Drug discovery inspired by African natural products

We are setting up an AI/ML platform for the identification of novel antivirals from African medicinal plants at the University of Buea

Implementing Ersilia in Cameroon

This 5-year project is supported by the Gates Foundation Calestous Juma Science Leadership Fellowship awarded to Prof. Fidele Ntie-Kang (University of Buea). Us, Ersilia, and Dr. Ian Tietjen from the Wistar Institue (US), provide our expertise to help create a leading center for drug discovery in Central and West Africa. Together, we will screen the largest collection of African natural products in search for novel antiviral drugs, with a focus on HIV (AIDS) and SARS-CoV-2 (Covid-19).

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University of Buea

Novel antimalarial and antituberculosis treatments at H3D

AI/ML models can support the decision-making process at every stage of the drug discovery cascade

Set up of a virtual screening cascade

The Holistic Drug Discovery and Development Centre (H3D) at the University of Cape Town, South Africa, is the first drug discovery center in the African continent. Their sustainability model combines international partnerships with on-site capacity building. We have partnered with them to bring novel AI/ML tools to their research pipelines and support their capacity strengthening efforts in the continent. Learn more about H3D. Our first project together has been the development of a virtual screening cascade based on H3D historical screening data. Currently, our models serve over one hundred scientists in the centre.

Collect experimental data

We start by collecting experimental data available from our collaborator. At H3D, multiple bioassays related to malaria, tuberculosis and antimicrobial resistance were available.

Train AI/ML predictive models

We use Zaira Chem to train AI/ML models at scale, based on collaborator's data. Our framework has built-in AutoML methodologies that yield excellent out of the box models.

Deploy models on-premises

We make our models broadly accessible to our partner institutions. We identify local champions and train them to use and maintain our tools.

Clinical data analysis at CIDRZ, Zambia

We collaborate with the Centre for Infectious Disease Research in Zambia (CIDRZ) to increase the efficiency of cervical cancer screening amongst women living with HIV in Lusaka.

Clinical record linkage

Clinical record linkage, or matching patient’s data from siloed healthcare facilities, is a crucial step to ensure appropriate care and follow-up. However, in many healthcare systems record linkage is not yet automatized and it requires a large effort of manual data curation. AI/ML models can help speed up the process. Our solution will bridge health care data silos, including data from routine care and implementation campaigns, that dominate the health care data ecosystem in low and middle income countries (LMIC) hampering evidence-based patient and program management.

Cervical cancer

Cervical cancer is highly preventable, and one of the most treatable forms of cancer if diagnosed early. Our first record linkage was performed in Lusaka (Zambia) on the biggest cervical cancer database in the region, the Cervical Cancer Prevention Program in Zambia (CCPPZ). As a result of our data analysis, we evaluated the outcomes of the current patient referral guidelines for HIV-positive and HIV-negative women and proposed to strengthen the surveillance on the onset of patients with higher probability of suffering from cervical cancer.

Open source antimalarials

We have contributed to the Open Source Malaria Consortium, aimed at developing antimalarial drugs following a collaborative approach.

A collaborative approach to antimalarial drug discovery

The Open Source Malaria (OSM) consortium aims to identify new treatments against malaria using a fully Open Science approach. This means all findings are disclosed in real time, promoting scientific collaboration and overcoming intellectual property constraints. We propose a wet-lab/dry-lab cycle of collaboration between OSM and Ersilia where new compounds devised by the computer are probed experimentally and undergo successive rounds of modification to achieve a highly potent antimalarial that can progress to clinical trials.


Potential antimalarial drug candidates

We used generative AI/ML methods to create a long list of antimalarial drug candidates, based on previous expertise by Open Source Malaria.


Predicted to be highly active

Then, we evaluated each drug candidate with a high-confidence predictive model for activity against the malaria parasite. We also considered synthetic accessibility of the compounds, as well as drug-like properties.


Selected for experiments

Finally, we selected a list of high-confidence compounds for experimental validation by the Open Source Malaria team. Experimental validation coming soon!

Capacity building

We believe the best way to transfer skills is by working side-by-side with our collaborators. Based on these interactions, we create resources focused on the dissemination of computational skills (AI/ML and others) to scientists in different fields.