MolPredict
Neural-based solution to boost drug preclinical research success
Total Cost €
0EC-Contrib. €
0Partnership
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0MolPredict project word cloud
Explore the words cloud of the MolPredict project. It provides you a very rough idea of what is the project "MolPredict" about.
Project "MolPredict" data sheet
The following table provides information about the project.
| Coordinator |
PHARMACELERA SL
Organization address contact info |
| Coordinator Country | Spain [ES] |
| Project website | https://www.pharmacelera.com/uncategorized/pharmacelera-participates-in-the-sme-instrument-phase-i-program/ |
| Total cost | 71˙429 € |
| EC max contribution | 50˙000 € (70%) |
| Programme |
1. H2020-EU.3. (PRIORITY 'Societal challenges) 2. H2020-EU.2.3. (INDUSTRIAL LEADERSHIP - Innovation In SMEs) 3. H2020-EU.2.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies) |
| Code Call | H2020-SMEInst-2018-2020-1 |
| Funding Scheme | SME-1 |
| Starting year | 2018 |
| Duration (year-month-day) | from 2018-06-01 to 2018-11-30 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | PHARMACELERA SL | ES (SAINT CUGAT DEL VALLES BARCELONA) | coordinator | 50˙000.00 |
Map
Project objective
The competitiveness of the R&D process in this industry has been steadily declining during the past two decades. Whereas in 2000 the average cost for developing a new drug was close to €1 billion, in 2015 the estimated cost was €2.58 billion, a 150% increase. Consequently, the different actors of the pharmaceutical R&D industry have an urgent need to improve their effectiveness to develop new drugs. In this sense, the early stage of the R&D process shows the greatest potential to increase the success rate of new drug candidates, as it is in this phase where candidate molecules are selected for further testing in humans. Computer Aided Drug Design (CADD) is the most cost-effective tool for drug discovery. These technologies allow researchers to screen large databases of molecules and simulate its interaction in vivo. However, they are extremely intensive in computational resources. As a result, existing solutions have to implement simplifications in the simulations in order to incur acceptable computational times. These simplifications come at the cost of accuracy, finding a low rate of bioactive molecules that, in addition, usually fail during clinical trials. Pharmacelera aims at creating a new computing standard for Computer-Aided Drug Design (CADD) through an open machine learning platform that enables accurate prediction of molecular properties of candidate molecules based on quantum-mechanics (QM) calculations during drug development. By providing early stage ADME-Tox analysis, thus dramatically reducing the failure rate of the drug design process. In addition, by combining HPC, new disruptive molecular modelling techniques (computational chemistry) and machine learning, PharmAgile will be 20x faster and 10x more precise than any existing solution. Consequently, PharmAgile will be able to reduce the overall costs of drug development by up to 19% and the drug development time up to 8%. This translates into $335 million and 2 years saved per new approved drug.
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The information about "MOLPREDICT" are provided by the European Opendata Portal: CORDIS opendata.