Assessing Molecular Contacts Using Atom Environments Described by Ranked Lists

We investigate the representation of atomic molecular environments as a list of the atoms neighboring an origin ranked by distance. We show that contact densities collected for origin-neighbor pairs of set types have regular and specific distributions when stratified by ranks. This is used to build a scoring function assessing the fitness of individual atoms with respect to their environment (FS, fitness score), as well as to characterize the contact preferences (CP) for pairs of atoms. Tested against the 3DRobot data set, the FS score was found to be highly discriminative of decoys vs native X-ray. The code is freely available on GitHub (https://github.com/DreanoLoic/Fitness_score.git).

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2026-07-16
DOI
https://doi.org/10.1021/acs.jcim.6c00694
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Assessing Molecular Contacts Using Atom Environments Described by Ranked Lists

Henri Xhaard, Mael A. Briand, Ashenafi Legehar, Evgeni Grazhdankin et al.
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
article

Assessing Molecular Contacts Using Atom Environments Described by Ranked Lists

Henri Xhaard, Mael A. Briand, Ashenafi Legehar, Evgeni Grazhdankin, Loïc Dreano, Alexandre Borrel
article en

Abstract

We investigate the representation of atomic molecular environments as a list of the atoms neighboring an origin ranked by distance. We show that contact densities collected for origin-neighbor pairs of set types have regular and specific distributions when stratified by ranks. This is used to build a scoring function assessing the fitness of individual atoms with respect to their environment (FS, fitness score), as well as to characterize the contact preferences (CP) for pairs of atoms. Tested against the 3DRobot data set, the FS score was found to be highly discriminative of decoys vs native X-ray. The code is freely available on GitHub (https://github.com/DreanoLoic/Fitness_score.git).

Journal of Chemical Information and Modeling
University of Helsinki (FI), Scion (NZ), Pharmaceutical Biotechnology (Czechia) (CZ)
Strategic Research Council
Reduced inequalities
Openalex Percentile: Top 7%
Computational Drug Discovery Methods
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