Periodic Reporting for period 2 - KvasirAccelerator (The first utility-oriented, agnostic software platform for quantum-centric drug discovery)
Okres sprawozdawczy: 2024-08-01 do 2025-07-31
Pharma companies are striving to meet the ever-increasing complexity of drug R&D, while not wasting money upfront. On average it costs in the vicinity of €2.5bn to develop a new drug, with 42-50% of the costs spent on drug discovery/preclinical development. Currently, trial success rates range as low as 10-12%, and the industry is faced with timelines of 10-15 years/successful drug developed. As quantum computing (QC) is getting closer to commercial utility, chemistry and pharma are seen as two of the most promising and disruptive areas of application.
At the moment, computational problems are holding back pharma R&D due to inaccurate simulations of intricate molecular/biochemical systems using even the world’s largest supercomputers, and too much complexity for targeted users to comprehend or extract knowledge.
Kvantify aims to develop and deploy the first user-friendly software platform combining QC and high-performance computing (HPC). The goal is to reduce wet-lab and animal testing, ultimately reducing time to market for new drugs. Our platform will provide the chemistry and pharma (including biotech) industry with a versatile, high-performing, and cost-efficient software capable of harnessing the power of QC and HPC to address complex computational problems with unprecedented speed, accuracy, and explainability. Our solution is based on quantum mechanics and classical mechanics methods to simulate physical systems to boost the interpretability, efficiency, and speed in chemical and pharmaceutical R&D processes.
Our goal is to develop a problem- and customer-centric quantum-hardware-agnostic software tool. Such a solution will directly address the needs of customers who currently rely on unintuitive, slow, and laborious simulation routines, which require extensive knowledge and computational expertise, often generating inaccurate results that are not translatable to the wet lab and, thus, leading to increased failure rates and spending by pharma players during drug development.
At the moment, computational problems are holding back pharma R&D due to inaccurate simulations of intricate molecular/biochemical systems using even the world’s largest supercomputers, and too much complexity for targeted users to comprehend or extract knowledge.
Kvantify aims to develop and deploy the first user-friendly software platform combining QC and high-performance computing (HPC). The goal is to reduce wet-lab and animal testing, ultimately reducing time to market for new drugs. Our platform will provide the chemistry and pharma (including biotech) industry with a versatile, high-performing, and cost-efficient software capable of harnessing the power of QC and HPC to address complex computational problems with unprecedented speed, accuracy, and explainability. Our solution is based on quantum mechanics and classical mechanics methods to simulate physical systems to boost the interpretability, efficiency, and speed in chemical and pharmaceutical R&D processes.
Our goal is to develop a problem- and customer-centric quantum-hardware-agnostic software tool. Such a solution will directly address the needs of customers who currently rely on unintuitive, slow, and laborious simulation routines, which require extensive knowledge and computational expertise, often generating inaccurate results that are not translatable to the wet lab and, thus, leading to increased failure rates and spending by pharma players during drug development.
By pioneering quantum-enhanced tools for drug discovery that are beyond state of the art, we have contributed directly towards advancing the field of Computer-Aided Drug Design (CADD). Our approach offers significantly faster, yet accurate, ranking of ligands, relative to competitive methods, thus holding the promise of fewer failed trials and less heavy reliance on wet-lab experiments. Our unbinding-kinetics algorithm constitutes a novel solution with a significant market potential. By demonstrating the value of in-silico methods in drug discovery, the outcomes of the KvasirAccelerator project are poised to accelerate the commercialization of effective therapeutics and simultaneously lower R&D expenses throughout the pharma industry, to the benefit of patients and the healthcare system at large.
Our KvantifyQDK (Quantum Development Kit) enables domain experts without quantum expertise to construct hybrid quantum-classical workflows easily and efficiently, leveraging real quantum hardware in the cloud. This significantly lowers the entry barrier to quantum computing within chemistry and, in the long term, unlocks quantum advantage for pharma and biotech companies without in-house quantum resources.
We launched the product Koffee Unbinding Kinetics in March 2024 and the product Koffee Binding Affinity in October 2024. We are planning to launch our KvantifyQDK in Q4 of 2025.
