scholarly journals Understanding the Differences Between Cocrystal and Salt Aqueous Solubilities

2018 ◽  
Vol 107 (1) ◽  
pp. 113-120 ◽  
Author(s):  
Katie L. Cavanagh ◽  
Chinmay Maheshwari ◽  
Naír Rodríguez-Hornedo
Keyword(s):  
Aqueous Solubilities

scholarly journals Asymmetric synthesis of host-directed inhibitors of myxoviruses

2013 ◽  
Vol 9 ◽  
pp. 197-203 ◽  
Author(s):  
Terry W Moore ◽  
Kasinath Sana ◽  
Dan Yan ◽  
Pahk Thepchatri ◽  
John M Ndungu ◽  
...  
Keyword(s):  
Antiviral Activity ◽  
Asymmetric Synthesis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Aqueous Solubility ◽  
Influenza Viruses ◽  
Lead Compound ◽  
Synthetic Strategy ◽  
Aqueous Solubilities ◽  
Nanomolar Range

High-throughput screening (HTS) previously identified benzimidazole 1 (JMN3-003) as a compound with broad antiviral activity against different influenza viruses and paramyxovirus strains. In pursuit of a lead compound from this series for development, we sought to increase both the potency and the aqueous solubility of 1. Lead optimization has achieved compounds with potent antiviral activity against a panel of myxovirus family members (EC50 values in the low nanomolar range) and much improved aqueous solubilities relative to that of 1. Additionally, we have devised a robust synthetic strategy for preparing 1 and congeners in an enantio-enriched fashion, which has allowed us to demonstrate that the (S)-enantiomers are generally 7- to 110-fold more potent than the corresponding (R)-isomers.

scholarly journals Three machine learning models for the 2019 Solubility Challenge

ADMET & DMPK ◽  
2020 ◽  
Author(s):  
John Mitchell
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gold Standard ◽  
Challenge Test ◽  
Wisdom Of Crowds ◽  
Learning Models ◽  
The Third ◽  
Aqueous Solubilities ◽  
Machine Learning Models ◽  
Better Than

<p class="ADMETabstracttext">We describe three machine learning models submitted to the 2019 Solubility Challenge. All are founded on tree-like classifiers, with one model being based on Random Forest and another on the related Extra Trees algorithm. The third model is a consensus predictor combining the former two with a Bagging classifier. We call this consensus classifier Vox Machinarum, and here discuss how it benefits from the Wisdom of Crowds. On the first 2019 Solubility Challenge test set of 100 low-variance intrinsic aqueous solubilities, Extra Trees is our best classifier. One the other, a high-variance set of 32 molecules, we find that Vox Machinarum and Random Forest both perform a little better than Extra Trees, and almost equally to one another. We also compare the gold standard solubilities from the 2019 Solubility Challenge with a set of literature-based solubilities for most of the same compounds.</p>

Aqueous solubilities of chlorinated phenols at 25 .degree.C

1993 ◽  
Vol 38 (3) ◽  
pp. 364-366 ◽  
Author(s):  
Kuo Ching Ma ◽  
Wan Ying Shiu ◽  
Donald Mackay
Keyword(s):  
Chlorinated Phenols ◽  
Aqueous Solubilities

Vapour pressures, aqueous solubilities, Henry’s Law constants, and octanol/water partition coefficients of a series of mixed halogenated dimethyl bipyrroles

Chemosphere ◽  
2004 ◽  
Vol 57 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Sheryl A. Tittlemier ◽  
Eric Braekevelt ◽  
Thor Halldorson ◽  
Christopher M. Reddy ◽  
Ross J. Norstrom
Keyword(s):  
Partition Coefficients ◽  
Henry's Law ◽  
Henry’S Law ◽  
Henry’S Law Constants ◽  
Henry's Law Constants ◽  
Aqueous Solubilities
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