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Relevant bibliographies by topics / In silico methodologies

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Journal articles

Academic literature on the topic 'In silico methodologies'

Author: Grafiati

Published: 14 March 2023

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Journal articles on the topic "In silico methodologies"

1

Hasan, Doaa Mohamed, Ahmed Sharaf Eldin, Ayman Elsayed Khedr, and Hanan Fahmy. "In-Silico Methodologies for Cancer Multidrug Optimization." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 17, no. 2 (2018): 7186–205. http://dx.doi.org/10.24297/ijct.v17i2.7168.

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Drug combinations is considered as an effective strategy designed to control complex diseases like cancer. Combinations of drugs can effectively decrease side effects and enhance adaptive resistance. Therefore, increasing the likelihood of defeating complex diseases in a synergistic way. This is due to overcoming factors such as off-target activities, network robustness, bypass mechanisms, cross-talk across compensatory escape pathways and the mutational heterogeneity which results in alterations within multiple molecular pathways. The plurality of effective drug combinations used in clinic were found out through experience. The molecular mechanisms underlying these drug combinations are often not clear, which makes it not easy to suggest new drug combinations. Computational approaches are proposed to reduce the search space for defining the most promising combinations and prioritizing their experimental evaluation. In this paper, we review methods, techniques and hypotheses developed for in silico methodologies for drug combination discovery in cancer, and discuss the limitations and challenges of these methods.

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Scotti, Luciana, Jahan Ghasemi, and Marcus T. Scotti. "Editorial: In Silico Methodologies Applied to Drug Discovery." Combinatorial Chemistry & High Throughput Screening 21, no. 3 (2018): 150–51. http://dx.doi.org/10.2174/138620732103180423125817.

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Scotti, Luciana, and Marcus T. Scotti. "In Silico Methodologies Applied to Anti-infections Drug Discovery." Combinatorial Chemistry & High Throughput Screening 23, no. 6 (2020): 456–57. http://dx.doi.org/10.2174/138620732306200612101828.

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4

Remtulla, Raheem, Sanjoy Kumar Das, and Leonard A. Levin. "Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning." Molecules 26, no. 9 (2021): 2505. http://dx.doi.org/10.3390/molecules26092505.

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Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.

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Uysal, Sengul, Abdurrahman Aktumsek, Carene M. N. Picot, et al. "A comparative in vitro and in silico study of the biological potential and chemical fingerprints of Dorcycinum pentapyllum subsp. haussknechtii using three extraction procedures." New Journal of Chemistry 41, no. 22 (2017): 13952–60. http://dx.doi.org/10.1039/c7nj03497k.

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6

Moura, Ana S., Amit K. Halder, and M. Natália DS Cordeiro. "From biomedicinal to in silico models and back to therapeutics: a review on the advancement of peptidic modeling." Future Medicinal Chemistry 11, no. 17 (2019): 2313–31. http://dx.doi.org/10.4155/fmc-2018-0365.

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Bioactive peptides participate in numerous metabolic functions of living organisms and have emerged as potential therapeutics on a diverse range of diseases. Albeit peptide design does not go without challenges, overwhelming advancements on in silico methodologies have increased the scope of peptide-based drug design and discovery to an unprecedented amount. Within an in silico model versus an experimental validation scenario, this review aims to summarize and discuss how different in silico techniques contribute at present to the design of peptide-based molecules. Published in silico results from 2014 to 2018 were selected and discriminated in major methodological groups, allowing a transversal analysis, promoting a landscape vision and asserting its increasing value in drug design.

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7

Ball, Nicholas, Remi Bars, Philip A. Botham, et al. "A framework for chemical safety assessment incorporating new approach methodologies within REACH." Archives of Toxicology 96, no. 3 (2022): 743–66. http://dx.doi.org/10.1007/s00204-021-03215-9.

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AbstractThe long-term investment in new approach methodologies (NAMs) within the EU and other parts of the world is beginning to result in an emerging consensus of how to use information from in silico, in vitro and targeted in vivo sources to assess the safety of chemicals. However, this methodology is being adopted very slowly for regulatory purposes. Here, we have developed a framework incorporating in silico, in vitro and in vivo methods designed to meet the requirements of REACH in which both hazard and exposure can be assessed using a tiered approach. The outputs from each tier are classification categories, safe doses, and risk assessments, and progress through the tiers depends on the output from previous tiers. We have exemplified the use of the framework with three examples. The outputs were the same or more conservative than parallel assessments based on conventional studies. The framework allows a transparent and phased introduction of NAMs in chemical safety assessment and enables science-based safety decisions which provide the same level of public health protection using fewer animals, taking less time, and using less financial and expert resource. Furthermore, it would also allow new methods to be incorporated as they develop through continuous selective evolution rather than periodic revolution.

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8

Gimeno, Aleix, María Ojeda-Montes, Sarah Tomás-Hernández, et al. "The Light and Dark Sides of Virtual Screening: What Is There to Know?" International Journal of Molecular Sciences 20, no. 6 (2019): 1375. http://dx.doi.org/10.3390/ijms20061375.

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Virtual screening consists of using computational tools to predict potentially bioactive compounds from files containing large libraries of small molecules. Virtual screening is becoming increasingly popular in the field of drug discovery as in silico techniques are continuously being developed, improved, and made available. As most of these techniques are easy to use, both private and public organizations apply virtual screening methodologies to save resources in the laboratory. However, it is often the case that the techniques implemented in virtual screening workflows are restricted to those that the research team knows. Moreover, although the software is often easy to use, each methodology has a series of drawbacks that should be avoided so that false results or artifacts are not produced. Here, we review the most common methodologies used in virtual screening workflows in order to both introduce the inexperienced researcher to new methodologies and advise the experienced researcher on how to prevent common mistakes and the improper usage of virtual screening methodologies.

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9

Kothandan, Gugan, Changdev G. Gadhe, Thirumurthy Madhavan, and Seung J. Cho. "Binding Site Analysis of CCR2 Through In Silico Methodologies: Docking, CoMFA, and CoMSIA." Chemical Biology & Drug Design 78, no. 1 (2011): 161–74. http://dx.doi.org/10.1111/j.1747-0285.2011.01095.x.

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10

Gadhe, Changdev G., Gugan Kothandan, and Seung Joo Cho. "Binding site exploration of CCR5 using in silico methodologies: a 3D-QSAR approach." Archives of Pharmacal Research 36, no. 1 (2013): 6–31. http://dx.doi.org/10.1007/s12272-013-0001-1.

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