Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 697 to 705 of 705 entries
Sorted by: Best Match Show Resources per page
Rewarding innovation in drug discovery.

Health affairs (Project Hope)

Lopes G.
PMID: 19414906
Health Aff (Millwood). 2009 May-Jun;28(3):922-3. doi: 10.1377/hlthaff.28.3.922-a.

No abstract available.

Cite
Lopes G. Rewarding innovation in drug discovery. Health Aff (Millwood). 2009;28(3):922-3doi: 10.1377/hlthaff.28.3.922-a.
Lopes, G. (2009). Rewarding innovation in drug discovery. Health affairs (Project Hope), 28(3), 922-3. https://doi.org/10.1377/hlthaff.28.3.922-a
Lopes, Gilberto. "Rewarding innovation in drug discovery." Health affairs (Project Hope) vol. 28,3 (2009): 922-3. doi: https://doi.org/10.1377/hlthaff.28.3.922-a
Lopes G. Rewarding innovation in drug discovery. Health Aff (Millwood). 2009 May-Jun;28(3):922-3. doi: 10.1377/hlthaff.28.3.922-a. PMID: 19414906.
Copy Download .nbib Format: NLM
Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding.

Bioinformatics (Oxford, England)

Jang G, Park S, Lee S, Kim S, Park S, Kang J.
PMID: 34252937
Bioinformatics. 2021 Jul 12;37:i376-i382. doi: 10.1093/bioinformatics/btab275.

MOTIVATION: Identifying mechanism of actions (MoA) of novel compounds is crucial in drug discovery. Careful understanding of MoA can avoid potential side effects of drug candidates. Efforts have been made to identify MoA using the transcriptomic signatures induced by...

Cite
Jang G, Park S, Lee S, et al. Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding. Bioinformatics. 2021;37:i376-i382doi: 10.1093/bioinformatics/btab275.
Jang, G., Park, S., Lee, S., Kim, S., Park, S., & Kang, J. (2021). Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding. Bioinformatics (Oxford, England), 37i376-i382. https://doi.org/10.1093/bioinformatics/btab275
Jang, Gwanghoon, et al. "Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding." Bioinformatics (Oxford, England) vol. 37 (2021): i376-i382. doi: https://doi.org/10.1093/bioinformatics/btab275
Jang G, Park S, Lee S, Kim S, Park S, Kang J. Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding. Bioinformatics. 2021 Jul 12;37:i376-i382. doi: 10.1093/bioinformatics/btab275. PMID: 34252937; PMCID: PMC8275331.
Copy Download .nbib Format: NLM
Predicting Drug-Target Interactions With Multi-Label Classification and Label Partitioning.

IEEE/ACM transactions on computational biology and bioinformatics

Pliakos K, Vens C, Tsoumakas G.
PMID: 31689203
IEEE/ACM Trans Comput Biol Bioinform. 2021 Jul-Aug;18(4):1596-1607. doi: 10.1109/TCBB.2019.2951378. Epub 2021 Aug 06.

Identifying drug-target interactions is crucial for drug discovery. Despite modern technologies used in drug screening, experimental identification of drug-target interactions is an extremely demanding task. Predicting drug-target interactions in silico can thereby facilitate drug discovery as well as drug...

Cite
Pliakos K, Vens C, Tsoumakas G. Predicting Drug-Target Interactions With Multi-Label Classification and Label Partitioning. IEEE/ACM Trans Comput Biol Bioinform. 2021;18(4):1596-1607doi: 10.1109/TCBB.2019.2951378.
Pliakos, K., Vens, C., & Tsoumakas, G. (2021). Predicting Drug-Target Interactions With Multi-Label Classification and Label Partitioning. IEEE/ACM transactions on computational biology and bioinformatics, 18(4), 1596-1607. https://doi.org/10.1109/TCBB.2019.2951378
Pliakos, Konstantinos, et al. "Predicting Drug-Target Interactions With Multi-Label Classification and Label Partitioning." IEEE/ACM transactions on computational biology and bioinformatics vol. 18,4 (2021): 1596-1607. doi: https://doi.org/10.1109/TCBB.2019.2951378
Pliakos K, Vens C, Tsoumakas G. Predicting Drug-Target Interactions With Multi-Label Classification and Label Partitioning. IEEE/ACM Trans Comput Biol Bioinform. 2021 Jul-Aug;18(4):1596-1607. doi: 10.1109/TCBB.2019.2951378. Epub 2021 Aug 06. PMID: 31689203.
Copy Download .nbib Format: NLM
Idiopathic pulmonary fibrosis: Disease mechanisms and drug development.

