The DBT-BIRAC Joint Call for Proposals on Bio AI for Fostering High Performance Biomanufacturing under the BioE3 Policy invites researchers, startups, and industry-academia consortia to co-create AI-driven biomanufacturing solutions. This initiative emphasizes interdisciplinary collaboration, state-of-the-art computation, and open, scalable platforms to accelerate health, agriculture, and environmental outcomes. Proposals must align with five identified focus categories and advance AI-enabled bioprocessing through the establishment of Bio AI Hubs, with a strong emphasis on collaboration, infrastructure, and shared facilities. The last date for submission is 30 June 2026, providing a clear window for planning, partnerships, and proposal development.

Overview: Objectives, Scope, and Funder Context

This joint call seeks to build data‑driven, high‑performance biomanufacturing ecosystems by integrating artificial intelligence, machine learning, and robust computational methods into bioprocess R&D. The program envisions the creation of Bio AI Hubs that bring together academia, industry, startups, and small and medium enterprises to foster cross-institutional collaboration. By establishing AI-guided, closed-loop platforms, the initiative aims to shorten development cycles, optimize manufacturing workflows, and enable rapid translation from discovery to scalable deployment. A shared DBTL (Design-Build-Test-Launch) facility approach, implemented in PPP mode, will enable scaling activities across research and industry settings, ultimately accelerating innovation while maintaining rigorous scientific and manufacturing standards.

BioAI Hubs and Collaboration Model: Structure, Roles, and Infrastructure

Core to this call is the development of Bio AI Hubs that catalyze interdisciplinary and interinstitutional collaboration. Hubs will be anchored by organizations with demonstrated expertise in AI/ML and equipped with robust infrastructure and advanced computational methodologies. Startups, SMEs, and other academic institutions can participate as spokes, contributing to the hub’s research and innovation ecosystem while leveraging hub capabilities. The hubs are envisioned to generate AI-guided closed‑loop platforms where predictions, experiments, and analyses operate within a positive feedback loop, continuously improving performance and accelerating scientific discoveries. These hubs will also serve as shared, state‑of‑the‑art DBTL facilities in PPP mode, enabling scaling-up of various research activities in both academia and industry settings, as well as joint industry–academia collaborations.

Participation in this model emphasizes collaboration, integration, and scalability. By connecting diverse players—academic researchers, industry developers, and startup innovators—the program aims to create an ecosystem that not only advances fundamental science but also translates findings into practical, manufacturable technologies. The collaboration framework encourages data sharing, standardized methodologies, and interoperable platforms that can be leveraged across institutions and sectors. This approach is designed to reduce duplication, accelerate validation, and foster a culture of iterative, evidence-based improvement in biomanufacturing processes driven by AI insights.

Focus Areas under Bio AI: Categories and Subtopics

The call invites proposals under five categories, each focusing on distinct AI-enabled biomanufacturing applications. Proposals may address one or more categories, with scope defined to enable deep, cutting-edge work while ensuring cross‑hub collaboration and shared infrastructure. The following subtopics provide a structured lens for applicants to map their research plans to the program’s goals.

Biomolecular Design

• Engineering novel proteins, enzymes, and RNA-based molecules for advanced biomedical and biotechnological applications.
• Designing next-generation protein and RNA-based antibodies for targeted therapeutics and immune modulation.
• Developing advanced enzyme inhibitors, RNA aptamers, and de novo enzyme designs for precision disease intervention and metabolic engineering.
• Constructing bio-nanomachines and catalytic biomolecules with programmable molecular functions for diagnostics, therapeutics, and synthetic biology applications.

In this category, proposals should articulate how AI/ML approaches will support design decisions, predictive modeling of molecular function, and the optimization of biocatalysts and biomolecules for scalable production. Emphasis will be on integrating computational design with experimental validation to shorten lead times and improve success rates in developing novel biomolecules for health and industrial biotechnology.

Sustainable Agriculture

• Engineering drought-resistant cereal crops for enhanced climate resilience.
• Developing AI-powered, non-invasive sensing technologies (e.g., drone imaging, infrared spectroscopy) for early disease detection.
• Predictive modeling of milk production and health parameters in dairy cattle using AI and genomics.

This focus area targets AI-enabled solutions that strengthen agricultural productivity, resilience, and sustainability. Applications may include crop design and trait optimization through AI-guided interpretation of genomic and phenotypic data, remote sensing for crop health surveillance, and data-driven animal health and productivity models that inform management strategies in farming systems. Collaborative work across agrigenomics, remote sensing, and data science will be encouraged to deliver deployable tools for farming communities and agribusiness.

