Quantum Computing Applications in Biology & Drug Discovery
Quantum-Powered Biology—Redefining the Future of Drug Discovery
About This Course
Biological systems and drug discovery involve highly complex molecular interactions that are often computationally expensive to simulate using classical computers. Challenges such as accurate protein folding prediction, quantum-level chemical reaction modeling, and large-scale compound screening demand new computational paradigms. Quantum computing offers a transformative approach by leveraging quantum mechanics to process information in fundamentally new ways, enabling faster simulation of molecular systems and improved optimization strategies.
This workshop explores how quantum computing can be applied to molecular biology, structural bioinformatics, and pharmaceutical drug discovery pipelines. Participants will learn about quantum algorithms such as VQE (Variational Quantum Eigensolver), QAOA (Quantum Approximate Optimization Algorithm), and quantum machine learning models relevant to biomolecular problems. The program emphasizes dry-lab conceptual workflows, real-world case studies, and the future potential of quantum-enhanced drug discovery.
Aim
This workshop aims to introduce participants to the emerging role of quantum computing in solving complex problems in biology, chemistry, and drug discovery. It focuses on how quantum algorithms can accelerate molecular simulations, protein–ligand interaction modeling, and optimization tasks in pharmaceutical R&D. Participants will gain an understanding of quantum principles and their practical relevance to life sciences. The program bridges quantum technology with next-generation computational biology.
Workshop Objectives
- Understand quantum computing fundamentals relevant to molecular biology.
- Explore quantum algorithms for molecular simulation and drug design.
- Learn how quantum optimization accelerates compound screening workflows.
- Study quantum machine learning applications in biomolecular prediction.
- Discuss current limitations, opportunities, and future directions in quantum biology.
Workshop Structure
Day 1: Quantum Biology & Quantum Computing
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- Quantum coherence, tunnelling, entanglement
- Case studies: Photosynthetic energy transfer, Enzyme catalysis via proton tunnelling, Magnetoreception in avian systems
- Classical vs Quantum computing paradigms
- Qubits, superposition, entanglement
- Quantum gates and quantum circuits
- Overview of major platforms:
- IBM Quantum, Google Quantum AI, Rigetti Computing
- Complexity of protein folding and molecular interactions
- Computational bottlenecks in drug discovery
- Quantum advantage in computational chemistry
- Tools & Platforms: Python (NumPy, SciPy, Matplotlib)/ QiskitCirq/ PennyLane
🔹 Day 2: Quantum Algorithms in Drug Discovery & Molecular Modeling
- Quantum Algorithms for Chemistry
- Variational Quantum Eigensolver (VQE)
- Quantum Phase Estimation (QPE)
- Hybrid quantum–classical models
- Simulation of small molecular Hamiltonians
- Protein–ligand interaction modeling
- Quantum computational chemistry in lead optimization
- Quantum machine learning in pharmaceutical research
- Running a simple molecular Hamiltonian example
- Interpreting quantum simulation outputs
- Tools & Platforms: Qiskit Nature, OpenFermion, PySCF, RDKit, AutoDock (conceptual comparison with quantum workflows)
🔹 Day 3: Quantum AI, Omics Integration & Future Prospects
- Quantum machine learning for omics and precision medicine
- Hybrid AI–quantum pipelines in drug discovery
- Quantum-enhanced systems biology modeling
- Quantum acceleration for single-cell and high-dimensional biomedical data
- Research directions, industry opportunities, and global policy trends
- Ethical, regulatory, and long-term outlook of quantum biomedical innovation
Tools & Platforms: PennyLane (QML), TensorFlow Quantum, Scikit-learn (hybrid workflows), R (Bioconductor packages), Omics data integration pipelines, Cloud-based quantum simulators
Who Should Enrol?
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Important Dates
Registration Ends
02/18/2026
IST 7:00 PM
Workshop Dates
02/18/2026 – 02/20/2026
IST 8:00 PM
Workshop Outcomes
Participants will be able to:
- Explain the relevance of quantum computing in biological and chemical problems.
- Understand quantum algorithms used in molecular simulation and optimization.
- Identify potential applications in protein modeling and drug discovery pipelines.
- Explore quantum ML approaches for biomolecular prediction tasks.
- Gain awareness of future career pathways in quantum-enabled biotech innovation.
Fee Structure
Student Fee
₹1999 | $70
Ph.D. Scholar / Researcher Fee
₹2999 | $80
Academician / Faculty Fee
₹3999 | $95
Industry Professional Fee
₹4999 | $110
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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