
Computational Drug Design - 2020/2021 Edition
Lecturers: Dr. Florian Blanc, Dr. Ramachandra Bhaskara, M.Sc. Laura Schulz
< back to Teaching Activities
Course open to all interested students
every Tuesday 14:00 – 16:00 (s.t.)
Course Documentation - 2020/2021
Lecture 1, 2020.11.03 (PDF) password protected
Lecture 2, 2020.11.10 (PDF) password protected
Lecture 3, 2020.11.17 (PDF) password protected
Lecture 4, 2020.11.24 (PDF) password protected
Lecture 5+6, 2020.12.01 (PDF) password protected
Lecture 7, 2020.12.16 (PDF) password protected
Lecture 8, 2021.01.12 (PDF) password protected
Lecture 9, 2021.01.19 (PDF) password protected
Lecture 10, 2021.01.26 (PDF) password protected
Lecture 11, 2021.02.02 (PDF) password protected
Lecture 12, 2021.02.09 (PDF) password protected
All lectures will be held via Zoom. For Zoom login information please contact the lecturers via email:
Florian Blanc: florian.blanc@biophys.mpg.de
Ramachandra Bhaskara: ramachandra.bhaskara@biophys.mpg.de
Laura Schulz: laura.schulz@biophys.mpg.de

Content covered in 2019 lectures (incl. practicals)
- 1. Introduction to computational drug design
- 2. Ligand-based approachees
- a. (Quantitative) structure-activity relationship (SAR & QSAR)
- b. Pharmacophore modeling
- 3. Bioinformatics approaches (target recognition and structural modeling)
- a. Sequence alignments and searches
- b. Gene identification and prediction
- c. Homology modeling
- 4. Structure-based approaches
- a. Molecular docking
- i. Ligand docking: theory and scoring functions
- ii. Virtual screening
- iii. Protein-protein docking and interaction
- b. Molecular dynamics simulation
- i. Introduction into molecular dynamics
- ii. Estimation of ligand binding affinity
- a. Molecular docking
- 5. Free energy perturbation
- 6. Enhance sampling methods
Topics for student presentations
QSAR
- 1. QSAR modeling using partial least square (PLS)
- 2. Application of neural networks in QSAR
Modeling
- 1. Profile based methods and applications to sequence alignment
- 2. Threading for protein structure prediction
- 3. Using co-evolution for protein structure prediction
Docking
- 1. Protein-protein interface prediction using 3D structure and residue conservation
- 2. Induced-fit docking
MD simulation
- 1. Thermodynamic integration (TI) for free energy calculation
- 2. Non-equilibrium simulation
- 3. Advanced methods in enhanced sampling
- 4. Metadynamics