Summary
The main goal of the proposed research project is the computational evaluation of eco-toxicity (diverse endpoints) of various chemicals that are vastly utilized and produced by the pharmaceutical and cosmetic industries, such as green solvents (including future ones, i.e., ionic liquids and deep eutectic solvents) and active pharmaceutical ingredients (API).
We will be majorly focusing on toxicity in aquatic environment, where the toxicity data will cover four trophic levels of aquatic organisms, i.e., fish (vertebrates), invertebrates such as daphnids, algae (aquatic plants), and microorganisms. The toxicity related properties that will be studied include acute and chronic toxicity, biodegradation and bioaccumulation.
The research methodology to perform toxicity assessment and for understanding the structural features responsible for the eco-toxicity, will involve diverse Artificial Intelligence (AI) and chemoinformatics techniques like Quantitative Structure-Activity Relationship (QSAR), interspecies QSAR (QAAR), toxicophore mapping, virtual screening, similarity search, clustering techniques, multimedia mass-balance (MM) modeling (to understand the distribution profile of chemicals in different environmental compartments), matched molecular pair (MMPs) analysis etc.
The knowledge gained from the study will help in classifying existing chemicals into toxic and non-toxic groups and will also help in designing novel analogues of selected chemical that will show better desirable physicochemical properties with less or no eco-toxicity. This project will also include development of AI software tools and scheming KNIME workflows for various computational tasks.
We will be majorly focusing on toxicity in aquatic environment, where the toxicity data will cover four trophic levels of aquatic organisms, i.e., fish (vertebrates), invertebrates such as daphnids, algae (aquatic plants), and microorganisms. The toxicity related properties that will be studied include acute and chronic toxicity, biodegradation and bioaccumulation.
The research methodology to perform toxicity assessment and for understanding the structural features responsible for the eco-toxicity, will involve diverse Artificial Intelligence (AI) and chemoinformatics techniques like Quantitative Structure-Activity Relationship (QSAR), interspecies QSAR (QAAR), toxicophore mapping, virtual screening, similarity search, clustering techniques, multimedia mass-balance (MM) modeling (to understand the distribution profile of chemicals in different environmental compartments), matched molecular pair (MMPs) analysis etc.
The knowledge gained from the study will help in classifying existing chemicals into toxic and non-toxic groups and will also help in designing novel analogues of selected chemical that will show better desirable physicochemical properties with less or no eco-toxicity. This project will also include development of AI software tools and scheming KNIME workflows for various computational tasks.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/845373 |
| Start date: | 16-09-2019 |
| End date: | 15-09-2021 |
| Total budget - Public funding: | 172 932,48 Euro - 172 932,00 Euro |
Cordis data
Original description
The main goal of the proposed research project is the computational evaluation of eco-toxicity (diverse endpoints) of various chemicals that are vastly utilized and produced by the pharmaceutical and cosmetic industries, such as green solvents (including future ones, i.e., ionic liquids and deep eutectic solvents) and active pharmaceutical ingredients (API).We will be majorly focusing on toxicity in aquatic environment, where the toxicity data will cover four trophic levels of aquatic organisms, i.e., fish (vertebrates), invertebrates such as daphnids, algae (aquatic plants), and microorganisms. The toxicity related properties that will be studied include acute and chronic toxicity, biodegradation and bioaccumulation.
The research methodology to perform toxicity assessment and for understanding the structural features responsible for the eco-toxicity, will involve diverse Artificial Intelligence (AI) and chemoinformatics techniques like Quantitative Structure-Activity Relationship (QSAR), interspecies QSAR (QAAR), toxicophore mapping, virtual screening, similarity search, clustering techniques, multimedia mass-balance (MM) modeling (to understand the distribution profile of chemicals in different environmental compartments), matched molecular pair (MMPs) analysis etc.
The knowledge gained from the study will help in classifying existing chemicals into toxic and non-toxic groups and will also help in designing novel analogues of selected chemical that will show better desirable physicochemical properties with less or no eco-toxicity. This project will also include development of AI software tools and scheming KNIME workflows for various computational tasks.
Status
CLOSEDCall topic
MSCA-IF-2018Update Date
28-04-2024
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