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Project Genetics, Drug Discovery & Quantum

Accelerate drug-discovery with Quantum Simulations

Context

The process of drug discovery involves identifying potential drug candidates that can bind to a specific target protein, usually an enzyme or a receptor. The binding of a drug candidate to the target protein can be modeled using molecular docking simulations. These simulations require a significant amount of computational resources and time, which can slow down the drug discovery process.

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UseCase

Quantum computing can be employed to accelerate molecular docking simulations through algorithms that can solve complex problems more efficiently than classical computing methods. By using quantum computing, the time required to identify potential drug candidates can be drastically reduced.

Proposed Implementation Method

To implement this use case, a quantum computing algorithm known as the Variational Quantum Eigensolver (VQE) can be employed. VQE can estimate the lowest energy state of a molecule, which is crucial for understanding molecular interactions and drug binding.

Obtain the protein structure:

Acquire the 3D structure of the target protein from a database like the Protein Data Bank (PDB).

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Prepare the protein-ligand complex:

Prepare the target protein and potential drug candidates (ligands) for the docking simulation. This involves adding hydrogens, assigning proper atom types, and optimizing their geometry.

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Quantum chemistry calculations:

Perform quantum chemistry calculations to estimate the potential energy surface of the protein-ligand complex. This involves calculating the molecular orbitals, electron densities, and molecular energies.

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Convert the problem into a quantum problem:

Map the molecular docking problem onto a quantum computing problem by expressing it as a Hamiltonian, a mathematical representation of the total energy of the system.

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Run VQE algorithm:

Utilize the VQE algorithm on a quantum computer to estimate the ground state energy of the Hamiltonian, which represents the optimal binding energy between the protein and the ligand.

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Analyze the results:

Evaluate the binding energy and the predicted binding pose to assess the efficacy of the drug candidate. The lower the binding energy, the higher the likelihood of the ligand binding to the target protein effectively.

Whats in it for Quantum researchers?

Collaboration and Networking:

Connect with professionals, foster collaborations, share knowledge, and access job opportunities in the quantum computing field.

Skill Development and Knowledge Exchange:

Access resources, workshops, and mentorship to enhance quantum skills and stay current with cutting-edge advancements.

Project Opportunities and Funding:

Collaborate on projects, gain practical experience, and access financial support for research within the quantum computing community.

What's in it for bio/medical professionals?

Interdisciplinary collaboration

Joining the project enables collaboration with quantum computing experts, fostering innovative solutions that combine domain-specific knowledge with advanced computational techniques.

Enhanced research impact:

Contribute to groundbreaking advancements in drug discovery and precision medicine, potentially benefiting patient outcomes and revolutionizing healthcare.

Professional Growth:

Expand knowledge of quantum computing applications in biomedicine, bioinformatics, and genomics, positioning oneself at the forefront of emerging interdisciplinary technologies.

Join this Project Team

Got what it takes ? Apply to the right rol below!

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Giridhar

Business Lead

Co-founder - GrowthOnDemand.Club & Community Operations Lead - qDAO

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Dr ShibiChakravarthy Kannan

Advisor - Industry

Physician Scientist with a medical degree from India and PhD in Biochemistry & Molecular Biolog

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Kumar Sankaran

Advisor - Industry

Scientist & Co-founder - Microbiome

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Dr A Deevan Paul

Advisor - Industry

Nanomedical Researcher in drug development

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Mentor (Quantum Simulations)

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Technical Lead

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Researcher (Quantum Simulations) - 1

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Researcher (Quantum Simulations) - 2

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To: Investors

Why should you invest in this Project?

Flexibility and innovation:

Investing in a decentralized project enables support for a diverse set of ideas and approaches, potentially leading to breakthroughs that traditional companies might overlook.

Lower overhead costs:

The project's decentralized structure often translates to reduced overhead and operational costs, allowing for a more efficient allocation of funds toward research and development.

Early access to disruptive technologies:

Investing in the project offers the opportunity to be part of cutting-edge advancements in quantum computing applications, potentially yielding significant returns as these technologies reshape industries.

To: Clients

Why should you collaborate with this decentralised community?

Access to diverse expertise:

Investing in the project allows access to a wide range of experts with interdisciplinary skills, offering unique perspectives and innovative solutions that may not be available in-house.

Cost-effectiveness:

By leveraging the DAO's collaborative structure, clients can tap into a shared pool of resources and expertise, potentially reducing costs compared to hiring full-time developers.

Agility and adaptability:

The project's decentralized nature enables rapid response to new developments and trends, ensuring that pharmaceutical research companies stay at the cutting edge of technology and innovation.

Invest in the Project

Schedule a 30 min call with our Business Team

Contact Us

Potential Long Term Impact

By leveraging quantum computing algorithms like VQE, drug discovery researchers can perform molecular docking simulations more efficiently, saving time and resources. This accelerated process can potentially lead to faster identification of promising drug candidates and expedite the development of new treatments for various diseases.

Accelerate Drug Discovery:

Quantum optimization has the potential to greatly accelerate the drug discovery process, leading to the development of more effective cancer treatments.

New Potential Drug Candidates:

The successful completion of this research project would lead to the discovery of new potential drug candidates for cancer.

Effective Treatments:

With the use of quantum optimization, new treatments for cancer and other life-threatening diseases could be developed, leading to improved patient outcomes.

Reference Research Papers

Market Research

Project Roadmap

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Team Interviews & Finalisation

August 1

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Use Case Research

Sep 1

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Strategy Document & Budgeting

Oct 1st

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Funding Closure

Nov 15

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Project development and kickstart

Nov 1