Jul 2026· Journal of Chemical Information and Modeling· Vol 66, pp. 8893 - 8907· 0 citations· 29 references
MedicineComputer Science
TL;DR
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.
Abstract
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.
The BIRC5-NAMPT-TOP1 combination showed reproducible phenotypic activity in the tested genetic and pharmacological models and support the use of DeepMDS as a hypothesis-generation tool for higher-order target prioritization.
Chun-Lai Feng, Qiuqi Feng, Sheng-Nan She et al.· Pharmaceuticals· 0 citations
This review provides a comprehensive overview of the major metabolic pathways that are dysregulated in TNBC and their relevance to disease progression and therapeutic intervention, highlighting the integration of artificial intelligence and machine learning approaches.
An integrated chemoinformatics and machine learning workflow with ligand-based similarity filtering is developed and prospectively validated, identifying chlormidazole as a promising repurposing candidate for PCa and demonstrating the value of integrating chemoinformatics with ML for drug repurposing and virtual screen...
Leonardo Bernal, Luca Pinzi, T. Martinelli et al.· Journal of Medicinal Chemist...· 0 citations
This review highlights the synergy between AI and HTS, emphasizing DL techniques such as convolutional neural networks for bioactivity prediction, recurrent neural networks for de novo design, and reinforcement learning for property optimization.
Kamil Herbetko, Katarzyna Herbetko, Magdalena Mikołajek et al.· Future Medicinal Chemistry· 1 citation
Despite training on 5000 SMILES due to limited computational resources, the model achieved high validity, strong molecular diversity, and drug-like physicochemical properties, demonstrating the feasibility of a scalable, target-specific AI pipeline for lung cancer drug discovery using fragment-based molecular generatio...
Mohavia Ben Amid Sinon, U. Chude-Okonkwo· Drugs and Drug Candidates· 0 citations
The findings demonstrate the potential of combining generative AI with high-throughput computational screening as an efficient, reliable, and scalable approach to identify inhibitors for breast cancer targets, although these inhibitors need to be tested in the lab to validate their biological activity and potential for...
Saad Amin, G. Albadrani, Refka Ghodhbani· Journal of Pharmaceutical In...· 0 citations
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