Synthetic Biology

• AI-optimized metabolic pathways for biofuel and biomaterial production.
• Predicting enzyme functions for more efficient bioprocessing.
• Improve strain engineering for microbial factories.
• Synthetic/engineered organisms as environmental biosensors.

Under Synthetic Biology, emphasis lies on leveraging AI to streamline pathway design, optimize host metabolism, and enhance production yields in a safe, scalable manner. Proposals should describe how AI-driven modeling, simulations, and data analytics will guide the construction and optimization of microbial systems, as well as strategies for robust, responsible deployment of engineered organisms in biomanufacturing contexts. Collaborative projects may integrate computational design, high‑throughput experimentation, and process-scale validation to demonstrate tangible improvements in bioproduction pipelines.

Ayurveda

• AI-driven Ayurvedic Pharmacogenomics: Developing predictive models to personalize Ayurvedic treatments based on genetic, metabolic, and microbiome profiles.
• AI-enabled Herb-Drug Interaction Analysis: Identifying potential interactions between Ayurvedic formulations and modern pharmaceuticals using AI and molecular modelling.
• Standardization & Authentication of Ayurvedic Formulations: Leveraging AI and analytical chemistry to validate and authenticate medicinal plant extracts.
• AI-assisted Plant-Based Drug Discovery: Mining Ayurvedic texts and databases to identify bioactive compounds with therapeutic potential.

Ayurveda-focused efforts should articulate how AI can illuminate personalized treatment paradigms, safety considerations, and quality assurance of plant-based formulations. Proposals may combine pharmacogenomic data, metabolomics, and microbiome insights to optimize traditional remedies for contemporary clinical and wellness applications, while ensuring rigorous standardization and reproducibility for broader adoption.

Implementation Context: Infrastructure, Collaboration, and Impact

Implementation envisions a robust, networked ecosystem where AI/ML capabilities are embedded within biomanufacturing R&D. Bio AI Hubs will not only generate innovative science but also deliver practical platforms and tools that can be adopted across institutions and industries. The PPP-enabled DBTL facility model emphasizes shared access to high-end infrastructure, data platforms, and translational capabilities, enabling smooth transition from discovery to deployment. By fostering cross-sector partnerships, the program aims to de-risk early-stage technologies, accelerate product development timelines, and create a pipeline for scalable biomanufacturing solutions that address health, agriculture, and environmental challenges.

Beyond research advances, expected impacts include strengthened national capabilities in bioinformatics, AI for life sciences, and bioprocess optimization; enhanced collaboration between academia and industry; and the creation of an ecosystem that supports startups and SMEs in bringing AI-enhanced biomanufacturing technologies to market. The initiative also anticipates contributing to global competitiveness in biotechnologies, with potential spillovers to diagnostics, therapeutics, agriculture, and sustainable manufacturing practices. Clear governance, data-sharing norms, and evaluation metrics will underpin the program to ensure scientific rigor, ethical standards, and responsible innovation throughout the lifecycle of funded projects.

Dates, Eligibility Cues, and Engagement Flow

Key administrative detail includes the last date for proposal submissions, which is 30 June 2026. While specific eligibility criteria and application mechanics are not enumerated in the provided text, the program is designed to accommodate multi-stakeholder consortia spanning academia, industry, and startups/SMEs, unified by their capacity to contribute AI/ML expertise, biomanufacturing know-how, and robust infrastructural capabilities. Prospective applicants should prepare joint proposals that demonstrate interdisciplinary collaboration, clear translational potential, and a plan for implementing AI-guided biomanufacturing workflows within a shared hub environment. Engagement flow envisions the formation of hub-led consortia that assemble required expertise, align with the BioE3 policy objectives, and submit proposals under the five focus categories for consideration by the funders.

• Last Date for Submissions: 30 June 2026
• Funder: Department of Biotechnology (DBT) and Biotechnology Industry Research Assistance Council (BIRAC), India
• Program Aim: Fostering high-performance biomanufacturing through Bio AI hubs and AI-guided, closed-loop platforms
• Collaboration Model: Interdisciplinary hubs anchored by AI/ML experts, with spokes from startups/SMEs and academic institutions; PPP DBTL facility for scaling research
• Focus Categories: Biomolecular Design; Sustainable Agriculture; Synthetic Biology; Ayurveda

Applicants are encouraged to design proposals that leverage AI/ML to optimize biomanufacturing pipelines, from molecule design to process scale-up, while ensuring robust validation, reproducibility, and safety in translational contexts. The funding framework emphasizes shared infrastructure, cross-institutional learning, and the creation of scalable platforms accessible to a broad set of stakeholders. By articulating clear milestones, measurable impact, and an achievable path to deployment, consortia can position themselves to contribute to India’s BioE3 policy objectives and to global leadership in bio-enabled manufacturing technologies.