AI-DRIVEN PARADIGMS IN PRECISION ONCOLOGY: MAPPING THE CONVERGENCE OF MULTI-OMICS DRUG DISCOVERY, MEDICINAL CHEMISTRY, AND ENVIRONMENTAL CARCINOGENESIS

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 silico multi-parameter optimization. This systematic review comprehensively evaluates computational advancements in oncology drug discovery published between 1997 and 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 novo molecular 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 bioactivation pathways 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 and structural 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.