Pharmacology & therapeutics

Spagnolo P, Kropski JA, Jones MG, Lee JS, Rossi G, Karampitsakos T, Maher TM, Tzouvelekis A, Ryerson CJ.
PMID: 33359599
Pharmacol Ther. 2021 Jun;222:107798. doi: 10.1016/j.pharmthera.2020.107798. Epub 2020 Dec 24.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive disease of unknown cause characterized by relentless scarring of the lung parenchyma leading to reduced quality of life and earlier mortality. IPF is an age-related disorder, and with the population aging...

Cite
Spagnolo P, Kropski JA, Jones MG, et al. Idiopathic pulmonary fibrosis: Disease mechanisms and drug development. Pharmacol Ther. 2020;222:107798doi: 10.1016/j.pharmthera.2020.107798.
Spagnolo, P., Kropski, J. A., Jones, M. G., Lee, J. S., Rossi, G., Karampitsakos, T., Maher, T. M., Tzouvelekis, A., & Ryerson, C. J. (2021). Idiopathic pulmonary fibrosis: Disease mechanisms and drug development. Pharmacology & therapeutics, 222107798. https://doi.org/10.1016/j.pharmthera.2020.107798
Spagnolo, Paolo, et al. "Idiopathic pulmonary fibrosis: Disease mechanisms and drug development." Pharmacology & therapeutics vol. 222 (2021): 107798. doi: https://doi.org/10.1016/j.pharmthera.2020.107798
Spagnolo P, Kropski JA, Jones MG, Lee JS, Rossi G, Karampitsakos T, Maher TM, Tzouvelekis A, Ryerson CJ. Idiopathic pulmonary fibrosis: Disease mechanisms and drug development. Pharmacol Ther. 2021 Jun;222:107798. doi: 10.1016/j.pharmthera.2020.107798. Epub 2020 Dec 24. PMID: 33359599; PMCID: PMC8142468.
Copy Download .nbib Format: NLM
SARS-CoV-2 Drug Discovery based on Intrinsically Disordered Regions.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

Mudide A, Alterovitz G.
PMID: 33691011
Pac Symp Biocomput. 2021;26:131-142.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a close relative of SARS-CoV-1, causes coronavirus disease 2019 (COVID-19), which, at the time of writing, has spread to over 19.9 million people worldwide. In this work, we aim to discover drugs...

Cite
Mudide A, Alterovitz G. SARS-CoV-2 Drug Discovery based on Intrinsically Disordered Regions. Pac Symp Biocomput. 2021;26:131-142
Mudide, A., & Alterovitz, G. (2021). SARS-CoV-2 Drug Discovery based on Intrinsically Disordered Regions. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 26131-142.
Mudide, Anish, and Alterovitz, Gil. "SARS-CoV-2 Drug Discovery based on Intrinsically Disordered Regions." Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing vol. 26 (2021): 131-142.
Mudide A, Alterovitz G. SARS-CoV-2 Drug Discovery based on Intrinsically Disordered Regions. Pac Symp Biocomput. 2021;26:131-142. PMID: 33691011.
Copy Download .nbib Format: NLM
Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities.

The AAPS journal

Terranova N, Venkatakrishnan K, Benincosa LJ.
PMID: 34008139
AAPS J. 2021 May 18;23(4):74. doi: 10.1208/s12248-021-00593-x.

The exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artificial intelligence (AI) and machine learning (ML). Patient-centered...

Cite
Terranova N, Venkatakrishnan K, Benincosa LJ. Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities. AAPS J. 2021;23(4):74doi: 10.1208/s12248-021-00593-x.
Terranova, N., Venkatakrishnan, K., & Benincosa, L. J. (2021). Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities. The AAPS journal, 23(4), 74. https://doi.org/10.1208/s12248-021-00593-x
Terranova, Nadia, et al. "Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities." The AAPS journal vol. 23,4 (2021): 74. doi: https://doi.org/10.1208/s12248-021-00593-x
Terranova N, Venkatakrishnan K, Benincosa LJ. Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities. AAPS J. 2021 May 18;23(4):74. doi: 10.1208/s12248-021-00593-x. PMID: 34008139; PMCID: PMC8130984.
Copy Download .nbib Format: NLM
In silico methods and tools for drug discovery.

Computers in biology and medicine

Shaker B, Ahmad S, Lee J, Jung C, Na D.
PMID: 34520990
Comput Biol Med. 2021 Oct;137:104851. doi: 10.1016/j.compbiomed.2021.104851. Epub 2021 Sep 08.

