Computing the aqueous solubility of organic drug-like molecules and understanding hydrophobicity

Solubility;Sublimation (Chemistry);Melting points

McDonagh, James L. ; Mitchell, John B. O. ; Mitchell, John B. O.

University:University of St Andrews

Department:Chemistry (School of)

关键词: Solubility;    Sublimation (Chemistry);    Melting points;   

Others  :  https://research-repository.st-andrews.ac.uk/bitstream/handle/10023/6534/JamesMcDonaghPhDThesis.pdf?sequence=6&isAllowed=y

来源: DR-NTU

【 摘 要 】

This thesis covers a range of methodologies to provide an account of the current (2010-2014) state of the art and to develop new methods for solubility prediction. We focus on predictions of intrinsic aqueous solubility, as this is a measure commonlyused in many important industries including the pharmaceutical and agrochemicalindustries. These industries require fast and accurate methods, two objectiveswhich are rarely complementary. We apply machine learning in chapters 4 and5 suggesting methodologies to meet these objectives. In chapter 4 we lookto combine machine learning, cheminformatics and chemical theory. Whilst inchapter 5 we look to predict related properties to solubility and apply themto a previously derived empirical equation. We also look at ab initio (from firstprinciples) methods of solubility prediction. This is shown in chapter 3. In thischapter we present a proof of concept work that shows intrinsic aqueous solubilitypredictions, of sufficient accuracy to be used in industry, are now possible fromtheoretical chemistry using a small but diverse dataset. Chapter 6 provides asummary of our most recent research. We have begun to investigate predictionsof sublimation thermodynamics. We apply quantum chemical, lattice minimisationand machine learning techniques in this chapter.In summary, this body of work concludes that currently, QSPR/QSAR methods remain the current state of the art for solubility prediction, although it is becoming possible for purely theoretical methods to achieve useful predictions of solubility. Theoretical chemistry can offer little useful additional input to informatics models for solubility predictions. However, theoretical chemistry will be crucial for enriching our understanding of the solvation process, and can have a beneficial impact when applied to informatics predictions of properties related to solubility.