Artificial intelligence in multi-omics analysis of small-molecule drug discovery

Artificial intelligence (AI) is transforming multi-omics analysis in small-molecule drug discovery by advancing vast datasets from genomics, transcriptomics, proteomics, and metabolomics to cover novel therapeutic targets and optimize lead compounds. Machine learning (ML) algorithms, such as deep neural networks (DNNs) and graph convolutional networks (GCNs), excel at identifying complex patterns in multidimensional omics data, predicting drug-target interactions, and predicting molecular dynamics with exclusive accuracy. AI platforms as AlphaFold, accelerated protein structure prediction, allowing virtual screening of millions of small molecules. In multi-omics systems, generative adversarial networks (GANs) and transformers synthesize multimodal data, attractive biomarker discovery, and reduce preclinical failure rates from 90 % to potentially below 70 %. Challenges like data heterogeneity and interpretability, AI biases through federated learning, and explainable AI techniques. This synergy potentiates faster, cost-effective drug development, escorting in a new era of precision medicine.