Informatics and Visualisation
Glycoinformatics | Bioinformatics | Structural Biology | Cheminformatics | Visual Analytics
Computation and Modelling
QM/MM Computations | Free Energy Computations | Coarse Grain Computations | Molecular Modelling | Ionic Liquid Property Design
Accelerated Code for Life Science Applications
GPU Hardware acceleration | InfiniBand CPU clusters
Cancer Translational Science Laboratory
Glycobiology | Next Generation Antimicrobials | Cancer
Glycoenzyme Kinetics
Synthesis | High Throughput Screening | Glycoenzyme Assays | Computational Design | Kinetics


Saturday, 19 October 2019
Attendance at Indaba9


Sunday, 02 September 2018 - Friday, 07 September 2018

Presenting at the INDABA 9 Conference

Prof Kevin Naidoo - Multidimensional Reaction Dynamics of Enzyme Catalysis Dr Chris Barnett - Computational Cancer Glycobiology: Simulation, Analysis and Visualisation
Publication Date:
Sun, 02 Sep 2018 - 16:00
A first-of-its-kind cancer treatment

BXQ-350 is a first-of-its-kind cancer treatment that weaponizes the special mechanics of cancer to destroy it. Discovered by Xiaoyang Qi - Professor of Medicine, Co-Division Chief for Basic Science Research - BXQ-350 is a protein which can fuse to the walls of cancerous cells, causing those cells to die off.

The news article and publication are available.

Publication Date:
Fri, 10 Aug 2018 - 16:00
South Africa's battle to resuscitate cancer care


The South African Health Minister Aaron Motsoaledi acknowledged that the national healthcare system is battling a rise in cancer cases. Some patients die before receiving treatment (Read full article). 
We are developing cancer diagnostics with a focus on early detection, read more about our research.

Publication Date:
Fri, 25 May 2018 - 16:00
Unsupervised Learning method "Denoising Autoencoder Self-Organising Map"

See our Unsupervised Learning method "Denoising Autoencoder Self-Organising Map" (DASOM) although developed for the Early Cancer Diagnostics Project it is universally applicable to a wide range of applications. DASOM integrates autoencoders into a hierarchically organised hybrid model where the front-end component is a grid of topologically ordered neurons. The model maintains clustering properties but by extending and enhancing its visualisation capacity it enables an inclusion and analysis of the intermediate representative space. Download the paper for free at

Publication Date:
Fri, 25 May 2018 - 16:00