Summary
Computational design and discovery of molecules and materials relies on the exploration of increasingly growing chemical spaces. The discovery and formulation of new drugs, antivirals, antibiotics, catalysts, battery materials, and in general chemicals with tailored properties, require a fundamental paradigm shift to search in unchartered swaths of the vast chemical space. This is in stark contrast to current approaches, which start from (commercially available) libraries of compounds from various suppliers. Within the ERC Consolidator grant BeStMo (grant agreement ID 725291) we aimed to substantially advance our ability to model and understand the behaviour of molecules in complex environments. As a result, we successfully developed a set of machine learning and physics-based methods for covalent and non-covalent interactions that now allow an accurate and efficient modelling of molecules of increasing size (from 10 to 1000 atoms). These methods now enable routine calculations of quantum-mechanical properties of molecules throughout chemical compound space, provided that enough reference data is produced as a starting point for training. Within DISCOVERER, we aim to promote a paradigm shift in chemical discovery by inverting the selection pyramid by starting with pre-defined parameters from which new chemical entities are designed through machine learning and AI-enabled algorithms. We can do so by integrating these modules into a commercial platform: “Chemical Space Machine”. DISCOVERER’s main goal is to finalize the development of a commercial alpha version of “Chemical Space Machine” and setting up its commercialisation strategy.
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| Web resources: | https://cordis.europa.eu/project/id/966781 |
| Start date: | 01-09-2021 |
| End date: | 28-02-2023 |
| Total budget - Public funding: | - 150 000,00 Euro |
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Original description
Computational design and discovery of molecules and materials relies on the exploration of increasingly growing chemical spaces. The discovery and formulation of new drugs, antivirals, antibiotics, catalysts, battery materials, and in general chemicals with tailored properties, require a fundamental paradigm shift to search in unchartered swaths of the vast chemical space. This is in stark contrast to current approaches, which start from (commercially available) libraries of compounds from various suppliers. Within the ERC Consolidator grant BeStMo (grant agreement ID 725291) we aimed to substantially advance our ability to model and understand the behaviour of molecules in complex environments. As a result, we successfully developed a set of machine learning and physics-based methods for covalent and non-covalent interactions that now allow an accurate and efficient modelling of molecules of increasing size (from 10 to 1000 atoms). These methods now enable routine calculations of quantum-mechanical properties of molecules throughout chemical compound space, provided that enough reference data is produced as a starting point for training. Within DISCOVERER, we aim to promote a paradigm shift in chemical discovery by inverting the selection pyramid by starting with pre-defined parameters from which new chemical entities are designed through machine learning and AI-enabled algorithms. We can do so by integrating these modules into a commercial platform: “Chemical Space Machine”. DISCOVERER’s main goal is to finalize the development of a commercial alpha version of “Chemical Space Machine” and setting up its commercialisation strategy.Status
CLOSEDCall topic
ERC-2020-POCUpdate Date
27-04-2024
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