In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in...

Cite
Shaker B, Ahmad S, Lee J, et al. In silico methods and tools for drug discovery. Comput Biol Med. 2021;137:104851doi: 10.1016/j.compbiomed.2021.104851.
Shaker, B., Ahmad, S., Lee, J., Jung, C., & Na, D. (2021). In silico methods and tools for drug discovery. Computers in biology and medicine, 137104851. https://doi.org/10.1016/j.compbiomed.2021.104851
Shaker, Bilal, et al. "In silico methods and tools for drug discovery." Computers in biology and medicine vol. 137 (2021): 104851. doi: https://doi.org/10.1016/j.compbiomed.2021.104851
Shaker B, Ahmad S, Lee J, Jung C, Na D. In silico methods and tools for drug discovery. Comput Biol Med. 2021 Oct;137:104851. doi: 10.1016/j.compbiomed.2021.104851. Epub 2021 Sep 08. PMID: 34520990.
Copy Download .nbib Format: NLM
Toward an Understanding of Pan-Assay Interference Compounds and Promiscuity: A Structural Perspective on Binding Modes.

Journal of chemical information and modeling

Bolz SN, Adasme MF, Schroeder M.
PMID: 33899463
J Chem Inf Model. 2021 May 24;61(5):2248-2262. doi: 10.1021/acs.jcim.0c01227. Epub 2021 Apr 26.

Pan-assay interference compounds (PAINS) are promiscuous compound classes that produce false positive hits in high-throughput screenings. Yet, the mechanisms of PAINS activity are poorly understood. Although PAINS are often associated with protein reactivity, several recent studies have shown that...

Cite
Bolz SN, Adasme MF, Schroeder M. Toward an Understanding of Pan-Assay Interference Compounds and Promiscuity: A Structural Perspective on Binding Modes. J Chem Inf Model. 2021;61(5):2248-2262doi: 10.1021/acs.jcim.0c01227.
Bolz, S. N., Adasme, M. F., & Schroeder, M. (2021). Toward an Understanding of Pan-Assay Interference Compounds and Promiscuity: A Structural Perspective on Binding Modes. Journal of chemical information and modeling, 61(5), 2248-2262. https://doi.org/10.1021/acs.jcim.0c01227
Bolz, Sarah Naomi, et al. "Toward an Understanding of Pan-Assay Interference Compounds and Promiscuity: A Structural Perspective on Binding Modes." Journal of chemical information and modeling vol. 61,5 (2021): 2248-2262. doi: https://doi.org/10.1021/acs.jcim.0c01227
Bolz SN, Adasme MF, Schroeder M. Toward an Understanding of Pan-Assay Interference Compounds and Promiscuity: A Structural Perspective on Binding Modes. J Chem Inf Model. 2021 May 24;61(5):2248-2262. doi: 10.1021/acs.jcim.0c01227. Epub 2021 Apr 26. PMID: 33899463.
Copy Download .nbib Format: NLM
On failure modes in molecule generation and optimization.

Drug discovery today. Technologies

Renz P, Van Rompaey D, Wegner JK, Hochreiter S, Klambauer G.
PMID: 33386095
Drug Discov Today Technol. 2019 Dec;32:55-63. doi: 10.1016/j.ddtec.2020.09.003. Epub 2020 Oct 24.

There has been a wave of generative models for molecules triggered by advances in the field of Deep Learning. These generative models are often used to optimize chemical compounds towards particular properties or a desired biological activity. The evaluation...

Cite
Renz P, Van Rompaey D, Wegner JK, et al. On failure modes in molecule generation and optimization. Drug Discov Today Technol. 2020;32:55-63doi: 10.1016/j.ddtec.2020.09.003.
Renz, P., Van Rompaey, D., Wegner, J. K., Hochreiter, S., & Klambauer, G. (2019). On failure modes in molecule generation and optimization. Drug discovery today. Technologies, 3255-63. https://doi.org/10.1016/j.ddtec.2020.09.003
Renz, Philipp, et al. "On failure modes in molecule generation and optimization." Drug discovery today. Technologies vol. 32 (2019): 55-63. doi: https://doi.org/10.1016/j.ddtec.2020.09.003
Renz P, Van Rompaey D, Wegner JK, Hochreiter S, Klambauer G. On failure modes in molecule generation and optimization. Drug Discov Today Technol. 2019 Dec;32:55-63. doi: 10.1016/j.ddtec.2020.09.003. Epub 2020 Oct 24. PMID: 33386095.
Copy Download .nbib Format: NLM
Showing 697 to 705 of 705 entries