Jul 2026· Magna Scientia Advanced Research and Reviews· 0 citations
TL;DR
Recent literature on the application of artificial intelligence (AI) and data science within bioinformatics-driven cancer drug discovery is synthesized, examining how these tools are reshaping target identification, molecular design, biomarker discovery, and treatment personalization.
Abstract
Cancer remains one of the leading causes of death worldwide, with the GLOBOCAN estimates placing the 2022 global burden at close to 20 million new cases and 9.7 million deaths (Bray et al., 2024), a burden projected by the American Cancer Society (2024) to rise to roughly 35 million annual cases by 2050. Conventional cytotoxic chemotherapy, though still central to treatment for many tumor types, is frequently associated with prolonged treatment courses, non-specific systemic toxicity, and reduced quality of life. This review synthesizes recent literature on the application of artificial intelligence (AI) and data science within bioinformatics-driven cancer drug discovery, examining how these tools are reshaping target identification, molecular design, biomarker discovery, and treatment personalization. The analysis shows that deep learning-based protein structure prediction (Jumper et al., 2021), generative molecular design (Gangwal & Lavecchia, 2024), multi-omics target identification (Bhinder et al., 2021; Wei et al., 2023), digital pathology and radiomics (Bera et al., 2022; Lu et al., 2024), and machine learning models for predicting chemotherapy toxicity (Huang et al., 2024; Moslemi et al., 2025) are collectively shortening discovery timelines, improving the precision of treatment selection, and reducing treatment-related adverse effects in reported studies. Case evidence is presented, including a generative-AI-designed molecule that reached Phase I clinical trials in under 30 months (Insilico Medicine, 2022) and the 2024 Nobel Prize in Chemistry awarded for the AlphaFold protein-structure-prediction system. While these advances present a credible pathway toward shorter, more targeted, and less toxic cancer treatment, and in specific molecular contexts may reduce reliance on conventional chemotherapy, the evidence does not yet support claims that AI will universally eliminate chemotherapy; rather, it points toward an increasingly personalized standard of oncologic care. The review concludes by discussing the ethical, regulatory, and data-governance barriers that must be addressed for these gains to be realized safely and equitably.
This review aims to explore the computational foundations of big data in cancer genomics and examine emerging pathways that support precision oncology and personalized cancer care. A narrative review approach was adopted to synthesize evidence from PubMed, Scopus, Web of Science, and IEEE Xplore. The literature search was conducted between January 10 and February 25, 2026, and 68 relevant studies were included in the final synthesis. Relevant studies were selected on the basis of their focus on computational methods, data integration strategies, and artificial intelligence (AI) applications in cancer genomics. Extracted data were organized into thematic categories and analyzed using an iterative synthesis framework. The findings indicate that high‐throughput sequencing and multi‐omics technologies have significantly expanded the volume and complexity of cancer‐related data. Advanced infrastructures, including cloud platforms, improve storage and access but raise privacy and interoperability concerns. Machine learning and AI support tumor classification, biomarker discovery, and treatment prediction. Integrative multi‐omics enhances biological insight and predictive accuracy. However, challenges such as data heterogeneity, limited model generalizability, and gaps in clinical integration remain. Big data in cancer genomics offer substantial potential to advance precision oncology by enabling more accurate and personalized treatment strategies. However, overcoming technical, ethical, and infrastructural barriers is essential to ensure effective translation into clinical practice and equitable healthcare outcomes.
Nur Vanu, Nur Mohammad, Fahad Ahmed et al.· Computational and Systems On...· 0 citations
Natural products (NPs) have historically yielded numerous therapeutic agents, yet their integration into modern drug discovery has been constrained by chemical complexity, low abundance, laborious dereplication, and limited target annotation. Convergence of multi-omics technologies with high-resolution structural and biological data has created unprecedented opportunities for artificial intelligence (AI) to accelerate NP-based therapeutics development. This review provides an operational, end-to-end workflow that explicitly connects computational predictions to medicinal chemistry decision points, addressing a critical gap between computational prediction and clinical translation. We trace the complete discovery pipeline: computational mining of biosynthetic gene clusters (BGCs) and metabolomes, deep learning (DL)-assisted structural elucidation and dereplication, network-based target identification using protein-ligand prediction, and generative molecular design inspired by NP scaffolds (including large language models, diffusion models, and genetic algorithms). Critical evaluation of current limitations (data scarcity, lack of standardized ontologies, model interpretability) is complemented by discussion of emergent strategies (foundation models trained on multi-modal data, graph neural networks, autonomous closed-loop laboratories). Representative case studies, including the synthetic AI-designed clinical benchmark rentosertib, illustrate the current evidence spectrum from discovery-level validation to early clinical benchmarking, while also highlighting that most AI-enabled NP discovery workflows remain at the preclinical or proof-of-concept stage, with limited quantitative evidence of improved clinical productivity. We conclude with an Outlook proposing feasible developments for 2025-2030: self-driving laboratories with reported acceleration in specific experimental contexts, foundation models enabling hypothesis-free chemical space exploration, and sustainability-aware AI frameworks embedding biodiversity impact assessments. This operational focus fills a critical gap between algorithmic capability and clinically actionable NP-derived leads. Importantly, while AI has demonstrably accelerated several early discovery steps, quantitative comparisons with classical NP workflows remain limited, and most reported advances are supported by preclinical or proof-of-concept studies rather than systematic evidence of improved time-to-lead, cost reduction, or clinical success rates.
