Quantitative structure-activity artificial neural network

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

Maddalena DJ. Applications of artificial neural networks to quantitative structure-activity relationships. Expert Opin Ther Patents 1996 6 239-51. 

Ghoshal, N., Mukhopadhayay, S.N., Ghoshal, T.K. and Achari, B. (1993). Quantitative Structure-Activity Relationship Studies Using Artificial Neural Networks. Indian J.Chem., 32B, 1045-1050. 

Hasegawa, K., Deushi, T., Yaegashi, O., Miyashita, Y. and Sasaki, S. (1995a). Artificial Neural Network Studies in Quantitative Structure-Activity Relationships of Antifungal Azoxy Compounds. Eur.JMed.Chem., 30, 569-574. 

Ivanciuc, O. (1996). Artificial Neural Networks Applications. 2. Using Theoretical Descriptors of Molecular Structure in Quantitative Structure-Activity Relationships Analysis of the Inhibition of Dihydrofolate Reductase. Rev.Roum.Chim., 41,645-652. 

Methods other than thermodynamic cycles are often used to calculate acid dissociation constants. Previous publications implement the theoretical relationship between pKa and structural property [6], bond valence methods and bond lengths [33], pKa correlations with highest occupied molecular orbital (HOMO) energies and frontier molecular orbitals [34], and artificial neural networks [35] to predict pKa values. In addition much work has been done using physical properties as quantitative structure-activity relationship (QSAR) descriptors, and regression equations with such descriptors to yield accurate pKa values for specific classes of molecules [36-47]. The correlation of pKas to various molecular properties, however, is often restricted to specific classes of compounds, and it is... 

In terms of practical application, expert systems overlap with systems for deriving and applying quantitative structure-activity relationship (QSAR) models or equations, and with systems using artificial neural networks (ANN) or genetic algorithms. The expert systems described in this chapter are characterized by their use of a generalized store of knowledge. 

Ivanciuc, O. (1997) Artificial neural networks applications. Part 3. A quantitative structure-activity relationship for the actinidin hydrolysis of substituted-phenyl hippurates. Rev. Roum. Chim., 42, 325-332. 

Also in chemistry artificial neural networks have found wide use. They have been used to fit spectroscopic data, to investigate quantitative structure-activity relationships (QSAR), to predict deposition rates in chemical vapor deposition, to predict binding sites of biomolecules, to derive pair potentials from diffraction data on liquids, " to solve the Schrodinger equation for simple model potentials like the harmonic oscillator, to estimate the fitness function in genetic algorithm optimizations, in experimental data analysis, to predict the secondary structure of proteins, to predict atomic energy levels, " and to solve classification problems from clinical chemistry, in particular the differentiation between diseases on the basis of characteristic laboratory data. ... 

Hansch, C. and Fujita, T. (1964) p-o-7i Analysis. A method for correlation of biological activity and chemical structure. J. Am. Chem. Soc. 86,1616. Maddalena, D. J. (1996) Applications of artificial neural networks to quantitative structure-activity relationships. Expert Opin. Ther. Pat. 6,239-251. 

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