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

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

ICBcDrug is a freely accessible and valuable resource for advancing ICB combination therapy that integrates 2311 reported or predicted compounds across 18 cancer types and accurately predicted both the efficacy and potential mechanisms of the known ICB enhancer entinostat.

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

This study demonstrates the viability of the deep learning models to discover structurally novel anticancer agents that are distinct from conventional drugs, thereby expanding the therapeutic arsenal for TNBC patients.

Machine learning-guided dose-time optimization and experimental validation enhance coumarin therapeutics in oncology.

Experimental analysis showing that the ensemble model predictions align with in vitro results provides initial support for the potential of ML to help reduce experimental complexity, though further validation across additional cell lines and coumarins is warranted.

Multiclass Machine Learning-Based Discovery of Novel Scaffold Inhibitors Targeting ALK

This integrated ML-to-simulation workflow prioritizes structurally novel candidate hits with predicted ALK inhibitory activity and provides an effective strategy for scaffold discovery and hit prioritization.

Abstract A037: Drug Development Using Machine Learning Approches: The Therapeutic Impact of Golnar on Inhibition of Tumor Growth in Colorectal Cancer

Colorectal-cancer (CRC) is the third leading cause of mortality due to cancer, thus there is a need for innovative-therapeutic-agents to enhance the efficacy of current treatments and improve outcomes. Here we performed different machine learning (MI) approaches (e.g., random forest, support vector machines, convolutional neural networks, CNN,…), with capable of handling the complex relationship between target markers, and CRC were utilized to select an approapriate with higher efficancy agent and then investigated the therapeutic impact of PGP in CRC. RNAseq and the integrative systems biology technique followed by MI algoritisms were applied to identify differentially expressed genes (DEGs) followed by validation in a large cohort of patients and then PGP was selected for in vitro and in vivo studies. Antiproliferative-activity of (Punica granatum var. pleniflora (PGP)) was tested in 2 and 3D cell-culture models. The effect of PGP on migratory-behaviors and apoptosis was determined using a wound-healing-assay and AnnexinV/PI staining, respectively. The expression were assessed using q-RT-PCR. Molecular-Pathology and histopathological-assessment was used followed by evaluation of oxidative-stress-markers. Metabolomics for assessment of chemical and active components of the PGP extract were determined by LC-MS/MS. The result illustrated a total of 856 upregulated/downregulated-genes in patients. Among the high top-score genes, fibrotic/inflammatory pathways were detected and further validated in 65 patients. PGP inhibited cell-growth and migration in cells by modulating CyclinD1, Survivin, and E-cadherin. Furthermore, PGP increased apoptosis. Moreover, PGP significantly decreased tumor-size in an animal-xenograft CRC via perturbation of fibrosis-markers, Col1A/ACTA2. PGP reduced inflammation, and oxidative-stress via modulation of SOD/Cat/total thiol. Phytochemical profiling showed a total of 28 and 43 compounds including Corilagin, Ellagic acid, Gallic Acid and Quercetin-hexoside, which have anti-cancer properties. The results demonstrated the therapeutic potential of PGP in tumor-growth reduction, indicating its potential value as a new approach in the treatment of colorectal-cancer. Aida Yavari Kondori, Mehrdad Moetamani Ahmadi, Seyede Elnaz Nazari, Fereshteh Asgharzadeh, Elisa Giovannetti, Majid Khazaei, Amir Avan. Drug Development Using Machine Learning Approches: The Therapeutic Impact of Golnar on Inhibition of Tumor Growth in Colorectal Cancer [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A037.

Machine learning-guided QSAR screening of fluconazole analogs and FDA-approved drugs against Candida albicans, with docking, molecular dynamics, and ADMET analysis.