AI-DRIVEN PARADIGMS IN PRECISION ONCOLOGY: MAPPING THE CONVERGENCE OF MULTI-OMICS DRUG DISCOVERY, MEDICINAL CHEMISTRY, AND ENVIRONMENTAL CARCINOGENESIS
2026· Asian Journal of Medical Research and Health Sciences· Vol 04, pp. 2180-2202· 0 citations
TL;DR
This systematic review comprehensively evaluates computational advancements in oncology drug discovery published between 1997 and 2026 and concludes that AI has transitioned into an indispensable, data-driven framework for modern oncology chemistry and structural toxicology.
Abstract
Traditional oncology drug discovery is severely constrained by prolonged timelines, high attrition rates, and the non-linear, spatial-temporal heterogeneity of malignant neoplasms. Rapid mutational rewiring and the active upregulation of ATP-binding cassette (ABC) efflux transporters (e.g., ABCB1) routinely undermine static small-molecule pipelines. Artificial intelligence (AI) has emerged as a multi-scale systems biology framework capable of transforming this landscape from empirical screening into predictive in silicomulti-parameter optimization. This systematic review comprehensively evaluates computational advancements in oncology drug discovery published between 1997and 2026. We examine the structural parameterisation of multi-modal data layouts—including 1D SMILES, 3D molecular graphs, 3D voxel density grids, and single-cell transcriptomics—across four core analytical pillars: target discovery, de novomolecular generation, virtual screening, and precision clinical translation. Furthermore, we audit the field’s primary computational and ethical bottlenecks through the lens of algorithmic fairness, socio-demographic training bias, data-silo constraints, and hardware-aware cryptographic privacy. The evidence synthesizes how deep learning architectures (such as Graph Neural Networks, Generative Adversarial Networks, and Vision Transformers) accurately model biochemical cascades, bypass costly semi-empirical wavefunction simulations, and map the metabolic bioactivationpathways of exogenous environmental carcinogens. However, significant challenges remain regarding dataset shift and demographic skew, where public biobanks overrepresent European ancestry(81.3%), leading to elevated predictive errors (up to 34.5%) in underserved cohorts. We evaluate technical solutions to these constraints, documenting that fairness-aware loss optimization successfully reduces performance variances across ancestral subgroups. Additionally, decentralized architectures—specifically Federated Learning combined with Homomorphic Encryption (HE) or Secure Multi-Party Computation (MPC)—demonstrate generalizability scores matching centralized data pools while preserving patient privacy. AI has transitioned into an indispensable, data-driven framework for modern oncology chemistry andstructural toxicology. Realizing its full clinical potential requires moving beyond internal cross-validation toward hybrid workflows that combine physics-informed neural networks with prospective multi-centric clinical validation and robust, localized bias-mitigation layers.
Colorectal cancer is a leading cause of cancer death, yet its molecular heterogeneity remains poorly translated into individualized treatment. We present a reproducible artificial intelligence (AI) framework that integrates multi-omics benchmarking, sample-level drug prioritization, E3 ubiquitin ligase selection, and shape-anchored Proteolysis Targeting Chimera (PROTAC) design for KRASG12D in colon adenocarcinoma (COAD). A controlled synthetic benchmark comprising 425 tumor and 41 simulated normal profiles, parameterized to match The Cancer Genome Atlas (TCGA) distributions, was used for pipeline verification. Among sixteen methods, the Balanced Latent Integration with Stability Selection (BLISS) model achieved the highest silhouette width (0.86) and competitive agreement (Adjusted Rand Index, ARI, 0.90). The pipeline was validated on real data: a TCGA COAD cohort (186 tumors) with independent Consensus Molecular Subtype (CMS) labels and a CPTAC cohort (104 tumors). Integration modestly recovered CMS (ARI 0.28), and stage, not molecular cluster, drove survival (log-rank p = 0.005 versus 0.81). Sample-level prioritization differed from cluster-level ranking in 82.6% of profiles, below chance (p < 0.0001), without indicating efficacy. Candidate NOVEL00489 showed a good MM-GBSA estimate, matching the reference ASP3082. Compounds are computational candidates requiring experimental validation. This establishes a transparent benchmark for in silico degrader generation in precision oncology.
Khaled M. Elamin, S. Elbashir, I. Adam· International Journal of Mol...· 0 citations
The discussion on precision oncology integrates multiomics technologies and artificial intelligence, specifically addressing biomarker discovery and personalized therapeutic strategies. In this way, clinical translation and multiomics biomarkers are reconstructed challenges such as heterogeneity, validate, algorithmic bias, regulatory complexities, and ethical issues. This review critically evaluates how advanced technologies operating in an integrated manner facilitate precision oncology by supporting fields such as genomics, transcriptomics, proteomics, metabolomics, and radiomics. To conduct the study, we performed literature search using standard databases such as PubMed, Web of Science, and Scopus, focusing on papers published between 2020 and 2025. We address the all aspects of biomarker identification and clinical applications; we employed a five-stage framework comprising multiparametric data generation, integration, biomarker discovery, rigorous validation, and regulatory implementation. To further examine this review, we have employed emerging computational approaches, including machine learning and deep learning and graph neural networks alongside regulatory frameworks and ethical, legal, and social considerations. Discussing translation barriers, we consider factors such as limited reproducibility, validation, and critical discussion particularly studies. Ultimately, a future model based on standardized, validated, and transparent learning strategies accelerates and fosters the development of clinically reliable standards. Our review provides an integrative roadmap for modern, multiomics-driven biomarker approaches in precision oncology practice.
