Applying quantum computing in life sciences As Quantum Computing matures in both Qubit quality and quantum volume, pharma companies are exploring the application of quantum computers and software in chemistry and biology. Understanding the structure of proteins and how they fold is crucial to treat diseases such as Alzheimer’s, Huntington’s, Parkinson’s, Cancer, and even COVID. Alphafold, with its deep-learning-based algorithm, is bound by several approximations of the neural network and takes a huge amount of computing power and time. These are instances where classical computers or supercomputers are not powerful enough to make progress. Only Quantum Computing systems, with its ability to process huge amounts of information and data within a short time, can be the way forward.
Frameworks and Applications: We have designed frameworks focused on the study of chemistry — the structure of molecules — and life sciences. Fractal-orchestrated Application Layers dwell on discovering and simulating drug-target interactions, chemical interactions, protein folding, protein interactions, and compound generation. Being co-related, the layers will eventually branch out into several applications performing chemical and biological simulations. Algorithms: We have identified algorithms applicable to protein folding and chemical interaction issues. Efforts are underway to develop different combinations and better variations of the algorithms with an aim to predict workable molecules and proteins. Libraries & Frameworks: Leveraging reputed Machine Learning libraries like Pennylane, Qiskit, our Quantum AI team is developing full-stack application-focused frameworks. Fractal has access to matured life sciences and open-source chemistry frameworks by Google, Rosetta Commons. This enables the open-source frameworks to interface with libraries for simulations and chemical approximations. Hardware Stack: Our hardware stack accessible via Cloud has two models - Adiabatic Model, which is restricted and has hard-to-predict behavior at scale; and the Gate Based Circuit Model, which has predictable behavior at scale. Fractal is more inclined to Gate Model as it is closer to a Universal Quantum computer and gives the flexibility to control each qubit. Fractal efforts in other areas of quantum computing Our Fractal Quantum AI team is studying Generative Quantum machine learning for various problems and applications in quantum chemistry and finance, and the study of many body systems. Demonstrating quantum advantage over the classical algorithm, simulated molecular interaction was found between a subset of HIV and a hypothetical antiretroviral drug. You can find our entire work under Quantum Computing in HealthCare — Protein Folding Part-1 and Quantum Computing in HealthCare — Protein Folding Part-2 . Also underway, is the protein fold prediction with simulated annealing & D-Wave’s Adiabatic Quantum annealer.
Quantum computing is expected to provide higher level of clarity regarding the workings of biological processes, leading to accurate, fast-tracked cures for diseases.
Fractal contribution toward life sciences
At Fractal, we believe quantum computing can disrupt the life sciences industry through its increasingly accurate protein folding predictions, and breakthrough drug discoveries. Toward that, we have designed frameworks and applications, identified algorithms, and are developing libraries and frameworks.
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