Causaly provides Covid-19 dataset to Bill and Melinda Gates' funded Global Health Drug Discovery Institute

Noelle Baquiche
Noelle Baquiche
published on March 05, 2020

At Causaly, we're honored to be supporting the 2019-nCoV Collective Research Initiative led by the Global Health Drug Discovery Institute in Beijing (GHDDI).

Launched on the 27th January in partnership with the School of Pharmaceutical Sciences (SPS) and Tsinghua University, the initiative is designed to make internal drug discovery platforms and resources available to external researchers to aid drug discovery efforts against the novel coronavirus (2019-nCoV) outbreak.

Causaly has machine-read, analyzed and mapped all existing knowledge on the corona virus genus, and supplied it to the initiative's information sharing platform, to support data collection efforts.

The main contents of the GHDDI 2019-nCoV platform include:

  1. Data mining and integration of historical drug discovery efforts against coronavirus (e.g. SARS/MERS) using AI and big data;

  2. Relevant preclinical and clinical data resources;

  3. Molecular chemical modeling and simulation data using computational tools;

  4. Latest scientific research progress on 2019-nCoV.

As part of our ongoing efforts to support Open Science and accelerate research into COVID-19, we are offering complimentary accounts for Causaly's open research dataset for all non-commercial research.

Get in touch to request access, or for more information.

Causaly vs PubMed®: 2x as many relevant articles identified by Causaly using the same data
use case

Causaly vs PubMed®: 2x as many relevant articles identified by Causaly using the same data

Causaly AI finds more relevant articles than PubMed alone, using its advanced machine-reading technology.

Target identification and validation using AI for literature-based insights: Causaly & Pierre Fabre Joint Webinar

Target identification and validation using AI for literature-based insights: Causaly & Pierre Fabre Joint Webinar

Causaly and Pierre Fabre joint webinar Causaly and Pierre Fabre co-hosted a joint webinar on the 28th of October, addressing how...

Full-text vs Abstract advantage: Causaly identifies 3x as many relevant articles by machine-reading the full-text
Application

Full-text vs Abstract advantage: Causaly identifies 3x as many relevant articles by machine-reading the full-text

Causaly enables regulatory experts to reduce time spent scanning research literature, while at the same time increase the yield from full-text articles which typically are not selected due to unsuspecting abstracts.

Sign up for Causaly newsletter