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
African 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.
Privacy-preserving AI/ML to foster open science in pharmaceutical companies
We are creating a tool to encrypt our AI/ML models in order to encourage pharmaceutical companies to contribute their data to the open domain without compromising their IP.
Encryption of AI/ML models
We argue that experimental results produced by pharmaceutical companies may be effectively made available to the scientific community in the form of AI/ML models, which retain the essential properties of the data but do not display the identity of the underlying compounds. We have received the support of a Merck BioPharma Speed Grant to develop a first prototype of an AI/ML model encryption tool tailored to small molecule data.
Ersilia
Modelling and encryption toolsWe provide ZairaChem, our automated AI/ML tool for chemistry, and ChemXOR, an AI/ML model encryption tool.
Data provider
IP-sensitive datasetsUsing ChemXOR & ZairaChem, pharmaceutical companies can train encrypted AI/ML models end-to-end.
Host
Cloud deploymentModels can be hosted in the cloud in their encrypted form. Either the data provider, Ersilia, or a third party can act as model hosts.
User
Protected searchesUsers can query models. Privacy of user queries is also ensured by ChemXOR, so that the identity of the queries is not revealed to the host.
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.
Code repositories
Browse a selection of our GitHub repositories to find more about specific projects, read the code and contribute!