Local-Global Contrast for Learning Voice-Face Representations

Chen, Guangyu; Zhang, Deyuan; Liu, Tao; Du, Xiaoyong

doi:10.1109/icip49359.2023.10222130

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…The computational teams chose approaches from a vast variety of different methods (see Figure 2) and therefore considered diverse orthogonal approaches. However, from today's perspective few-(and zero-) shot methods, developed more recently, would have been an intuitive fit [98][99][100][101][102][103][104][105]. An important aspect of this challenge was its emphasis on the exploration of billions of candidate compounds for activity against the target proteins.…”

Section: Discussionmentioning

confidence: 99%

A community effort in SARS‐CoV‐2 drug discovery

Schimunek,

Seidl,

Elez

et al. 2023

Molecular Informatics

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The COVID‐19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small‐molecule drugs that are widely available, including in low‐ and middle‐income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the “Billion molecules against COVID‐19 challenge”, to identify small‐molecule inhibitors against SARS‐CoV‐2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find ‘consensus compounds’. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding‐, cleavage‐, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS‐CoV‐2 treatments.

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