Antonio Lavecchia· Medicinal research reviews (...· 0 citations
Artificial Intelligence (AI) is transforming drug discovery by making the process faster, more cost-effective, and more accurate than traditional methods, which often require 10–15 years and billions of dollars to develop a new drug. AI techniques such as machine learning, deep learning, natural language processing, reinforcement learning, and generative AI are widely used for drug target identification, biomarker discovery, molecular screening, toxicity prediction, lead optimization, and clinical trial support. Advanced models including Support Vector Machines (SVM), Random Forests (RF), Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), and Transformers improve the prediction of molecular properties and drug-target interactions, while generative AI enables the design of novel therapeutic molecules. This study reviews AI-driven drug discovery methods, presents a structured AI pipeline from data collection to candidate selection, and evaluates performance using metrics such as prediction accuracy, screening efficiency, lead optimization success, and toxicity reduction. Despite its advantages, AI faces challenges including limited high-quality datasets, model bias, interpretability, regulatory uncertainty, computational complexity, and integration with conventional laboratory workflows. The findings indicate that AI significantly improves drug discovery efficiency, reduces research costs, and accelerates pharmaceutical innovation. Future advancements will rely on explainable AI, multimodal biological data integration, federated learning, and stronger regulatory frameworks.
Joseph Robin· International Journal of Mod...· 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.
K. Herbetko, Katarzyna Herbetko, Magdalena Mikołajek et al.· Future Medicinal Chemistry· 0 citations
Traditional cancer drug discovery encounters challenges, including lengthy synthesis durations, high costs, and a 90% failure rate in clinical trials, primarily due to inadequate chemical design and drug properties. Artificial intelligence (AI) provides powerful computational tools to overcome these issues by speeding up target identification, predicting properties, and optimizing leads. This review emphasizes the influence of new AI platforms like AlphaFold3, molecular interactions are structurally optimized (MISATO), and ZairaChem on the discovery of oncology drugs. We specifically examine how AI reconciles chemical design with pharmacological feasibility. In addition to evaluating these advancements, we meticulously evaluate methodological challenges, including dataset bias, overfitting, insufficient external validation, and reproducibility issues. Furthermore, the development of complex and targeted modalities, such as antibody-drug conjugates (ADCs), aptamer-drug conjugates (Ap‑DCs), and proteolysis-targeting chimeras (PROTACs), is being explored for cancer treatment using AI. Following a detailed review of regulatory and clinical translation issues, this review presents practical tips for improving model validation, data sharing, and incorporation into medicinal chemistry workflows. By examining successes and persistent limitations, this review article offers a strategic roadmap for leveraging AI to provide clinically translatable cancer therapies with enhanced chemical and pharmacological balance.
Mohsin Ali, Muhammad Ali Tajwar, Farid Ahmed et al.· Medicinal research reviews (...· 0 citations
Introduction Breast cancer remains one of the leading causes of cancer-related mortality rate worldwide, and the identification of effective drug combinations is an essential requirement in pharmaceutical research. The integration of Artificial Intelligence (AI) in processing large volumes of chemical and biological data combines molecular representation, predictive modeling and structured support within a single accessible tool, which accelerates early-stage candidate identification for breast cancer research while promoting reproducibility, transparency and user centered design. Aim The current research focuses on developing and designing “PredictRx” which is an artificial intelligence based driven decision support tool which tends to benefit healthcare practioners to analyze the combination of drug which can be utilized for breast cancer patients. Methodology PredictRx was developed using molecular descriptors, physicochemical properties, and drug interaction datasets collected from publicly available biomedical databases. The tool integrates in total six supervised and unsupervised learning techniques to examine the structural similarities between compounds and predict the potential drug interactions for breast cancer. Various machine learning techniques, including Random Forest, Support Vector Machine, Logistic Regression, K-Means Clustering, DBSCAN, and Agglomerative Clustering, to analyse structural similarities and predict potential drug interactions and synergy patterns. Model performance was evaluated using Classification matrix, Silhouette Score, Calinski-Harabasz Index, and Davies-Bouldin Index. The tool was deployed as a browser-accessible web application for real-time interaction and visualization. Result The results suggests that Random Forest has the highest predictive performance accuracy of 1, and Agglomerative clustering delivered strongest scores (Silhouette Score: 0.6946; Davies-Bouldin Index: 0.2457). The current tool was deployed as a browser accessible web tool with possibility of real time interaction and result visualization. PredictRx is a distinctive easy to use, and interpretable screening tool focused on drug compatibility and synergy analysis. EDA further identified molecular weight, lipophilicity, and structural similarity as important contributors to drug compatibility prediction. Conclusion PredictRx shows how AI-driven predictive modeling which can speed up molecular screening and early-stage breast cancer medication discovery. The technology facilitates the effective identification of appropriate drug combinations and offers a scalable foundation for upcoming AI-assisted pharmaceutical research by combining clustering, classification, molecular representation, and visualization into a single interpretable platform.
Ritu Chauhan, Neha Pandey, M. Zuhairi· Frontiers in Artificial Inte...· 0 citations