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Wednesday, January 29, 2025
Reproducible Machine Learning in Drug Discovery: How Polaris Serves as a Single Source of Truth
One of the guiding design principles we followed when first building Polaris, was this idea of serving as a single source of truth for the machine learning community tackling drug discovery challenges. But what exactly does that mean? How does that inform the features that we're building for the platform? Read the blog to learn more!
Jonathan Hsu
Tuesday, December 17, 2024
Imagining the Future of ML Evaluation in Drug Discovery
Due to the scattered and inaccessible nature of evaluative insights, innovation in machine learning for drug discovery often struggle to translate into real drug discovery programs. At Polaris, we are actively re-thinking how ML methods are assessed and compared in order to bridge this gap. Let’s take a closer look at what this future of ML evaluation in drug discovery could look like!
Kun Zhu
Tuesday, December 3, 2024
Antiviral Competition: Advancing Open Science with ASAP Discovery and OpenADMET
Competitions are a critical tool to evaluate the current state of the art in computational molecular sciences. As par of its open science mission, the ASAP Discovery Consortium is launching an antiviral competition in collaboration with OpenADMET. Test your skills in ligand pose prediction, potency prediction, and ADMET prediction, all revolving around SARS-CoV-2 and MERS-CoV Mpro.
Jenke Scheen
Thursday, October 31, 2024
Julien St-Laurent

Dataset v2.0 - Built to scale!

With the Polaris Hub, we set out to design a universal data format for ML scientists in drug discovery. Whether you’re working with phenomics, small molecules, or protein structures, you shouldn’t have to spend time learning about domain-specific file formats, APIs, and software tools to be able to run some ML experiments. Learn more about how we support extra-large datasets, like BELKA, in this behind-the-scenes post!

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Thursday, October 17, 2024
Cas Wognum

Introducing our Small Molecule Steering Committee

Over the past few years, the field of machine learning for drug discovery (MLDD) has experienced an unprecedented surge of innovation. But how do we know whether these innovations can truly impact the development of new drugs? It's hard to tell.

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