Smart Drug Design: AI in Transcriptional Modulation and Clinical Innovation
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.