Artificial Intelligence in Biotechnology: Current Applications, Challenges, and Future Perspectives
Vasudevan Ranganathan *
Department of Biotechnology, Aurora’s Degree & PG College, Telangana, India.
Ayesha Rahmath
Department of Biotechnology, Aurora’s Degree & PG College, Telangana, India.
N. Manasa
Department of Biotechnology, Aurora’s Degree & PG College, Telangana, India.
N. Deepika
Department of Biotechnology, Aurora’s Degree & PG College, Telangana, India.
*Author to whom correspondence should be addressed.
Abstract
Artificial intelligence (AI) is emerging as a transformative technology in biotechnology, offering innovative approaches to biological research, healthcare, pharmaceutical development, and industrial applications. The integration of AI techniques, including machine learning, deep learning, natural language processing, and generative AI, enables the rapid analysis of complex biological data and supports efficient decision-making. In biotechnology, AI has demonstrated significant potential in drug discovery and development, protein structure prediction, genomic analysis, disease diagnosis, personalised medicine, genetic engineering, and microbial biotechnology. AI-driven computational tools facilitate the identification of potential drug targets, prediction of protein functions, analysis of gene expression patterns, and development of novel therapeutic molecules. Furthermore, AI contributes to agricultural biotechnology, industrial bioprocess optimisation, enzyme engineering, and the development of sustainable biotechnological products. Despite these advances, several challenges limit the widespread implementation of AI in biotechnology. These include the availability and quality of biological datasets, data privacy concerns, algorithmic bias, limited model interpretability, high computational requirements, and the need for experimental validation of computational predictions. In addition, ethical considerations, regulatory uncertainties, and the lack of interdisciplinary expertise present significant barriers to practical applications. Future developments are expected to focus on integrating AI with multi-omics technologies, automation, high-throughput screening, synthetic biology, and laboratory robotics. Generative AI and advanced predictive models may further accelerate biological discovery and enable the design of proteins, enzymes, and therapeutics with improved properties. Responsible AI development, transparent algorithms, standardised datasets, and collaboration between computational scientists and experimental biologists will be essential for translating AI-driven predictions into reliable biological applications. This mini-review highlights the current applications of AI in biotechnology, examines the major technical and ethical challenges, and discusses future perspectives for advancing research, innovation, and sustainable biotechnological development.
Keywords: Artificial intelligence, biotechnology, machine learning, deep learning, genomics, bioinformatics, drug discovery, protein engineering, synthetic biology, genetic engineering, multi-omics integration, bioprocess optimisation