CACHE Challenge #1: Docking with GNINA Is All You Need

We describe our winning submission to the first Critical Assessment of Computational Hit-Finding Experiments (CACHE) challenge. In this challenge, 23 participants employed a diverse array of structure-based methods to identify hits to a target with no known ligands. We utilized two methods, pharmacophore search and molecular docking, to identify our initial hit list and compounds for the hit expansion phase. Unlike many other participants, we limited ourselves to using docking scores in identifying and ranking hits. Our resulting best hit series tied for first place when evaluated by a panel of expert judges. Here, we report our top-performing open-source workflow and results.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2024-12-10
DOI
https://doi.org/10.1021/acs.jcim.4c01429
Citations
15
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
5.42

Funders

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article

CACHE Challenge #1: Docking with GNINA Is All You Need

Somayeh Pirhadi, David Ryan Koes, Ian Dunn, Smmrithi Ravindran et al.
15 citations
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
5.42
article

CACHE Challenge #1: Docking with GNINA Is All You Need

Somayeh Pirhadi, David Ryan Koes, Ian Dunn, Smmrithi Ravindran, Carter Concepcion, Yao Wang
article en
15 citations

Abstract

We describe our winning submission to the first Critical Assessment of Computational Hit-Finding Experiments (CACHE) challenge. In this challenge, 23 participants employed a diverse array of structure-based methods to identify hits to a target with no known ligands. We utilized two methods, pharmacophore search and molecular docking, to identify our initial hit list and compounds for the hit expansion phase. Unlike many other participants, we limited ourselves to using docking scores in identifying and ranking hits. Our resulting best hit series tied for first place when evaluated by a panel of expert judges. Here, we report our top-performing open-source workflow and results.

Journal of Chemical Information and ModelingVol. 64(24)
University of Pittsburgh (US)
National Institute of General Medical Sciences
Openalex Percentile: Top 4%
Computational Drug Discovery Methods
5.42
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