Ujwal Havelikar, Atharva A. Shinde, Hrushikesh Mhaismale et al.· Omics· 0 citations
Glioblastoma (GBM) is a malignant brain tumor frequently driven by mutations in isocitrate dehydrogenase (IDH1 and IDH2) enzymes, which promote neomorphic synthesis of the oncometabolite 2-hydroxyglutarate (2-HG) and subsequent metabolic dysfunction. Although these mutations are associated with different clinical outcomes, therapeutic intervention remains challenging due to tumor heterogeneity, cellular plasticity, restricted blood-brain barrier permeability, drug efflux mechanisms, and effective DNA repair pathways. Therefore, this study aims to identify selective inhibitors of mutant IDH proteins by overcoming these challenges.
10,309 compounds from ChemFaces and MedChemExpress libraries are used for large-scale virtual screening and multi-level ADMET filtering, uncovering 19 lead compounds with favourable drug-likeness. Further, molecular docking followed by 300 ns of molecular dynamics simulations, PCA analysis, and MM-PBSA calculations were performed to explore conformational dynamics.
Molecular docking revealed strong binding affinities of PubChem ID 91457 (I1Hit1) as −7.57 kcal/mol and PubChem ID 4946 (I1Hit2) as −6.82 kcal/mol against IDH1, and PubChem ID 71550939 (I2Hit1) as −11.23 kcal/mol and PubChem ID 4946 (I2Hit2) as −7.87 kcal/mol against IDH2, by outperforming the standard (−6.17 kcal/mol). Molecular dynamics simulations, PCA analysis, and MM-PBSA calculations confirmed complex stability and inhibitory potential. Notably, I1Hit1 and I2Hit1 exhibited enhanced binding free energies of −20.67 ± 2.32 kcal/mol against IDH1 and -28.78 ± 2.67 kcal/mol against IDH2 respectively, compared to the standard, as further supported by computational pharmacophore evaluation.
Collectively, these findings highlight I1Hit1 and I2Hit1 as novel therapeutic compounds with efficient IDH-target inhibition to address epigenetic modification in GBM, and further experimental validation of these compounds is required to demonstrate potential inhibitors of IDH-driven metabolism in GBM.
Nivedhitha Tamilazhagan, S. Arumugam· Frontiers in Bioinformatics· 0 citations
TUBG1 (γ-tubulin 1) is a centrosome-associated tubulin essential for microtubule nucleation and mitotic spindle organization. Despite the role of cytoskeletal dysregulation in cancer, the pan-cancer clinical, immunogenomic, and druggability landscape of TUBG1 remains poorly defined. We characterized TUBG1 as a pan-cancer oncogenic candidate, assessed its prognostic and immune relevance, and identified novel small-molecule inhibitors through integrated in silico design and molecular modeling. Using public multi-omics resources, we evaluated TUBG1 expression across tumor types, disease states, and protein abundance using CPTAC data. Associations with grade, stage, and survival were assessed using correlation analyses and Kaplan-Meier plots. Genomic alterations, promoter methylation, and immune context (via purity-adjusted deconvolution, TMB, and MSI) were profiled. Pharmacogenomic correlations and single-cell analyses were performed. De novo ligand generation, docking, molecular dynamics, binding free-energy estimation, ADMET screening, and DFT prioritized candidate inhibitors. TUBG1 was significantly dysregulated across multiple tumors, with increased protein abundance in glioblastoma, hepatocellular carcinoma, and head and neck squamous cell carcinoma. Expression correlated with tumor grade, pathological stage, and poorer survival. Tumor-specific alteration and methylation patterns were observed. TUBG1 correlated with immune features and TMB/MSI in selected cancers. Two de novo compounds showed strong docking scores, stable dynamics, and favorable binding energies. This pan-cancer study supports TUBG1 as a clinically and immunogenomically relevant candidate and nominates computationally prioritized inhibitors for experimental validation.
Muhammad Alaa Eldeen, E. Fayad, Soha Y Alkhaldi et al.· Journal of Computational Bio...· 0 citations
This report systematically reviews the structural development, validation pathways, and regulatory mechanisms of next-generation oncology therapeutics and highlights clinical milestones and regulatory approvals from 2025 and 2026, including the landmark PROTAC vepdegestrant.
Rokonuzzaman, Md Nazmul Islam· International Journal of Pha...· 0 citations
Artificial intelligence (AI) has emerged as a transformative analytical paradigm for modelling cancer progression using medical imaging and complementary clinical data. This systematic review collates and analyses AI-powered systems and techniques for cancer progression risk analytics and prediction. It provides an empirical examination of algorithmic approaches, imaging-derived feature domains, and clinical metrics that underpin progression prediction. Following the PRISMA 2020 reporting guidance, empirical English-language studies published between 2020 and the final search cutoff date of 15 May 2026 were identified from PubMed, EBSCOhost, Google Scholar and Web of Science. Forty (40) eligible studies spanning diverse cancer types were included, appraised using the MMAT and synthesised narratively. The evidence shows that both classical machine learning models and deep learning architectures are widely used to extract predictive information from radiological data. Radiomic descriptors of intratumoural heterogeneity, tumour morphology, functional imaging biomarkers and multiscale transform-based features consistently demonstrate strong associations with disease progression. Imaging-derived features were linked to clinically meaningful progression endpoints, including progression-free survival, disease-free survival, recurrence and metastasis, while clinical and molecular covariates supported risk stratification. This study further develops the AI-driven cancer progression risk analytics framework (AI CanPRAF), which integrates AI taxonomies, imaging phenotypes, clinical context, progression analytics and decision support into one clinically oriented model. The results demonstrate the growing role of multimodal AI systems in the development of clinically grounded cancer progression analytics and prediction solutions.
Wellington Kanyongo, B. Chimbo· Information· 0 citations