-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathCITATION.cff
55 lines (53 loc) · 3.17 KB
/
CITATION.cff
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA - benchmark dataset.
message: A benchmark dataset based on ligands with known or putative binding activity toward six RNA targets.
type: dataset
authors:
- given-names: "Natalia A. Szulc "
email: [email protected]
affiliation: >-
International Institute of Molecular and Cell
Biology in Warsaw, Warsaw, Poland
orcid: "https://orcid.org/0000-0002-2991-3634"
- given-names: Zuzanna Mackiewicz
email: [email protected]
affiliation: >-
International Institute of Molecular and Cell
Biology in Warsaw, Warsaw, Poland
orcid: "https://orcid.org/0000-0003-1654-9025"
- orcid: "https://orcid.org/0000-0002-6633-165X"
given-names: Janusz M. Bujnicki
email: [email protected]
affiliation: >-
International Institute of Molecular and Cell
Biology in Warsaw, Warsaw, Poland
- orcid: "https://orcid.org/0000-0001-5758-9416"
given-names: Filip Stefaniak
affiliation: >-
International Institute of Molecular and Cell
Biology in Warsaw, Warsaw, Poland
email: [email protected]
identifiers:
- type: doi
value: 10.5281/zenodo.7486183
description: Zenodo repository
- type: url
value: >-
https://github.com/filipspl/fingernat-ml/
description: GitHub repository
repository-code: "https://github.com/filipspl/fingernat-ml/"
url: "https://github.com/filipspl/fingernat-ml/"
abstract: >-
Ribonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA—small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion- and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes.
keywords:
- rna
- small molecules
- non-covalent interactions
- interaction fingerprint
- python
- drug design
- machine learning
license: CC0-1.0