Descrizione del progetto
Progettazione intelligente di prodotti chimici verdi
I prodotti chimici industriali si fanno strada negli ambienti acquatici durante la produzione, l’uso e lo smaltimento dei prodotti finali. Anche i cosiddetti prodotti chimici «verdi» possono danneggiare gli organismi viventi se si accumulano particelle microscopiche in ambienti naturali. Per l’uso sicuro delle sostanze chimiche esistenti e la progettazione intelligente di quelle future, è necessaria una chiara comprensione delle caratteristiche strutturali responsabili dell’eco-tossicità. Per questo scopo, alcuni scienziati finanziati dall’UE stanno utilizzando strumenti ben noti quali modelli di relazione quantitativa struttura-attività (QSAR, Quantitative Structure–Activity Relationship) integrati con altri sviluppati nell’ambito del progetto. In questo modo si aiuteranno le industrie a essere competitive, promuovendo al contempo ambienti sostenibili per la vita acquatica.
Obiettivo
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.
Campo scientifico
- natural sciencescomputer and information sciencesartificial intelligence
- natural sciencescomputer and information sciencessoftware
- medical and health sciencesbasic medicinepharmacology and pharmacypharmaceutical drugs
- natural sciencesbiological sciencesmicrobiology
- natural sciencesbiological scienceszoologyinvertebrate zoology
Programma(i)
Argomento(i)
Meccanismo di finanziamento
MSCA-IF-EF-SE - Society and Enterprise panelCoordinatore
46018 Valencia
Spagna
L’organizzazione si è definita una PMI (piccola e media impresa) al momento della firma dell’accordo di sovvenzione.