SubsidieMeestersBioSim M2M: Molecules to Medicine
BioSimulytics' BioSim M2M technology accelerates pharmaceutical R&D by predicting stable crystal structures and binding poses, reducing analysis time from 3 months to 3 weeks.
Projectdetails
Introduction
BioSimulytics has developed breakthrough technology combining quantum physics, computational chemistry, machine learning, and high-performance computing to boost the success rates of pharmaceutical R&D.
Technology Overview
The BioSimulytics invention (patent EP3948877A1) is being applied to crystal structure prediction (CSP) for determining the most stable crystal structure or polymorph of a drug compound, as well as the most stable binding poses in protein-ligand complexes.
Current Challenges
Existing state-of-the-art techniques for polymorph analysis require long and painstaking experimentation by material scientists with uncertain results, achieving less than 1% success rates.
Benefits of BioSim M2M
BioSim M2M will:
- Reduce the time to find the most stable crystal structures of a molecule from 3 months to 3 weeks.
- Decrease candidate experiments from 400 to 40.
As a result, BioSim M2M will contribute to the Pharmaceutical Strategy for Europe by helping to ensure that patients have access to high quality, effective, and safe medicines.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 2.499.525 |
| Totale projectbegroting | € 3.570.750 |
Tijdlijn
| Startdatum | 1-9-2023 |
| Einddatum | 31-8-2025 |
| Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- BIOSIMULYTICS LIMITEDpenvoerder
Land(en)
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Vergelijkbare projecten uit andere regelingen
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|---|---|---|---|---|
Implementation of new machine learning algorithms for the optimisation of drug formulationsMACHINE-DRUG aims to leverage machine learning to accelerate the prediction of crystalline forms in pharmaceuticals, enhancing drug efficacy and stability while exploring broader industrial applications. | ERC Proof of... | € 150.000 | 2023 | Details |
Turning gold standard quantum chemistry into a routine simulation tool: predictive properties for large molecular systemsThis project aims to develop advanced quantum simulation methods for large molecules, enhancing predictive power and efficiency to study complex biochemical interactions and reactions. | ERC Starting... | € 1.175.215 | 2023 | Details |
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Implementation of new machine learning algorithms for the optimisation of drug formulations
MACHINE-DRUG aims to leverage machine learning to accelerate the prediction of crystalline forms in pharmaceuticals, enhancing drug efficacy and stability while exploring broader industrial applications.
Turning gold standard quantum chemistry into a routine simulation tool: predictive properties for large molecular systems
This project aims to develop advanced quantum simulation methods for large molecules, enhancing predictive power and efficiency to study complex biochemical interactions and reactions.
Membrane Micro-Compartments
The project aims to develop a system for in situ structural analysis of membrane proteins to enhance drug interaction studies and facilitate their commercialization in the pharmaceutical industry.
A holistic approach to bridge the gap between microsecond computer simulations and millisecond biological events
This project aims to bridge μs computer simulations and ms biological processes by developing methods to analyze conformational transitions in V1Vo–ATPase, enhancing understanding of ATP-driven mechanisms.
Dynamic nanocluster – biomolecule interfaces
DYNANOINT aims to develop multiscale simulation strategies using graph theory and machine learning to enhance the understanding of metal nanoclusters for applications in bioimaging and nanomedicine.