Our KvantifyQDK (Quantum Development Kit) enables domain experts without quantum expertise to construct hybrid quantum-classical workflows easily and efficiently, leveraging real quantum hardware in the cloud. This significantly lowers the entry barrier to quantum computing within chemistry and, in the long term, unlocks quantum advantage for pharma and biotech companies without in-house quantum resources.
We launched the product Koffee Unbinding Kinetics in March 2024 and the product Koffee Binding Affinity in October 2024. We are planning to launch our KvantifyQDK in Q4 of 2025.
We have developed the KvantifyQDK (Quantum Development Kit), which is designed to empower computational chemists and drug-discovery scientists. The KvantifyQDK provides a streamlined and intuitive interface to enable direct integration with classical-quantum chemistry methodologies through embedding techniques, facilitating realistic, hybrid quantum-classical workflows. It utilizes a generic interface for all quantum backends, making our customers’ applications fully platform-agnostic. Applications set up with KvantifyQDK can be run on any supported physical quantum computer or any of the bundled simulators, and it is trivial to switch between them. We are currently gathering feedback on the product, prior to its launch.
As part of our quantum efforts, we have developed the proprietary algorithm FAST-VQE. This is a novel and exceptionally adept algorithm for Noisy Intermediate-Scale Quantum (NISQ) devices, which delivers high-performing and cost-effective calculations of small molecules. We have included FAST-VQE in our framework, thus bringing the usage of quantum computing into the realm of realistic, industry-relevant applications.
Our classical computing efforts have focused on developing modules for extracting reaction-rate binding constants, and this has led to the development of our first product, Koffee Unbinding Kinetics. This innovative product ranks ligands based on residence time and allows users to calculate unbinding kinetics in minutes, thus complementing and reducing costly and time-consuming laboratory experiments. Unbinding kinetics is a critical parameter in the screening and selection of new drug candidates. The product adds value already today when used in a classical-computing framework, and our algorithm is designed to demonstrate quantum advantage as hardware capabilities evolve and mature toward the fault-tolerant era. Product trials are currently ongoing, and customer feedback is crucial for further refinement of the product’s applicability.
To guide potency optimization in drug discovery it is important to be able to measure how strongly a drug binds to its target. Existing, traditional methods either lack speed or precision, and in response to the urgent call for new methods in this space, we developed an algorithm for ranking ligands based on binding affinity. We launched this as our second product, Koffee Binding Affinity. Based on several customer trials, we have verified that our method performs on par with competitive solutions, and at the same time it is significantly faster and more user-friendly. We are currently refining the tool based on customer feedback and market insights.
As part of our quantum efforts, we have developed the proprietary algorithm FAST-VQE. This is a novel and exceptionally adept algorithm for Noisy Intermediate-Scale Quantum (NISQ) devices, which delivers high-performing and cost-effective calculations of small molecules. We have included FAST-VQE in our framework, thus bringing the usage of quantum computing into the realm of realistic, industry-relevant applications.
Our classical computing efforts have focused on developing modules for extracting reaction-rate binding constants, and this has led to the development of our first product, Koffee Unbinding Kinetics. This innovative product ranks ligands based on residence time and allows users to calculate unbinding kinetics in minutes, thus complementing and reducing costly and time-consuming laboratory experiments. Unbinding kinetics is a critical parameter in the screening and selection of new drug candidates. The product adds value already today when used in a classical-computing framework, and our algorithm is designed to demonstrate quantum advantage as hardware capabilities evolve and mature toward the fault-tolerant era. Product trials are currently ongoing, and customer feedback is crucial for further refinement of the product’s applicability.
To guide potency optimization in drug discovery it is important to be able to measure how strongly a drug binds to its target. Existing, traditional methods either lack speed or precision, and in response to the urgent call for new methods in this space, we developed an algorithm for ranking ligands based on binding affinity. We launched this as our second product, Koffee Binding Affinity. Based on several customer trials, we have verified that our method performs on par with competitive solutions, and at the same time it is significantly faster and more user-friendly. We are currently refining the tool based on customer feedback and market insights.