Digital chemistry
Digital chemistry
CHEMISTRY
Course of education |
2024- 2026 |
Specialisation |
Digital Chemistry |
Type of study |
Second-cycle studies / Master’s study |
Mode of study |
Full-time studies |
Study profile |
academic |
During each semester, the student should obtain a minimum of 30 ECTS points from obligatory and optional classes (elective)
SEMESTER 1 |
||||||
subject |
lecture |
auditorium classes |
laboratory classes |
TOTAL |
E/P |
ECTS |
Education health and safety (e-learning; extended course) |
|
5 |
|
5 |
PWN |
0 |
|
30 |
|
30 |
P |
3 |
|
|
30 |
|
30 |
P |
3 |
|
|
30 |
|
30 |
P |
3 |
|
10 |
|
|
10 |
P |
1 |
|
Introduction to Python programming – lecture |
15 |
|
|
15 |
E |
2 |
Introduction to Python programming – laboratory classes |
|
|
45 |
45 |
Z |
3 |
Quantum chemistry in practice – lecture |
30 |
|
|
30 |
E |
3 |
Quantum chemistry in practice – laboratory classes |
|
|
45 |
45 |
Z |
3 |
Exploratory analysis of multidimensional chemical space – lecture |
30 |
|
|
30 |
E |
3 |
Exploratory analysis of multidimensional chemical space – laboratory classes |
|
|
45 |
45 |
Z |
4 |
Foreign language III |
|
30 |
|
30 |
P |
2 |
In the first semester, students complete a compulsory library course |
||||||
SEMESTER 1 |
85 |
125 |
135 |
345 |
3 |
30 |
SEMESTER 2 |
||||||
subject |
lecture |
auditorium lasses |
laboratory classes |
TOTAL |
E/P |
ECTS |
Introduction to R programming - lecture |
15 |
|
|
15 |
E |
2 |
Introduction to R programming – laboratory classes |
|
|
45 |
45 |
Z |
3 |
Molecular mechanics & dynamics, coarse-grain modeling - lecture |
30 |
|
|
30 |
E |
3 |
Molecular mechanics & dynamics, coarse-grain modeling – laboratory classes |
|
|
45 |
45 |
Z |
3 |
Specialization lecture*: |
30 |
|
|
30 |
P |
3 |
|
|
180 |
180 |
P |
12 |
|
Facultative course I: - Parallel programing in Python - Data bases & big data |
|
|
30 |
30 |
P |
2 |
Facultative course II: - Microcontroler-based chemical diagnosis - Omics analysis in chemoinformatics |
|
30 |
|
30 |
P |
2 |
SEMESTER 2 |
75 |
30 |
300 |
405 |
2 |
30 |
YEAR I |
160 |
155 |
435 |
750 |
5 |
60 |
SEMESTER 3 |
||||||
subject |
lecture |
auditorium lasses |
laboratory classes |
TOTAL |
E/P |
ECTS |
Machine learning in chemistry – lecture |
30 |
|
|
30 |
E |
3 |
Machine learning in chemistry – laboratory classes |
|
|
45 |
45 |
Z |
3 |
15 |
|
|
15 |
P |
1 |
|
30 |
|
|
30 |
P |
2 |
|
|
|
180 |
180 |
P |
10 |
|
|
30 |
|
30 |
P |
4 |
|
Monographic lecture*: |
30 |
|
|
30 |
P |
3 |
Facultative course III: - Insights into reaction mechanisms and kinetics via quantum chemistry methods - QSAR in toxicology |
|
|
30 |
30 |
P |
2 |
Facultative course IV: - Statistical mechanics of biological macromolecules - Advanced nanoiformatics |
|
30 |
|
30 |
P |
2 |
SEMESTER 3 |
105 |
60 |
255 |
420 |
1 |
30 |
SEMESTER 4 |
||||||
subject |
lecture |
auditorium lasses |
laboratory classes |
TOTAL |
E/P |
ECTS |
30 |
|
|
30 |
P |
2 |
|
|
|
190 |
190 |
P |
10 |
|
|
30 |
|
30 |
P |
4 |
|
Monographic lecture*: |
30 |
|
|
30 |
P |
3 |
Facultative course V: - Numerical methods with agroithms for physical sciences - Computationally added drug design |
|
|
30 |
30 |
P |
2 |
Facultative coyrse VI: - Chemical bonding via quantum chemistry tools - Computational methods for designing advanced materials |
|
30 |
|
30 |
P |
2 |
MSc exam |
E |
7 |
||||
SEMESTER 4 |
60 |
60 |
220 |
340 |
1 |
30 |
YEAR II |
165 |
120 |
475 |
760 |
2 |
60 |
ATTENTION: Colours refer to two blocks of methods: (i) physics-based methods and (ii) data-based (chemoinformatics) methods
E – exam
P – pass with note
PWN – pass without note
*classes conducted at the Department, where the student is doing his master’s thesis
Second-cycle studies end with master’s examination and obtaining the professional title of master’s degree.