AIHealthcare Analytics

Antimicrobial Drug Discovery

AI, particularly machine learning, deep learning, and natural language processing, is reshaping antimicrobial drug discovery against bacterial, fungal, and viral infections across small molecules, peptides, phages, and protein drugs. A structured review covering 2020–2025 identifies AI-driven predictive and generative strategies as the defining approach for the next decade's "autonomous discovery" paradigm, while noting persistent challenges: data bias, lack of standardized benchmarking, and clinical translational gaps.

Tuberculosis Applications

AI is transforming tuberculosis drug discovery by addressing prolonged timelines, excessive cost, and Mycobacterium tuberculosis biology. ML and deep learning enable systematic analysis of heterogeneous biological and chemical datasets for target identification, virtual screening, and anti-TB activity prediction. Generative modeling supports de novo drug design, and AI advances integration of resistance genomics. Limitations include limited model generalizability, lack of mechanistic insight, data quality issues, and need for prospective validation.

Molecular Design and Protein Engineering

Generative AI models for small-molecule design — including NovMolG-GAN, which achieved 99.6% validity and 99.4% novelty on ChEMBL-35 — demonstrate high performance in unconstrained generation with drug-likeness optimization. AI-driven antibody design platforms use GANs, reinforcement learning, and multi-omics integration to accelerate target identification, optimize lead candidates, and refine pharmacokinetic profiles. BCR modeling using antibody-specific language models and graph neural networks uncovers clonal topology and antigen-binding semantics for cancer, infectious disease, and autoimmunity applications.

Peptide Therapeutics

AI integration with peptide-based therapeutics design is described as a paradigm shift, with ML algorithms enabling prediction of structure-activity relationships, bioavailability, and drug-like properties. Deep generative models, reinforcement learning, and LLMs overcome historical limitations including structural flexibility and enzymatic degradation. Applications span oncology, metabolic disorders, and infectious diseases, with convergence of computational prediction and experimental automation promising near-term clinical translation.