screening of DrugBank identified five repurposing candidates with confirmed antiproliferative activity in both 2D and 3D PCa models. Among them, the antifungal agent chlormidazole emerged as the most promising candidate, displaying tumor-selective and predominantly cytostatic activity associated with p57 upregulation, reduced Rb phosphorylation, and G1 arrest. Chlormidazole also enhanced the antiproliferative activity of docetaxel in both models, achieving comparable efficacy at substantially lower docetaxel concentrations. These findings identify chlormidazole as a promising repurposing candidate for PCa and demonstrate the value of integrating chemoinformatics with ML for drug repurposing and virtual screening.

AptCancerDB: A Curated Knowledgebase and Translational Discovery Platform for Anticancer Aptamers

Aptamers are emerging as important molecular recognition ligands in oncology, playing significant roles in cancer diagnostics, targeted therapies, drug delivery systems, and molecular imaging. Numerous aptamers have advanced to clinical trials, indicating their potential for real-world applications; however, existing databases fail to capture that. To bridge this critical gap, we developed AptCancerDB (https://webs.iiitd.edu.in/raghava/aptcancerdb/), a comprehensive, manually curated database of experimentally verified anticancer aptamers. The current release contains 1,941 entries collected from studies published between 2000 and 2025, covering 29 cancer types, approximately 200 cancer cell lines, and direct links to 22 clinical trials. Each entry is annotated with sequence information, target details, cancer type, cell line, SELEX methodology, affinity determination data, chemical modifications, and biological activities. The dataset is dominated by 82.7% ssDNA, reflecting its superior stability and ease of synthesis, while only 16.6% is ssRNA and appears primarily in studies targeting complex intracellular or protein-protein interactions. To facilitate structural analysis, predicted secondary structures, dot-bracket notations, specific structural elements, and minimum free energy values were also included. AptCancerDB integrates a MySQL backend with an ArcadeDB/OpenCypher-based Knowledge Graph, enabling exploration of relationships among aptamers, targets, cancer types, cell lines, and functional applications. The platform provides advanced search and browsing facilities, BLASTn-based similarity searching, and GC Calculator. Built on a modern, responsive frontend (React/TypeScript/Tailwind CSS), the platform includes a REST API for data retrieval. By integrating fragmented experimental data into a unified cancer-focused resource, AptCancerDB serves as a valuable resource for comparative analysis, aptamer discovery, and the development of next-generation aptamer-based diagnostics and therapeutics. Highlights Curated knowledge base of experimentally validated anticancer aptamers. AptCancerDB contain therapeutic, tumor-homing and cell-penetrating aptamers. Summarizes clinical progress and translational trends in anticancer aptamer research. Supports rational aptamer design using molecular, functional, and clinical annotations Disease-focused resource for cancer diagnosis, therapy, and drug delivery Teaser AptCancerDB maintains experimentally validated anticancer aptamers relevant to diagnosis, drug delivery, and therapy.

In vitro bioactivity profile-driven discovery of drug synergies in hepatocellular carcinoma

Combination therapy is a central strategy to overcome drug resistance in hepatocellular carcinoma (HCC), yet systematic identification of synergistic combinations is constrained by the combinatorial search space. We developed a data-driven workflow to integrate two orthogonal single-agent resources for 244 drugs, including cell viability profiles across 11 liver cancer cell lines and bioactivity signatures across 1,925 biochemical and cell-based assays. We calculated the correlation of the bioactivity profiles between each drug pair yielding 821 high-confidence combinations involving 190 unique drugs, from which eight anchors and a 125-drug library were selected by frequency-guided prioritization. Fixed-concentration screening of 992 anchor-library combinations in the Hep 3B2.1-7 cell line yielded 89 combinations with potency enhancement. Panobinostat showed the largest potency gains when combined with briciclib or thiocolchicine (69.5-fold and 81.1-fold lower IC₅₀, respectively). Matrix-based screening of these two representative combinations across 11 cell lines revealed pronounced concentration- and context-dependence. At optimal dose pairs, strong synergy was observed in Hep 3B2.1-7 (Zero Interaction Potency (ZIP) = 40.80 ± 4.22 with 58.53 ± 6.54% inhibition), whereas antagonism was observed in some cellular backgrounds. This label-free integration strategy reduces the experimental burden for hypothesis-driven combination discovery and provides context-specific preclinical leads for follow-up evaluation in HCC.

Discovery of novel RIPK3 inhibitors by virtual screening and bioactivity evaluation.

Essentiality-driven prediction of anticancer drug responses in preclinical and clinical contexts

Summary Precision oncology relies on tumor molecular profiles to predict drug responses. Instead of using conventional molecular features directly, we construct predictive signatures based on gene essentiality. Here, we present DrGee, an essentiality-centered platform that infers drug sensitivity solely from gene expression profiles. The built-in DeepEEAA model integrates gene expression, gene essentiality, drug-protein affinity, and drug-gene associations to quantitatively predict IC50 values. DeepEEAA achieved competitive predictive performance on independent cell line datasets (R2 = 0.764; MSE = 0.9345), outperforming recent benchmark deep learning methods. DrGee prioritized four candidate drugs for the 95-D lung cancer cell line, among which BI-97C1 and trimetrexate were validated by in vitro assays and mouse xenograft experiments. Robust predictive performance was further confirmed in OVCAR8 ovarian cancer cells. In TCGA cohorts, essentiality-driven predictions stratified patients with significantly different overall survival outcomes (AUC-PR = 0.825), highlighting the translational potential of DrGee.

Deep Learning-Driven Anticancer Drug Discovery: Emodepside as a Potential Therapeutic Candidate for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with a poor prognosis. The absence of effective targeted therapies and endocrine treatment options leads to limited therapeutic options, which remains one of the major clinical challenges in TNBC management. Drug discovery is typically a lengthy and costly process that could be significantly improved through drug repurposing. However, the biological complexity and insufficient repurposing strategies hinder the reuse. This study aims to develop a deep learning-based framework to accelerate drug discovery for TNBC, identify novel therapeutic candidates, and uncover potential drug targets. We developed a deep neural network framework to predict the anticancer efficacy, toxicity profiles, and structural similarities of compounds. By applying this platform to screen over 6,000 compounds from the Drug Repurposing Hub, we identified promising candidates with potential therapeutic efficacy and safety profiles against TNBC. The top-predicted compounds were subsequently validated through comprehensive in vitro and in vivo functional assays. Furthermore, we employed transcriptomic sequencing and mass spectrometry-based proteomics to elucidate the molecular mechanisms underlying the anti-TNBC activity. We identified emodepside, a structurally unique molecule diverging from conventional anticancer agents that exhibited potent antitumor efficacy across multiple TNBC cell lines. Significantly, emodepside administration (5 mg/kg) inhibited tumor growth in xenograft models. Integrated multiomics analyses (RNA-seq/CETSA-MS) identified NAMPT as the primary target. This study demonstrates the viability of our deep learning models to discover structurally novel anticancer agents that are distinct from conventional drugs, thereby expanding the therapeutic arsenal for TNBC patients. Emodepside emerges as a promising TNBC therapeutic candidate, with a possible mechanism of promoting TNBC cell apoptosis via NAMPT inhibition.