158 related articles for article (PubMed ID: 28690382)
1. QSAR based predictive modeling for anti-malarial molecules.
Bharti DR; Lynn AM
Bioinformation; 2017; 13(5):154-159. PubMed ID: 28690382
[TBL] [Abstract][Full Text] [Related]
2. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
Crider K; Williams J; Qi YP; Gutman J; Yeung L; Mai C; Finkelstain J; Mehta S; Pons-Duran C; Menéndez C; Moraleda C; Rogers L; Daniels K; Green P
Cochrane Database Syst Rev; 2022 Feb; 2(2022):. PubMed ID: 36321557
[TBL] [Abstract][Full Text] [Related]
3. CAPi: Computational Model for Apicoplast Inhibitors Prediction Against Plasmodium Parasite.
Dixit S; Singla D
Curr Comput Aided Drug Des; 2017 Nov; 13(4):303-310. PubMed ID: 28260517
[TBL] [Abstract][Full Text] [Related]
4. Development and validation of consensus machine learning-based models for the prediction of novel small molecules as potential anti-tubercular agents.
Wani MA; Roy KK
Mol Divers; 2022 Jun; 26(3):1345-1356. PubMed ID: 34110578
[TBL] [Abstract][Full Text] [Related]
5. Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data.
Koutsoukas A; Monaghan KJ; Li X; Huan J
J Cheminform; 2017 Jun; 9(1):42. PubMed ID: 29086090
[TBL] [Abstract][Full Text] [Related]
6. Targeting the apicoplast in malaria.
Biddau M; Sheiner L
Biochem Soc Trans; 2019 Aug; 47(4):973-983. PubMed ID: 31383817
[TBL] [Abstract][Full Text] [Related]
7. Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.
Wu Z; Zhu M; Kang Y; Leung EL; Lei T; Shen C; Jiang D; Wang Z; Cao D; Hou T
Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33313673
[TBL] [Abstract][Full Text] [Related]
8. Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.
Fang X; Bagui S; Bagui S
Comput Biol Chem; 2017 Aug; 69():110-119. PubMed ID: 28601761
[TBL] [Abstract][Full Text] [Related]
9. Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?
Anderson AB; Grazal CF; Balazs GC; Potter BK; Dickens JF; Forsberg JA
Clin Orthop Relat Res; 2020 Jul; 478(7):0-1618. PubMed ID: 32282466
[TBL] [Abstract][Full Text] [Related]
10. Predictive modeling of anti-malarial molecules inhibiting apicoplast formation.
Jamal S; Periwal V; ; Scaria V
BMC Bioinformatics; 2013 Feb; 14():55. PubMed ID: 23419172
[TBL] [Abstract][Full Text] [Related]
11. Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods.
Qin R; Wang H; Yan A
SAR QSAR Environ Res; 2021 May; 32(5):411-431. PubMed ID: 33896285
[TBL] [Abstract][Full Text] [Related]
12. Combinatorial QSAR modeling of P-glycoprotein substrates.
de Cerqueira Lima P; Golbraikh A; Oloff S; Xiao Y; Tropsha A
J Chem Inf Model; 2006; 46(3):1245-54. PubMed ID: 16711744
[TBL] [Abstract][Full Text] [Related]
13. In Silico Study of In Vitro GPCR Assays by QSAR Modeling.
Mansouri K; Judson RS
Methods Mol Biol; 2016; 1425():361-81. PubMed ID: 27311474
[TBL] [Abstract][Full Text] [Related]
14. A Study of Applications of Machine Learning Based Classification Methods for Virtual Screening of Lead Molecules.
Vyas R; Bapat S; Jain E; Tambe SS; Karthikeyan M; Kulkarni BD
Comb Chem High Throughput Screen; 2015; 18(7):658-72. PubMed ID: 26138573
[TBL] [Abstract][Full Text] [Related]
15. Understanding the biology of the Plasmodium falciparum apicoplast; an excellent target for antimalarial drug development.
Chakraborty A
Life Sci; 2016 Aug; 158():104-10. PubMed ID: 27381078
[TBL] [Abstract][Full Text] [Related]
16. Combinatorial QSAR of ambergris fragrance compounds.
Kovatcheva A; Golbraikh A; Oloff S; Xiao YD; Zheng W; Wolschann P; Buchbauer G; Tropsha A
J Chem Inf Comput Sci; 2004; 44(2):582-95. PubMed ID: 15032539
[TBL] [Abstract][Full Text] [Related]
17. Editorial: Current status and perspective on drug targets in tubercle bacilli and drug design of antituberculous agents based on structure-activity relationship.
Tomioka H
Curr Pharm Des; 2014; 20(27):4305-6. PubMed ID: 24245755
[TBL] [Abstract][Full Text] [Related]
18. Integrative proteomics and bioinformatic prediction enable a high-confidence apicoplast proteome in malaria parasites.
Boucher MJ; Ghosh S; Zhang L; Lal A; Jang SW; Ju A; Zhang S; Wang X; Ralph SA; Zou J; Elias JE; Yeh E
PLoS Biol; 2018 Sep; 16(9):e2005895. PubMed ID: 30212465
[TBL] [Abstract][Full Text] [Related]
19. Development and rigorous validation of antimalarial predictive models using machine learning approaches.
Danishuddin ; Madhukar G; Malik MZ; Subbarao N
SAR QSAR Environ Res; 2019 Aug; 30(8):543-560. PubMed ID: 31328578
[TBL] [Abstract][Full Text] [Related]
20. BPAGS: a web application for bacteriocin prediction via feature evaluation using alternating decision tree, genetic algorithm, and linear support vector classifier.
Akhter S; Miller JH
Front Bioinform; 2023; 3():1284705. PubMed ID: 38268970
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]