HIV-1 and Artificial Intelligence: From Molecular Insight to Population Impact

Artificial intelligence (AI) has become an indispensable ally in virology, enabling the analysis of enormous datasets that extend from viral genomes to behavioral and clinical information. HIV-1, a rapidly evolving retrovirus with extraordinary genetic diversity and a persistent latent reservoir, poses unique computational challenges that are now approachable through data-driven models. Modern machine-learning and deep-learning architectures can decode viral sequences, predict drug resistance and co-receptor usage, simulate evolutionary trajectories under therapy, and integrate multi-omics information to identify molecular determinants of persistence. In parallel, AI-assisted chemoinformatic shortens drug-discovery cycles, while network and language models enhance epidemiological surveillance and individualized care. The convergence of AI with organoid technologies, single-cell systems biology, and population informatics is redefining HIV research from static observation to dynamic prediction. Ethical transparency, algorithmic fairness, and equitable access remain central to ensuring that these innovations accelerate-not distort-the path toward durable remission and cure.