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).
Authors
- Henri Xhaard (ORCID: https://orcid.org/0000-0002-3000-7858)
- Mael A. Briand (ORCID: https://orcid.org/0000-0001-6801-7587)
- Ashenafi Legehar (ORCID: https://orcid.org/0000-0001-9706-4139)
- Evgeni Grazhdankin (ORCID: https://orcid.org/0000-0001-7932-5497)
- Loïc Dreano (ORCID: https://orcid.org/0000-0001-8280-3904)
- Alexandre Borrel (ORCID: https://orcid.org/0000-0001-6499-4540)
Institutions
- University of Helsinki (FI)
- Scion (NZ)
- Pharmaceutical Biotechnology (Czechia) (CZ)
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
Funders
- Strategic Research Council