Keyword: Artificial Intelligence

ABSTRACT: The human microbiome functions as a metabolically active organ whose biochemical output is continuously integrated with host physiology. Conventional microbiome surveys, built largely on taxonomic profiling, capture community composition and diversity but resolve neither the functional capacity of these communities nor the bidirectional metabolic exchange that links them to the host. A central limitation is that taxonomy is a poor proxy for function: phylogenetically distinct organisms can perform equivalent reactions, and closely related taxa can diverge metabolically. Resolving host microbiome interactions, therefore, requires integration across heterogeneous, high-dimensional molecular layers, such as metagenomics, metatranscriptomics, proteomics, metabolomics, and host genomic and phenotypic data at a scale and complexity that exceeds classical analytical pipelines. Artificial intelligence (AI) has emerged as a complementary framework for this problem. Machine learning, deep learning, and graph-based models can integrate multi-omics data, infer latent metabolic structure, predict microbial functional potential, and model microbe-metabolite-host relationships as connected networks rather than isolated parts. These approaches have sharpened the discovery of disease-associated microbial and metabolic signatures and candidate therapeutic targets, and they underpin emerging precision medicine applications, including individualized risk stratification, biomarker discovery, and treatment response prediction. Substantial barriers remain, however, including incomplete and non-standardized reference data, limited model interpretability, vulnerability to bias and overfitting, and a shortage of prospective clinical validation. Continued progress in foundation models, real-time microbiome monitoring, and patient-specific metabolic modelling is expected to move the field from descriptive association toward predictive, preventive, and personalized clinical application. 

ABSTRACT: It takes ten to fifteen years for a compound to progress from its identification to regulatory approval as a drug. Drug discovery is complex and resource-intensive process in which more than 90% of compounds never make it from bench to bedside and eventually get rejected during the development process. Experimental drugs failures often occur due to poor target selection, inadequate preclinical models, unforeseen toxicity, lack of efficacy in human trials, and the complexity of disease mechanisms, which make it difficult to predict drug responses accurately. Additionally, drug discovery is slowed down by a lack of collaboration between academia and industry, limiting the timely exchange of knowledge and expertise. Artificial intelligence (AI) is becoming an important tool in drug discovery, offering new possibilities to overcome existing challenges. It can help researchers identify better drug targets, make the screening process more efficient, and optimize drug design, which could speed up development and improve success rates. However, use of AI is associated with certain drawbacks such as potential exacerbation of healthcare gaps, protection of sensitive patient data and a need for informed consent. This review aims to discuss key challenges that hinder drug development process and explore future directions to enhance the efficiency of drug discovery.