Jul 2026· Journal of Applied Pharmaceutical Sciences and Research· Vol 9, pp. 22-31· 0 citations
TL;DR
AI-driven approaches in transcription modulator discovery are highlighted, including in silico target identification, multi-omics integration, and structure–activity optimization, and the importance of explainable AI and personalised therapeutics in advancing precision medicine and next-generation transcription-based drug discovery is emphasised.
Abstract
Transcriptional regulation is a critical mechanism controlling gene expression and plays a major role in cancer, genetic disorders, and complex diseases. However, developing drugs that precisely target transcriptional processes remains challenging due to the structural complexity of transcription factors and risks of off-target effects. Recent advances in artificial intelligence (AI) have transformed drug discovery by enabling better modelling of genomic and regulatory landscapes. This review highlights AI-driven approaches in transcription modulator discovery, including in silico target identification, multi-omics integration, and structure–activity optimization. It also discusses deep learning and transformer-based genomic models for identifying disease-specific regulators and DNA elements. Furthermore, the review examines progress in developing small-molecule, epigenetic, and RNA-targeting drugs. Finally, it emphasises the importance of explainable AI and personalised therapeutics in advancing precision medicine and next-generation transcription-based drug discovery.
The increasing complexity of disease pathogenesis and the limitations to the traditional approaches to drug discovery suggests that it is necessary to move to more effective and integrative approaches. In this study, a genome-wide genetic pathway analysis system has been built, and an artificial intelligence (AI)-assisted framework to identify the drug targets based on a genetic pathway analysis has been developed. The used dataset was the Genomics of Drug Sensitivity in Cancer (GDSC) data that included both measures of drug response and genomic features (gene expression, copy number alterations, and methylation data). The use of machine learning models like Random Forest, XGBoost, and Artificial Neural Networks were implemented in drug sensitivity prediction. The XGBoost is the best in predictive accuracy among them. Importance of feature analysis and pathway enrichment analysis revealed that key determinants of drug response are key signalling pathways, with PI3K-Akt and MAPK pathways being the most important. Moreover, a drug-target pathway network was built to explain the intricate biological interactions and to find out possible therapeutic targets. The results showed the utility of the combination of AI with pathway-level bioinformatics analysis to not only provide predictive accuracy but also provide biological interpretability. Though the use of in vitro data is limited in the study, the study provides a scalable, robust framework of drug discovery based on AI assistance. The results can be used to further the field of precision medicine through the identification of biologically relevant targets as well as optimization of therapeutic approaches.
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The integration of computational prediction with programmable genome and RNA engineering may support more precise, adaptable, and clinically translatable mRNA therapeutics.
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Pharmacogenomics and artificial intelligence (AI) are emerging as important drivers of precision medicine, enabling healthcare systems to adopt individualized therapeutic approaches. Pharmacogenomics examines how genetic variations influence drug response, efficacy, metabolism, and toxicity, while AI provides advanced computational tools for analyzing complex genomic and clinical data. This review highlights the integration of AI-driven pharmacogenomics in personalized therapy and its potential to improve treatment outcomes. Machine learning, deep learning, natural language processing, and big data analytics are increasingly used to identify genetic variants, predict drug responses, optimize medication selection and dosage, and reduce adverse drug reactions. These technologies support the interpretation of large-scale genomic information and facilitate evidence-based clinical decision-making. Significant applications have been demonstrated in oncology, cardiovascular diseases, neurological and psychiatric disorders, and rare genetic diseases, where personalized treatments can enhance therapeutic efficacy and patient safety. Recent advances in genomic sequencing, multi-omics integration, digital health technologies, explainable AI, and real-time patient monitoring have further expanded the scope of precision medicine. However, challenges related to data privacy, algorithm bias, regulatory frameworks, and clinical implementation remain. Future developments in explainable AI, predictive analytics, and AI-powered personalized therapy are expected to improve treatment precision and accelerate the realization of truly individualized healthcare. Overall, the convergence of AI and pharmacogenomics represents a transformative approach to modern medicine with substantial potential to improve patient outcomes and optimize therapeutic interventions.
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The principles that shape cardiovascular ASO candidate development are discussed, with emphasis on mechanism selection, chemical design, and exposure feasibility, and sequence optimization with exposure-informed target qualification and therapeutic-index engineering throughout ASO candidate selection.
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This review synthesizes current understanding of TF function in tumorigenesis, moving from mechanistic insights at the level of individual TFs to the higher-order organization of transcriptional regulatory networks, and assesses emerging therapeutic strategies aimed at perturbing aberrant TF activity.
The evolving role of AI in modern drug discovery is discussed while highlighting the importance of explainable algorithms, high-quality biomedical data, real-world evidence, and interdisciplinary collaboration.
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