Our projects

We aim to provide computational tools that simplify and speed up day-to-day research.


Our assets can be used without specific data science expertise.


We make tools that can run on the browser or on standard computers.

Precomputed datasets

We provide a number of predictions off-the-shelf to reduce computational burden.


Complex pipelines can be built by assembling multiple AI/ML assets.

Clean code

You can find our code well organized, commented, readable and open.


We work hand-in-hand with our collaborators and users.

Five hundred models in the Ersilia Model Hub

Our aim is to reach 500 models by the end of 2022, including AI/ML assets developed by the scientific community, as well as in-house assets build by us.

Latest models

Check the latest additions to the Ersilia Model Hub! We systematically look for AI/ML models and datasets in the scientific literature and incorporate them in our platform.

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Contribute to the Ersilia Model Hub

Suggest the models or datasets you would like to see in the Ersilia Model Hub or help us improve our open source platform!

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!

Set up of a virtual screening cascade

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

Automated machine learning for out-of-the-box predictive models

We have developed an automated AI/ML pipeline (Zaira Chem) to facilitate on-demand modelling.

No processing

A data pre-processing module takes care of removing spurious data points, standardise chemical structures, and harmonise inputs and outputs.

Comprehensive molecular representation

We use a comprehensive set of small molecule descriptors, including physicochemical properties, graph-based embeddings, text representations and bioactivity signatures.

State of the art AutoML

We benefit from the latest advances in automated machine learning to provide highly performant models without the need of choosing algorithms and hyperparameters.

Performance reports

Model validations tests are done automatically, and reports are produced to enable quick assessment of the model quality.

End-to-end implementation at H3D

Our first adopter has been the Holistic Drug Discovery and Development Centre (H3D) at the University of Cape Town, South Africa. Thanks to Zaira Chem, we have successfully trained over a dozen AI/ML models 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.

Medicinal plants as a source of novel antiviral drugs

We are contributing to the set up of a platform for nature-inspired identification of novel antivirals with distinctive mechanisms of action.

All projects

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Code repositories

Browse a selection of our GitHub repositories to find more about specific projects, read the code and contribute!

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