CRACKING THE MOLECULAR MATRIX: NEXT-GEN AI RESHAPES BIOMEDICINE

*Mohammad Yaghoub Abdollahzadeh Jamalabadi

ABSTRACT

Recent advances in computational intelligence are reshaping biomedical research, particularly in genomics and pharmaceutical development, by enabling faster, more precise scientific breakthroughs. This review synthesizes contemporary progress and emerging trends in neural network-driven approaches for analyzing biological sequences and streamlining therapeutic innovation. Within genomics, sophisticated architectures now achieve state-of-the-art performance in tasks such as genetic variant detection (e.g., DeepVariant) and regulatory element prediction (e.g., Enformer), surpassing conventional statistical methods. These frameworks have further enhanced resolution in epigenomic mapping, single-cell profiling, and functional annotation, revealing intricate biological mechanisms at scale. In therapeutic development, machine intelligence optimizes workflows spanning target prioritization, molecular screening, and clinical trial simulation. Architectures such as generative adversarial networks accelerate compound design, while graph neural networks improve property prediction, reducing attrition rates in preclinical phases. The fusion of genomic signatures with computational pharmacology enables bespoke treatment strategies, supported by unified analysis of multi-omics datasets. Critical hurdles remain, including limited high-quality training data, "black-box" model opacity, domain adaptation constraints, and ethical dilemmas in data governance. Promising avenues include cross-modal learning systems, autonomous molecular optimization, patient-specific in silico models, and hybrid quantum-AI pipelines. By bridging computational and life sciences, these innovations herald a new era of tailored medicine, offering scalable solutions to long-standing challenges in understanding and treating human disease.

Keywords:

Bioinformatics Pipeline; High-Throughput Sequencing (HTS); Next-Generation Sequencing (NGS); RNA-Seq Analysis; ChIP-Seq Analysis; DNA Methylation Profiling; Transcriptomics; Microarray Data Analysis; Gene Expression Profiling; Genome Assembly; Phylogen

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