The role of bioinformatics algorithms in modern biopharmaceutical design: Progress, challenges, and future perspectives

Abstract

Bioinformatics algorithms empowered by artificial intelligence (AI), machine learning (ML), and deep learning (DL) are revolutionizing biopharmaceutical design and development. These methods accelerate discovery through rapid in silico prediction of protein structure, function, and immunogenicity, reducing experimental cost and time. Generative and hybrid frameworks, especially those combining AI with physics-informed neural networks (PINNs), enable interpretable, mechanism-aware modeling for enzyme kinetics and protein engineering. Multi-omics integration and graph-based network algorithms support systems-level understanding of therapeutic targets. Despite remarkable progress, challenges persist, including limited data for novel modalities, interpretability gaps, and computational scalability. Recent advances such as AlphaFold 3, OpenFold, and NeuralPlexer, alongside evolving FDA and EMA guidelines for AI-derived therapeutics, are helping bridge innovation and clinical translation. The future of drug discovery will rely on synergistic human–algorithm collaboration to ensure responsible, reproducible, and clinically relevant biopharmaceutical innovation.

Keywords: Predictive algorithms, AI-driven biopharma, Structural bioinformatics, In silico biopharmaceuticals, Machine learning, Drug design, Systems biology