Neural networks physicochemical property

Partial Least Squares (PLS) regression (Section 35.7) is one of the more recent advances in QSAR which has led to the now widely accepted method of Comparative Molecular Field Analysis (CoMFA). This method makes use of local physicochemical properties such as charge, potential and steric fields that can be determined on a three-dimensional grid that is laid over the chemical stmctures. The determination of steric conformation, by means of X-ray crystallography or NMR spectroscopy, and the quantum mechanical calculation of charge and potential fields are now performed routinely on medium-sized molecules [10]. Modem optimization and prediction techniques such as neural networks (Chapter 44) also have found their way into QSAR. 

Quinones et al. (2000) reported the successful use of neural networks to predict the half-life of a series of 30 antihistamines. The input for the network was derived from the output of CODES, a routine that generates descriptors for a structure based on atom nature, bonding, and connectivity. Attempts to correlate the half-life with the physicochemical parameters log Kow, pKa, molecular weight, molar refractivity, molar volume, parachor, and polarity were unsuccessful. In a subsequent study by Quinones-Torrelo et al. (2001), the authors correlated the half-life of 18 antihistamines with their retention in a biopartitioning micellar chromatography system with a resultant correlation coefficient (R2adj) value of 0.89. The correlation is explained in that the retention in this system is dependent on hydrophobic, electronic, and steric properties, which are also important in determining half-life. 

Figure 2. Modeling relationships between chemical and physicochemical properties of catalyst components and their catalytical behavior by a neural network.

Log P can be used as an additional parameter, in combination with other descriptors. For example, neural network models developed by Liu and So and Goller et al use log P in combination with topological and quantum-chemical descriptors. Many methods do not use log R as a descriptor. These methods have been described in several reviews. However, there is a clear relationship between these two physicochemical properties, namely log P and aqueous solubility. 

The computational construction of artificial neural networks has also been applied to relate physicochemical parameters of benzodiazepines with their receptor affinity and to predict BZR properties and BZR ligand affinities. In a study by Maddalena and Johnston, back-propagation artificial neural networks were used to examine the QSAR between substituent constants at six positions on 57 ben-zodiazepinones with their empirically determined binding affinities (118). Among the findings of the study were the following ... 

Taskinen J, Yliruusi J. Prediction of physicochemical properties based on neural network modelling. Adv Drug Deliv Rev 2003 55 1163-83. 

Artemenko NV, Baskin II, Palyulin VA, Zefirov NS. Artificial neural network and fragmental approach in prediction of physicochemical properties of organic compounds. Russ Chem Bull 2003 52 20-9. 

Plasma area under the concentration—time curves (AUCs) of 57 NCEs were determined following oral cassette administration (5—9 NCEs/cassette) to mice. Physicochemical properties [such as, molecular weight, calculated molar refractivity, and calculated lipophilicity (clogP)] and molecular descriptors [such as presence or absence of N-methylation, cyclobutyl moiety, or heteroatoms (non-C,H,0,N)] were calculated or estimated for these compounds. This structural data, along with the corresponding pharmacokinetic parameters (primarily AUC), were used to develop artificial neural network models [8]. These models were used to predict the AUCs of compounds under synthesis [10]. This approach demonstrates that predictive models could be developed which potentially predict in vivo pharmacokinetics of NCEs under synthesis. Similar examples have been reported elsewhere [11—13]. 

Artificial neural network (AJ4N) was applied to screen effective additives of Cu/Zn based catalyst with y-alumina for high activity in one-step DME synthesis from syngas at 1 MPa, 498K. Physicochemical properties of additive X were... 

Next to ADME phenomena, recent data mining studies also focused on the development or improvement of models predicting physicochemical properties relevant to the field of ADME. Examples are Henry s law constant [92], polar surface area [93], and log P [94]. These models try to overcome limitations of already existing models, see for example SlogP [94] vs. Clogp [95], or aqueous solubility [96], The latter study used more than 2000 compounds selected from the AQUASOL [97] and PHYSOPROP [98] databases. Comparison with a multilinear regression showed clear preference for the neural network. 

Seven parameters of physicochemical properties, such as acid number, color, density, refractive index, moisture and volatility, saponification value and PV, were measured for quality and adnlter-ated soybean, as well as quality and rancid rapeseed oils. The chemometric methods were then applied for qualitative and quantitative discrimination and prediction of the oils by methods snch as exploratory principal component analysis (PCA), partial least squares (PLS), radial basis function-artificial neural networks (RBF-ANN), and multi-criteria decision making methods (MCDM), PROMETHEE and GAIA.260... 

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