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.
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.
406,000
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.
1,200
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.
35
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
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.