Mohammad Mostafa Pourseif
1,2,3,4* 
, Seyed Ali Baradaran Hosseini
1,5, Seyed Hossein Khoshraftar
1,6, Yadollah Omidi
7 1 Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
2 Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
3 Engineered Biomaterial Research Center (EBRC), Khazar University, Baku, Azerbaijan
4 Health Science and Technology Park, Tabriz University of Medical Sciences, Tabriz, Iran
5 Department of Medicinal Chemistry, School of Pharmacy, Urmia University of Medical Sciences, Urmia, Iran
6 Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
7 Department of Pharmaceutical Sciences, Barry and Judy Silverman College of Pharmacy, Nova Southeastern University, Fort Lauderdale, FL, 33328, USA
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.