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Open Science


How To Be Ready For Future Pandemics?

   by Andrii Buvailo    523
How To Be Ready For Future Pandemics?

What do antibiotic-resistant bacteria (“superbugs”) and coronaviruses have in common? They both can kill lots of people globally, and they both have been commercially unattractive targets for the pharma business for too long.

 

[Interview] How COVID-19 Catalyzed AI-assisted Open Science Drug Discovery

   by Andrii Buvailo    468
[Interview] How COVID-19 Catalyzed AI-assisted Open Science Drug Discovery

Biopharma companies are now racing to find much-needed cures against SARS-CoV-2, a virus that caused the largest global pandemic of our time. One notable effort is the COVID Moonshot project, organized by an international consortium of scientists from academia, biotechs, contract research organizations (CROs), and pharma -- all working pro bono or via crowdfunding, philanthropy, and grants. 

The aim of the project is to rapidly develop easily manufacturable antiviral drugs that can inhibit the SARS-CoV-2 main protease, which is believed to be an Achilles heel of the coronavirus. The project is managed by PostEra, a startup company that uses artificial intelligence (AI) algorithms to map routes for chemical synthesis to speed the drug-discovery process.

Chemspace collaborates with ChemAlive

   by Kateryna Shtereverya    210
Chemspace collaborates  with ChemAlive

Chemspace and ChemAlive (the largest repository of quantum chemical data, a Contract Research Company that develops software products to obtain accurate and reliable data on molecular properties, synthetic reactions, verification of molecules using quantum chemistry), announced the launch of the collaboration to provide accurate molecular structural information about commercial small molecules.

Genenerative AI Models In Small Molecule Drug Discovery: The Open Challenge To Create A Unified Benchmark

   by Mostapha Benhenda    4345
Genenerative AI Models In Small Molecule Drug Discovery: The Open Challenge To Create A Unified Benchmark

Generative AI models in chemistry are increasingly popular in the research community, mainly, due to their interest for drug discovery applications. They generate virtual molecules with desired chemical and biological properties (more details in this blog post).

However, this flourishing literature still lacks a unified benchmark. Such benchmark would provide a common framework to evaluate and compare different generative models. Moreover, it would help to formulate best practices for this emerging industry of ‘AI molecule generators’: how much training data is needed, for how long the model should be trained, and so on.