Transmembrane transporters play a pivotal role in absorption, distribution, elimination and toxicity of drugs and drug candidates. Furthermore, in polypharmacy they are often responsible for drug-drug interactions. With our in silico transporter models we cover most of the transporters which are considered by the regulators as important in this context, thus allowing prioritisation of compounds by early assessment of safety risks.
Transmembrane Transport Proteins (TMTs) control nutrient uptake, ion transport, and drug transport across biological membranes. Predicting substrate and inhibition profiles of small molecules towards these transporters helps medicinal chemists to prioritize compounds in an early phase of the drug development process and guide toxicologists in the safety assessment of candidate compounds. Based on our long-lasting experience in the field of transporter informatics we offer a set of high-quality computational models for predicting inhibitor profiles of small molecules towards a set of TMTs.
We offer detailed ligand- and structure-based assessment of how your compounds interact with transporters. Our model portfolio is constantly updated and expanded. Currently we are offering models for 9 transporters which are involved in drug/drug interactions and hepatoxicity. These include prediction of inhibitors of P-glycoprotein (MDR1), BCRP, BSEP, MRP3, OATP1B1, OATP1B3, OCT1, OCT2, and MATE1.
- provide predictions for substrate and inhibitor properties
- customise models using your in-house data sets
- develop models for transporter of interest
- perform large scale predictions and provide reports
- combine models with structure-based design
TRANSPORTER MODELS DEMO (video)
To give you a closer insight into the functionalities and design of our Transporter Models please watch our short demo video. See how you can easily draw and analyse your compounds and get results in a standardised and aggregated form with state-of-the-art data visualisation in just a few minutes:
Montanari F, Knasmüller B, Kohlbacher S, Hillisch C, Baierova C, Grandits M, Ecker GF (2020). Vienna LiverTox Workspace – A set of machine learning models for prediction of interaction profiles of small molecules with transporters relevant for regulatory agencies. Front Chem, January 2020, Vol 7, Article 899; https://doi.org/10.3389/fchem.2019.00899