Artificial neural networks validating

The abbreviation QSAR stands for quantitative structure-activity relationships. QSPR means quantitative structure-property relationships. As the properties of an organic compound usually cannot be predicted directly from its molecular structure, an indirect approach Is used to overcome this problem. In the first step numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical methods and artificial neural network models are used to predict the property or activity of interest, based on these descriptors or a suitable subset. A typical QSAR/QSPR study comprises the following steps structure entry or start from an existing structure database), descriptor calculation, descriptor selection, model building, model validation. 

E.P.P.A. Derks, M.L.M. Beckers, W.J. Meissen and L.M.C. Buydens, A parallel cross-validation procedure for artificial neural networks. Computers Chem., 20 (1995) 439-448. 

Chapter 17 - Vapor-liquid equilibrium (VLE) data are important for designing and modeling of process equipments. Since it is not always possible to carry out experiments at all possible temperatures and pressures, generally thermodynamic models based on equations on state are used for estimation of VLE. In this paper, an alternate tool, i.e. the artificial neural network technique has been applied for estimation of VLE for the binary systems viz. tert-butanol+2-ethyl-l-hexanol and n-butanol+2-ethyl-l-hexanol. The temperature range in which these models are valid is 353.2-458.2K at atmospheric pressure. The average absolute deviation for the temperature output was in range 2-3.3% and for the activity coefficient was less than 0.009%. The results were then compared with experimental data. 

How can we fine-tune the model Well, remember that we have prepared a different set of samples to validate the model. You can use this set to evaluate which model predicts best. If you have a sufficient number of samples, it would be even better to prepare a small testing or "control set (different from the calibration and validation sets and without a too large number of samples) to visualise which model seems best and, then, proceed with the validation set. This strategy has to be applied when developing artificial neural networks (see Chapter 5). 

Reasonable noise in the spectral data does not affect the clustering process. In this respect, cluster analysis is much more stable than other methods of multivariate analysis, such as principal component analysis (PCA), in which an increasing amount of noise is accumulated in the less relevant clusters. The mean cluster spectra can be extracted and used for the interpretation of the chemical or biochemical differences between clusters. HCA, per se, is ill-suited for a diagnostic algorithm. We have used the spectra from clusters to train artificial neural networks (ANNs), which may serve as supervised methods for final analysis. This process, which requires hundreds or thousands of spectra from each spectral class, is presently ongoing, and validated and blinded analyses, based on these efforts, will be reported. 

Cherkasov, 2005 a (79) for descriptors only Artificial neural networks (ANN)3 44 (77) Random peptides chosen according to two amino acid frequency distributions Sets A and B contained 933 and 500 peptides, respectively (see text for details, unpublished data) Training and validation within one set, independent testing on second set 1433 Set A models predicted activity with up to 83% accuracy on Set B Set B models predicted up to 43% accuracy on Set A (see text for details) nd... 

Properties such as thermodynamic values, sequence asymmetry, and polymorphisms that contribute to RNA duplex stability are taken into account by these databases (Pei and Tuschl 2006). In addition, artificial neural networks have been utilized to train algorithms based on the analysis of randomly selected siRNAs (Huesken et al. 2005). These programs siphon significant trends from large sets of RNA sequences whose efficacies are known and validated. Certain base pair (bp) positions have a tendency to possess distinct nucleotides (Figure 9.2). In effective nucleotides, position 1 is preferentially an adenosine (A) or uracil (U), and many strands are enriched with these nucleotides along the first 6 to 7 bps of sequence (Pei and Tuschl 2006). The conserved RISC cleavage site at nucleotide position 10 favors an adenosine, which may be important, while other nucleotides are... 

D 3D AD ADME ADMET ANN ARD BCI BCUT BNN C4.5 CART ClogP CoMFA CV Two dimensional Three dimensional Applicability domain Absorption, distribution metabolism, and excretion Absorption, distribution metabolism, excretion, and toxicity Artificial neural network Automatic relevance determination Bernard chemical information Burden, CAS, University of Texas descriptors Bayesian neural network Decision trees using information entropy Classification and regression tree Calculated partition coefficient between octanol and water Comparative molecular field analysis Cross-validation... 

Das, A., et al. (2003). Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network Internal and external validation of apredictive model. Lancet, 362 1261-1266. 

Artificial neural networks (ANNs) are most often used for function approximation and for an object classification even if only incomplete and noisy data are available (Rafiq et al. 2001). In structural rehability analysis the role of ANN as a universal tool for function approximation is utilized when the limit state function under consideration is complicated and computer-time consuming, cf Hurtado Alvarez (2001), Gomes Awruch (2004). Typical examples are nonlinear problems, e.g. the assessment of post-budding strength of plates or shells. The inevitable FEM calculations of strength are carried out for suitably chosen sets of training and validation patterns. A subsequent reliability analysis then can be performed by the obtained ANN approximation of the strength function. 

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