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Kenbelt Park, Nairobi, Kenya

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Outsourec yur clinical trials in Africa

Computer-Aided Drug Design Course

In this intensive training, participants will learn the science involved in disease target identification, virtual screening techniques, in-silico generation of ligands, protein optimization & energy minimization, molecular docking, creation of Grid Parameter & Dock Parameter files, running the Docking Algorithm, pharmacophore modeling, and drug repurposing. They will also learn how to select potent inhibitors on the basis of binding energies and Lipinski ’s Rule of 5, how to look for H-bonds between ligands and active sites of protein residues as well as aspects of drug Likeness, ADME & Toxicity. Practical hands-on training will encompass training on the use of KenClust, software such as MarvinSketch, UCSF Chimera, AutoDock Tools, Ligand Scout, Rosetta, Open Babel, and SPDV among others. Additional training will include pharmacophore modelling and use of AI tools such as AlphaFold to predict 3D Structure and generative AI for drug discovery. Lab work will include bacterial culture, antimicrobial susceptibility testing, and genotoxicity testing.

At the end of this course, the participant will be able to:
  • Understand the science of rational drug discovery and design
  • Discover or create drug leads and modify existing drugs
  • Test the safety and efficacy of drugs
  • Use AI Tools to model or discover drugs
  • Synthesize the drug leads and test the effect of the drug lead on selected laboratory animals
  • Cultivate bacteria, prepare antimicrobial discs, assess sensitivity to the drugs and perform genotoxic assays
  • Perform biochemistry assays to determine the biochemical effects of the drug
  • Perform hematology assays to determine the biochemical effects of the drug
  • Become an expert in CADD
Using the most powerful software, you will gain the latest skills in computer-aided drug design.
  • Web Resources – NCBI, SILVA Database
  • Software –
  • Computing environment – Linux computing using KENCLUST
  • Software add-ons for the participant – Required – PUTTY, WinSCP. Optional – R, XMING, BioLinux, and Java Virtual Box
  • Laptop*
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  • Introduction to in-silico-based drug discovery and design and its place in the drug pipeline.
  • Target identification using network pathways, interactomics, disease databases, literature search, metabolomics, and gene expression profiling
  • Obtaining crystallographic protein-ligand complexes from RCSB PDB, and homology modelling of protein targets.Energy minimization using the Amber force field/CHARMM.
  • Obtaining large ligand subsets using Wget from ZINC.
  • 1D/2D filtering of ligands using lead-likeness and Lipinski’s rule of five.
  • Preparation of coordinate files using Chimera 1.9.
  • Protein-ligand re-docking and docking using the AutoDock Tools/Vina.
  • Analysis of docking results.
  • Identification of ligand binding sites through blind docking.
  • Virtual screening using PyRx and Raccoon.
  • Analysis of virtual screening results using FOX and the summarize_results.py script
  • Ligand-based drug design and pharmacophore modelling.
  • Molecular dynamics using Gromacs
  • In-silico based ADME-tox
  • Introduction to HPC using KenClust
  • Dry Lab exercises.
  • Wet Lab experiments.
  • Animal studies
The course is ideal for students and researchers working in the pharma, biotech, and medical industries. The following terms and conditions apply.
  • The fee is Kshs. 100,000/ for East African residents. Non-East African students pay 1,000 USD.
  • Fees covers tuition, training material, and certificate only.
  • Fees are payable strictly in advance (at least 4 working days before the starting date).
  • Groups with a minimum of 5 people will enjoy a 10% discount and early bird registration (at least 2 weeks before the start) will be offered a 5% discount.
  • There will be a 10% administrative charge for cancellations received in writing up to 20 working days before the start of the course. No refunds will be made for cancellations received within 20 working days of the course start date or for the inability to attend the course for whatever reasons. Substitutions may, of course, be made at any time, providing you inform us in writing. KIBs is not liable for non-attendance due to travel disruptions, health problems or any other reason that might lead to a delegate not being able to attend the course.
  • Fee is payable by direct depsoit, bankers cheque or MPESA to: Bioinformatics Institute of Kenya Ltd. A/C No. 0012342070001, Cooperative Bank of Kenya, Co-op House Branch, Nairobi, Kenya. The Paybill number for payments via Mpesa is 400222 and the account number is 1679821#your name. Contact us for online payments.
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