Updating molecular properties during early drug discovery
Drug Discovery Today, ISSN: 1359-6446, Vol: 22, Issue: 6, Page: 835-840
2017
- 29Citations
- 69Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations29
- Citation Indexes29
- 29
- CrossRef8
- Captures69
- Readers69
- 69
Article Description
Current multiparameter optimization (MPO) strategies make use of few experimental physicochemical descriptors (i.e., solubility at physiological pH and lipophilicity in the octanol/water system). Here, we show how new trends in drug discovery (i.e., large and flexible molecules for ‘difficult’ targets) call for the integration of ad hoc descriptors in MPO approaches. In particular, to rank, select, and optimize drug candidates, it could be relevant to have experimental data relating to the acid–base properties and the folding of the molecule to mask polar groups (so-called ‘chameleonic’ properties). We propose two strategies to quantify ionization and chameleonic properties and discuss their practical integration in property criteria profiles.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1359644616304354; http://dx.doi.org/10.1016/j.drudis.2016.11.017; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85007425423&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/27890670; https://linkinghub.elsevier.com/retrieve/pii/S1359644616304354; https://dx.doi.org/10.1016/j.drudis.2016.11.017
Elsevier BV
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