Chair of molecular technology

Qsardb

QsarDB is a digital and smart repository for QSAR models and data. QSAR models, or quantitative structure-activity relationships, are created using machine learning (ML) and artificial intelligence (AI) methods. QsarDB applies FAIR (Findable, Accessible, Interoperable, Re-usable) principles to ensure free access and long-term data preservation. QsarDB covers various application and research areas of the QSAR community in chemical science, medical and health sciences, biological science, environmental science, agricultural and veterinary science, and material science.

Projects

The researchers of the chair have a long-term experience of participating in international cooperation projects and networks:

European Union framework programs

European Union cooperation networks

Industrial agreements

We are also active in applying for domestic financial instruments:

Estonian Research Agency

Other national knowledge-intensive research programs

News

Predictive Modeling of Pesticides Reproductive Toxicity in Earthworms Using Interpretable Machine-Learning Techniques on Imbalanced Data

Earthworms play an important role in maintaining soil fertility and in the formation and maintenance of water and aeration systems. This makes the earthworm a key indicator organism in soil ecosystems. This in turn makes the reproductive toxicity of chemical compounds (e.g. pesticides) to earthworms...

Adelle Altnurme's bachelor's thesis was awarded 1st prize in the field of natural sciences at the 2024 national student research paper competition. Supervisors Mare Oja and Uko Maran received a letter of appreciation for their supervision.

Adelle Altnurme's bachelor's thesis, "Experimental determination of octanol-water distribution coefficients of poorly soluble drug substances and comparison with predicted values", was awarded the 1st prize in the 2024 national student research competition in the field of natural sciences, for stude...

Nanomaterial Texture-Based Machine Learning of Ciprofloxacin Adsorption on Nanoporous Carbon

Drug substances in water bodies and groundwater have become a significant threat to the surrounding environment. Therefore our study focused on the ability of the nanoporous carbon materials to remove ciprofloxacin from aqueous solutions under specific experimental conditions and on the development...

Exploring the Influence of Ionic Liquid Anion Structure on Gas-Ionic Liquid Partition Coefficients of Organic Solutes Using Machine Learning

Ionic liquids (IL) form a class of chemical compounds consisting of ions, they are considered organic salts characterized by their low melting point. These unique substances have remarkable properties, including exceptionally low vapor pressure, high polarity, and excellent thermal stability. These...

Browse All

GET IN TOUCH

If you are interested in obtaining a bachelor's, master's or doctoral degree, please contact the academic and research staff of the chair. In the case of interest in consultation, training and cooperation within the above research directions, please contact:

Associate Professor Uko Maran

Professor emeritus Mati Karelson

Name *

E-mail *

Message *

Chair of Molecular Technology

Institute of Chemistry

University of Tartu

Ravila 14a,

Tartu, 50411
+372 737 5254
uko.maran@ut.ee

DESIGN: HTML5 UP.

MOL TECH