Computational literature-based natural product drug discovery
Auf einen Blick
- Projektleiter/in : Dr. Manuel Gil
- Projektteam : Dr. Maria Anisimova, Dr. Andreas Lardos, Dr. Evelyn Wolfram
- Projektstatus : abgeschlossen
- Drittmittelgeber : Interne Förderung
Beschreibung
Natural products such as medicinal plants and extract mixtures
have successfully supported the discovery of pharmaceuticals.
Medically relevant products and their properties are often found
through systematic analysis of the literature. In 1980s Swanson
found hidden links between pieces of knowledge in the scientific
literature by a manual algorithm, hinting that literature based
discovery (LBD) can be automated. Swanson formulated clinically
relevant hypotheses which were confirmed in trials.
LBD has become widespread, but little has been done to automate it
in the field of Natural Product Drug Discovery. Today manual text
mining is common practice in this field, but it is extremely
laborious and time-consuming. Semantic Web technology is ideal for
automating LBD. Yet, no LBD system has adopted it in an integrated
approach. It builds on semantic atomic data entities, so called
subject-predicate object triples. Terms in triples can be mapped to
ontologies, which define a common vocabulary, machine interpretable
concepts and logic based relations. The biomedical community has
been very active in ontology development.
We propose to devise an automated LBD system for natural product
drugs, using Semantic Web technologies. Our system can be used not
only for new discoveries, but also to query and explore present
knowledge. Further, it reduces discovery costs by saving time in
evaluating literature for drug development, but is also relevant to
safety and pharmacovigilance aspects.
Publikationen
-
Koroleva, Anna; Anisimova, Maria; Gil, Manuel,
2020.
Towards creating a new triple store for literature-based discovery [Paper].
In:
Lu, Wei; Zhu, Kenny Q., Hrsg.,
Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2020.
PAKDD 2020 Workshops, DSFN, GII, BDM, LDRC and LBD, Singapore, 11-14 May 2020.
Cham:
Springer.
S. 41-50.
Lecture Notes in Computer Science ; 12237.
Verfügbar unter: https://doi.org/10.1007/978-3-030-60470-7_5