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  • 1
    Electronic Resource
    Electronic Resource
    New approaches to QSAR: Neural networks and machine learning (1993)
    Springer
    Perspectives in drug discovery and design 1 (1993), S. 279-290 
    Details
    ISSN: 1573-9023
    Keywords: Artificial intelligence ; Drug design
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Summary Neural networks and machine learning are two methods that are increasingly being used to model QSARs. They make few statistical assumptions and are nonlinear and nonparametric. We describe back-propagation from the field of neural networks, and GOLEM from machine learning, and illustrate their learning mechanisms using a simple expository problem. Back-propagation and GOLEM are then compared with multiple linear regression (using the parameters and their squares) on two real drug design problems: the inhibition ofEscherichia coli dihydrofolate reductase (DHFR) by pyrimidines and the inhibition of rat/mouse tumour DHFR by triazines.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02174529
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  • 2
    Electronic Resource
    Electronic Resource
    The discovery of indicator variables for QSAR using inductive logic programming (1997)
    Springer
    Journal of computer aided molecular design 11 (1997), S. 571-580 
    Details
    ISSN: 1573-4951
    Keywords: Artificial intelligence ; Machine learning ; Regression
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract A central problem in forming accurate regression equations in QSAR studies isthe selection of appropriate descriptors for the compounds under study. Wedescribe a novel procedure for using inductive logic programming (ILP) todiscover new indicator variables (attributes) for QSAR problems, and show thatthese improve the accuracy of the derived regression equations. ILP techniqueshave previously been shown to work well on drug design problems where thereis a large structural component or where clear comprehensible rules arerequired. However, ILP techniques have had the disadvantage of only being ableto make qualitative predictions (e.g. active, inactive) and not to predictreal numbers (regression). We unify ILP and linear regression techniques togive a QSAR method that has the strength of ILP at describing stericstructure, with the familiarity and power of linear regression. We evaluatedthe utility of this new QSAR technique by examining the prediction ofbiological activity with and without the addition of new structural indicatorvariables formed by ILP. In three out of five datasets examined the additionof ILP variables produced statistically better results (P 〈 0.01) over theoriginal description. The new ILP variables did not increase the overallcomplexity of the derived QSAR equations and added insight into possiblemechanisms of action. We conclude that ILP can aid in the process of drugdesign.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007967728701
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  • 3
    Electronic Resource
    Electronic Resource
    Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines (1994)
    Springer
    Journal of computer aided molecular design 8 (1994), S. 405-420 
    Details
    ISSN: 1573-4951
    Keywords: QSAR ; Artificial intelligence ; Neural networks ; DHFR inhibitors
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Summary Neural networks and inductive logic programming (ILP) have been compared to linear regression for modelling the QSAR of the inhibition of E. coli dihydrofolate reductase (DHFR) by 2,4-diamino-5-(substitured benzyl)pyrimidines, and, in the subsequent paper [Hirst, J.D., King, R.D. and Sternberg, M.J.E., J. Comput.-Aided Mol. Design, 8 (1994) 421], the inhibition of rodent DHFR by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazines. Cross-validation trials provide a statistically rigorous assessment of the predictive capabilities of the methods, with training and testing data selected randomly and all the methods developed using identical training data. For the ILP analysis, molecules are represented by attributes other than Hansch parameters. Neural networks and ILP perform better than linear regression using the attribute representation, but the difference is not statistically significant. The major benefit from the ILP analysis is the formulation of understandable rules relating the activity of the inhibitors to their chemical structure.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00125375
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  • 4
    Electronic Resource
    Electronic Resource
    Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines (1994)
    Springer
    Journal of computer aided molecular design 8 (1994), S. 421-432 
    Details
    ISSN: 1573-4951
    Keywords: QSAR ; Artificial intelligence ; Neural networks ; DHFR inhibitors
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Summary One of the largest available data sets for developing a quantitative structure-activity relationship (QSAR) — the inhibition of dihydrofolate reductase (DHFR) by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazine derivatives — has been used for a sixfold cross-validation trial of neural networks, inductive logic programming (ILP) and linear regression. No statistically significant difference was found between the predictive capabilities of the methods. However, the representation of molecules by attributes, which is integral to the ILP approach, provides understandable rules about drug-receptor interactions.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00125376
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