Artificial Intelligence (AI)-driven gene sequencing, undertaken by the Department of Biotechnology (DBT) in Govt of India, promises to power India’s transition toward personalised medical prescriptions and predictive medicine, Union Minister for Science & Technology Dr. Jitendra Singh said, asserting that some of the country’s most substantive AI applications are currently unfolding in genomics.
In the backdrop of the ongoing AI Impact Summit, the Minister emphasised that large-scale genome sequencing initiatives supported by DBT are already AI-enabled, and future medical prescriptions will increasingly be based on individual genetic profiles analysed through AI-facilitated platforms.
“Our gene sequencing work is AI-driven. Tomorrow, when we move toward personalised prescriptions, they will be based on our gene studies facilitated by Artificial Intelligence,” Dr. Jitendra Singh said, underlining that DBT’s genomics ecosystem is positioning India to move beyond conventional treatment models toward precision healthcare driven by data and computational biology.
Reinforcing this shift, Dr. Jitendra Singh announced that DBT, along with BIRAC, will establish “Bio-AI Mulankur” hubs in 2026 to create integrated, closed-loop research platforms where AI-based predictions, laboratory validation and data analytics operate in a unified framework. The hubs will focus on genomics diagnostics, biomolecular design, synthetic biology and Ayurveda-based research. He said the objective is to institutionalise AI as a core scientific engine within biotechnology rather than as a peripheral analytical tool, in alignment with the BioE3 policy aimed at strengthening high-performance biomanufacturing for economic growth, environmental sustainability and employment generation.
Citing ongoing applications, the Minister pointed to the Indian Tuberculosis Genomic Surveillance Consortium (InTGS), supported by DBT, where AI is being used to catalogue drug-resistance mutations in Mycobacterium tuberculosis. AI-enabled analysis of whole-genome sequencing data has reduced confirmation timelines for drug resistance from weeks to days, allowing faster clinical response and strengthening public health surveillance.
In maternal health research, the GARBH-Ini programme has deployed AI-driven ultrasound image analysis and genomics tools to identify 66 genetic markers linked to preterm birth risk. Dr. Jitendra Singh said such initiatives demonstrate how AI-supported genomics can enable early risk prediction and targeted intervention, forming part of a broader effort to develop AI-based risk models for cancer, diabetes and cardiovascular diseases.
The Minister highlighted that the National Genomics Core, established at the National Institute of Biomedical Genomics in Kalyani and the Centre for DNA Fingerprinting and Diagnostics in Hyderabad, provides high-throughput sequencing and big data analytics infrastructure critical for AI-led research. Data generated under the GenomeIndia project, which maps the country’s genetic diversity, is being analysed using AI and machine learning techniques to identify disease-associated variants and strengthen translational medicine.
Referring to research under the Centre of Excellence in Genome Sciences and Predictive Medicine, Dr. Jitendra Singh said scientists are applying computational prediction and AI-based structural analysis to identify potential drug targets for rheumatoid arthritis. AI applications are also being extended to single-cell and spatial genomics to profile tumour microenvironments, as well as to protein engineering and therapeutic molecule design.
The Minister said the thrust now is to transition AI in biotechnology from proof-of-concept research to scalable, industry-ready solutions through partnerships supported by BIRAC. Positioning the initiatives within India’s broader science and innovation framework, he said embedding AI across DBT’s genomics platforms will strengthen predictive healthcare, disease surveillance and advanced biomanufacturing capabilities, reinforcing India’s competitiveness in the global biotechnology landscape.
