PMID- 32751710
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20200928
IS  - 2079-7737 (Print)
IS  - 2079-7737 (Electronic)
IS  - 2079-7737 (Linking)
VI  - 9
IP  - 8
DP  - 2020 Jul 30
TI  - Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with 
      Random Forest Models.
LID - 10.3390/biology9080198 [doi]
LID - 198
AB  - Drug-decorated nanoparticles (DDNPs) have important medical applications. The 
      current work combined Perturbation Theory with Machine Learning and Information 
      Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of 
      nanoparticle-compound/drug complexes having antimalarial activity (against 
      Plasmodium). The aim is to save experimental resources and time by using a 
      virtual screening for DDNPs. The raw data was obtained by the fusion of 
      experimental data for nanoparticles with compound chemical assays from the ChEMBL 
      database. The inputs for the eight Machine Learning classifiers were transformed 
      features of drugs/compounds and nanoparticles as perturbations of molecular 
      descriptors in specific experimental conditions (experiment-centered features). 
      The resulting dataset contains 107 input features and 249,992 examples. The best 
      classification model was provided by Random Forest, with 27 selected features of 
      drugs/compounds and nanoparticles in all experimental conditions considered. The 
      high performance of the model was demonstrated by the mean Area Under the 
      Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 
      +/- 0.000244 (10-fold cross-validation). The results demonstrated the power of 
      information fusion of the experimental-centered features of drugs/compounds and 
      nanoparticles for the prediction of nanoparticle-compound antimalarial activity. 
      The scripts and dataset for this project are available in the open GitHub 
      repository.
FAU - Urista, Diana V
AU  - Urista DV
AD  - Department of Organic Chemistry II, University of Basque Country (UPV/EHU), 
      Sarriena w/n, 48940 Leioa, Spain.
FAU - Carrue, Diego B
AU  - Carrue DB
AUID- ORCID: 0000-0002-7489-3775
AD  - RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus 
      Elvina s/n, 15071 A Coruna, Spain.
FAU - Otero, Iago
AU  - Otero I
AUID- ORCID: 0000-0002-7924-693X
AD  - RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus 
      Elvina s/n, 15071 A Coruna, Spain.
FAU - Arrasate, Sonia
AU  - Arrasate S
AD  - Department of Organic Chemistry II, University of Basque Country (UPV/EHU), 
      Sarriena w/n, 48940 Leioa, Spain.
FAU - Quevedo-Tumailli, Viviana F
AU  - Quevedo-Tumailli VF
AUID- ORCID: 0000-0001-8278-3632
AD  - RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus 
      Elvina s/n, 15071 A Coruna, Spain.
AD  - Universidad Estatal Amazonica UEA, Km. 2 1/2 via Puyo a Tena (paso lateral), Puyo 
      160150, Pastaza, Ecuador.
FAU - Gestal, Marcos
AU  - Gestal M
AUID- ORCID: 0000-0002-4371-8632
AD  - RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus 
      Elvina s/n, 15071 A Coruna, Spain.
AD  - Biomedical Research Institute of A Coruna (INIBIC), Hospital Teresa Herrera, 
      Xubias de Arriba 84, 15006 A Coruna, Spain.
FAU - Gonzalez-Diaz, Humbert
AU  - Gonzalez-Diaz H
AUID- ORCID: 0000-0002-9392-2797
AD  - Department of Organic Chemistry II, University of Basque Country (UPV/EHU), 
      Sarriena w/n, 48940 Leioa, Spain.
AD  - IKERBASQUE, Basque Foundation for Science, Alameda Urquijo 36, 48011 Bilbao, 
      Spain.
AD  - Basque Centre for Biophysics CSIC-UPVEHU, University of Basque Country UPV/EHU, 
      Barrio Sarriena, 48940 Leioa, Spain.
FAU - Munteanu, Cristian R
AU  - Munteanu CR
AUID- ORCID: 0000-0002-5628-2268
AD  - RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus 
      Elvina s/n, 15071 A Coruna, Spain.
AD  - Biomedical Research Institute of A Coruna (INIBIC), Hospital Teresa Herrera, 
      Xubias de Arriba 84, 15006 A Coruna, Spain.
LA  - eng
GR  - ED431C 2018/49/Ministry of Education, University and Vocational Training of Xunta 
      de Galicia/
PT  - Journal Article
DEP - 20200730
PL  - Switzerland
TA  - Biology (Basel)
JT  - Biology
JID - 101587988
PMC - PMC7465777
OTO - NOTNLM
OT  - ChEMBL database
OT  - Machine Learning
OT  - Perturbation Theory
OT  - antimalarial compounds
OT  - big data
OT  - decorated nanoparticles
OT  - drug delivery
COIS- The authors declare no conflict of interest.
EDAT- 2020/08/06 06:00
MHDA- 2020/08/06 06:01
PMCR- 2020/08/01
CRDT- 2020/08/06 06:00
PHST- 2020/06/24 00:00 [received]
PHST- 2020/07/22 00:00 [revised]
PHST- 2020/07/27 00:00 [accepted]
PHST- 2020/08/06 06:00 [entrez]
PHST- 2020/08/06 06:00 [pubmed]
PHST- 2020/08/06 06:01 [medline]
PHST- 2020/08/01 00:00 [pmc-release]
AID - biology9080198 [pii]
AID - biology-09-00198 [pii]
AID - 10.3390/biology9080198 [doi]
PST - epublish
SO  - Biology (Basel). 2020 Jul 30;9(8):198. doi: 10.3390/biology9080198.