Revisiting the General Solubility Equation: <i>In Silico</i> Prediction of Aqueous Solubility Incorporating the Effect of Topographical Polar Surface Area

Ali, Jogoth; Camilleri, Patrick; Brown, Marc; Hutt, Andrew J.; Kirton, Stewart B.

doi:10.1021/ci200387c

Cited by 193 publications

(151 citation statements)

References 19 publications

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“…Two topological methods to predict Water Solubility are included in SwissADME. The first one is an implementation of the ESOL model36 and the second one is adapted from Ali et al 37…”

Section: One-panel-per-molecule Outputmentioning

confidence: 99%

SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

Daina

Michielin

Zoete

2017

Sci Rep

8,702

5,720

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To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.

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“…Two topological methods to predict Water Solubility are included in SwissADME. The first one is an implementation of the ESOL model36 and the second one is adapted from Ali et al 37…”

Section: One-panel-per-molecule Outputmentioning

confidence: 99%

SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

Daina

Michielin

Zoete

2017

Sci Rep

8,702

5,720

View full text Add to dashboard Cite

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“…(5). The solubility of 81% of the compounds in the dataset was accurately predicted (Ali et al 2012b).…”

Section: Solubilitymentioning

confidence: 99%

In silicoADME/T modelling for rational drug design

Wang

Xing

Yue

et al. 2015

Quart. Rev. Biophys.

246

151

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Abstract. In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.

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“…However, there is no strong relationship found within the dataset that would allow classifying the type of solubility limitation based on molecular weight only. However, based on above mentioned findings, it becomes evident that the 'General Solubility Equation' (GSE) can be successfully refined by including the PSA [7]. …”

Section: What Can We Learn From Hydrates About Solid State Contributimentioning

confidence: 99%

“…Prediction of melting point and lattice energy is currently not available at a level beyond classification (high/intermediate/low) [5]. Additionally, it has been attempted to express the contribution of crystal lattice by chemical descriptors [6]; likewise improvements to the 'General Solubility Equation' (GSE) by including the PSA have been reported [7].…”

Section: Introductionmentioning

confidence: 99%