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Best matches for ("artificial intelligence" OR "machine learning" OR "neural network") AND drug AND (discovery OR development):

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Items: 1 to 20 of 2495

1.

Chronic inflammation: key player and biomarker-set to predict and prevent cancer development and progression based on individualized patient profiles.

Qian S, Golubnitschaja O, Zhan X.

EPMA J. 2019 Nov 20;10(4):365-381. doi: 10.1007/s13167-019-00194-x. eCollection 2019 Dec. Review.

PMID:
31832112
2.

Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant.

Camacho MB, Vijitbenjaronk WD, Anastasio TJ.

Eur Neuropsychopharmacol. 2019 Dec 9. pii: S0924-977X(19)31742-0. doi: 10.1016/j.euroneuro.2019.11.003. [Epub ahead of print]

PMID:
31831204
3.

Application of data mining techniques and logistic regression to model drug use transition to injection: a case study in drug use treatment centers in Kermanshah Province, Iran.

Najafi-Ghobadi S, Najafi-Ghobadi K, Tapak L, Aghaei A.

Subst Abuse Treat Prev Policy. 2019 Dec 12;14(1):55. doi: 10.1186/s13011-019-0242-1.

PMID:
31831013
4.

Prediction of health care expenditure increase: how does pharmacotherapy contribute?

Jödicke AM, Zellweger U, Tomka IT, Neuer T, Curkovic I, Roos M, Kullak-Ublick GA, Sargsyan H, Egbring M.

BMC Health Serv Res. 2019 Dec 11;19(1):953. doi: 10.1186/s12913-019-4616-x.

PMID:
31829224
5.

Genetic interactions and tissue specificity modulate the association of mutations with drug response.

Cramer D, Mazur J, Espinosa O, Schlesner M, Hübschmann D, Eils R, Staub E.

Mol Cancer Ther. 2019 Dec 11. pii: molcanther.0045.2019. doi: 10.1158/1535-7163.MCT-19-0045. [Epub ahead of print]

PMID:
31826931
6.

Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test.

Abbas HT, Alic L, Erraguntla M, Ji JX, Abdul-Ghani M, Abbasi QH, Qaraqe MK.

PLoS One. 2019 Dec 11;14(12):e0219636. doi: 10.1371/journal.pone.0219636. eCollection 2019.

7.

A novel one-class classification approach to accurately predict disease-gene association in acute myeloid leukemia cancer.

Vasighizaker A, Sharma A, Dehzangi A.

PLoS One. 2019 Dec 11;14(12):e0226115. doi: 10.1371/journal.pone.0226115. eCollection 2019.

8.

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities.

Thafar M, Raies AB, Albaradei S, Essack M, Bajic VB.

Front Chem. 2019 Nov 20;7:782. doi: 10.3389/fchem.2019.00782. eCollection 2019. Review.

9.

Development of Predictive Models for Identifying Potential S100A9 Inhibitors Based on Machine Learning Methods.

Lee J, Kumar S, Lee SY, Park SJ, Kim MH.

Front Chem. 2019 Nov 25;7:779. doi: 10.3389/fchem.2019.00779. eCollection 2019.

10.

Integrative Multi-Kinase Approach for the Identification of Potent Antiplasmodial Hits.

Lima MNN, Cassiano GC, Tomaz KCP, Silva AC, Sousa BKP, Ferreira LT, Tavella TA, Calit J, Bargieri DY, Neves BJ, Costa FTM, Andrade CH.

Front Chem. 2019 Nov 21;7:773. doi: 10.3389/fchem.2019.00773. eCollection 2019.

11.

Pred-MutHTP: Prediction of disease-causing and neutral mutations in human transmembrane proteins.

Kulandaisamy A, Zaucha J, Sakthivel R, Frishman D, Michael Gromiha M.

Hum Mutat. 2019 Dec 10. doi: 10.1002/humu.23961. [Epub ahead of print]

PMID:
31821684
12.

Comprehensive Exploration of Target-specific Ligands Using a Graph Convolution Neural Network.

Miyazaki Y, Ono N, Huang M, Altaf-Ul-Amin M, Kanaya S.

Mol Inform. 2019 Dec 9. doi: 10.1002/minf.201900095. [Epub ahead of print]

PMID:
31815371
13.

Potential mechanisms for phencyclidine/ketamine-induced brain structural alterations and behavioral consequences.

Wang C, Inselman A, Liu S, Liu F.

Neurotoxicology. 2019 Dec 5. pii: S0161-813X(19)30144-5. doi: 10.1016/j.neuro.2019.12.005. [Epub ahead of print] Review.

PMID:
31812709
14.

The NAD+-mitophagy axis in healthy longevity and in artificial intelligence-based clinical applications.

Aman Y, Frank J, Lautrup SH, Matysek A, Niu Z, Yang G, Shi L, Bergersen LH, Storm-Mathisen J, Rasmussen LJ, Bohr VA, Nilsen H, Fang EF.

Mech Ageing Dev. 2019 Dec 5:111194. doi: 10.1016/j.mad.2019.111194. [Epub ahead of print]

PMID:
31812486
15.

Immunotherapy for hepatocellular carcinoma.

Zongyi Y, Xiaowu L.

Cancer Lett. 2019 Dec 4;470:8-17. doi: 10.1016/j.canlet.2019.12.002. [Epub ahead of print]

PMID:
31811905
16.

The pathogenesis of systemic lupus erythematosus: Harnessing big data to understand the molecular basis of lupus.

Catalina MD, Owen KA, Labonte AC, Grammer AC, Lipsky PE.

J Autoimmun. 2019 Dec 2:102359. doi: 10.1016/j.jaut.2019.102359. [Epub ahead of print] Review.

PMID:
31806421
17.

Discovery of small-molecule inhibitors targeting the ribosomal peptidyl transferase center (PTC) of M. tuberculosis.

Tam B, Sherf D, Cohen S, Eisdorfer SA, Perez M, Soffer A, Vilenchik D, Akabayov SR, Wagner G, Akabayov B.

Chem Sci. 2019 Aug 6;10(38):8764-8767. doi: 10.1039/c9sc02520k. eCollection 2019 Oct 14.

18.

Is it time for artificial intelligence to predict the function of natural products based on 2D-structure.

Liu M, Karuso P, Feng Y, Kellenberger E, Liu F, Wang C, Quinn RJ.

Medchemcomm. 2019 Jun 6;10(10):1667-1677. doi: 10.1039/c9md00128j. eCollection 2019 Oct 1. Review.

PMID:
31803392
19.

Validation study of QSAR/DNN models using the competition datasets.

Kato Y, Hamada S, Goto H.

Mol Inform. 2019 Dec 5. doi: 10.1002/minf.201900154. [Epub ahead of print]

PMID:
31802634
20.

Rethinking drug design in the artificial intelligence era.

Schneider P, Walters WP, Plowright AT, Sieroka N, Listgarten J, Goodnow RA Jr, Fisher J, Jansen JM, Duca JS, Rush TS, Zentgraf M, Hill JE, Krutoholow E, Kohler M, Blaney J, Funatsu K, Luebkemann C, Schneider G.

Nat Rev Drug Discov. 2019 Dec 4. doi: 10.1038/s41573-019-0050-3. [Epub ahead of print] Review.

PMID:
31801986

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