Training courses
Thanks to our expertise in chemoinformatics and many years of experience working with commercial orders, the QSAR Lab scientific team offers specialized training courses.
Our training is conducted by a research team composed of specialists in the field of computer design and analysis of new, safe chemicals, nanotechnology, nanotoxicology, chemical statistics and chemometrics.
Training can take place according to standard programs developed by specialists or programs modified according to the client’s needs.
The training includes a theoretical component, in lecture format and a practical (workshop), during which participants independently perform the discussed issues on their computers.
Each course can be organized in two forms:
- Open training – conducted for a certain number of participants, approximately once every six months.
- Individual training – conducted out on special requests for companies and institutions.
All details such as: price, time and place of training, modules – agreed individually with the client. For closed training, please contact: contact@qsarlab.com
Current open training courses:
The safety assessment of chemicals
The course covers various topics related to using in silico (computer-based) methods to assess the safety of chemicals.
It provides training on machine learning methods, databases, molecular descriptors, similarity analysis, read-across approach, and QSAR/QSPR modeling.
The practical exercises and case studies involving open-source tools like the OECD QSAR Toolbox, VEGA, Danish (Q)SAR Database help you to use in practise in silico methods for chemical safety assessment.
Supervised and unsupervised methods
in computational chemistry
Do you work or you are interested in computational chemistry, chemical data analysis, and programming with Python?
The course is designed to cover both unsupervised and supervised methods for analyzing chemical data.
You will gain practical knowledge in chemical data curation, preprocessing, unsupervised learning, and supervised learning methods using Python to extract valuable information and make predictions from chemical datasets.
MD simulation and Molecular docking
Do you have background or interest in computational chemistry, biology, and drug discovery?
The course is designed to provide essential knowledge and skills in the field of molecular modeling, focusing on two fundamental techniques: Molecular Dynamics simulation and Molecular Docking.
Molecular modelling techniques, such as MD simulation and docking, are widely used in bioinformatics for protein structure prediction, virtual screening, and drug design.
