Amini, Ata published the artcileA Novel Logic-Based Approach for Quantitative Toxicology Prediction, SDS of cas: 2447-79-2, the publication is Journal of Chemical Information and Modeling (2007), 47(3), 998-1006, database is CAplus and MEDLINE.
There is a pressing need for accurate in silico methods to predict the toxicity of mols. that are being introduced into the environment or are being developed into new pharmaceuticals. Predictive toxicol. is in the realm of structure activity relationships (SAR), and many approaches have been used to derive such SAR. Previous work has shown that inductive logic programming (ILP) is a powerful approach that circumvents several major difficulties, such as mol. superposition, faced by some other SAR methods. The ILP approach reasons with chem. substructures within a relational framework and yields chem. understandable rules. Here, we report a general new approach, support vector inductive logic programming (SVILP), which extends the essentially qual. ILP-based SAR to quant. modeling. First, ILP is used to learn rules, the predictions of which are then used within a novel kernel to derive a support-vector generalization model. For a highly heterogeneous dataset of 576 mols. with known fathead minnow fish toxicity, the cross-validated correlation coefficients (R2CV) from a chem. descriptor method (CHEM) and SVILP are 0.52 and 0.66, resp. The ILP, CHEM, and SVILP approaches correctly predict 55, 58, and 73%, resp., of toxic mols. In a set of 165 unseen mols., the R2 values from the com. software TOPKAT and SVILP are 0.26 and 0.57, resp. In all calculations, SVILP showed significant improvements in comparison with the other methods. The SVILP approach has a major advantage in that it uses ILP automatically and consistently to derive rules, mostly novel, describing fragments that are toxicity alerts. The SVILP is a general machine-learning approach and has the potential of tackling many problems relevant to chemoinformatics including in silico drug design.
Journal of Chemical Information and Modeling published new progress about 2447-79-2. 2447-79-2 belongs to amides-buliding-blocks, auxiliary class Chloride,Amine,Benzene,Amide, name is 2,4-Dichlorobenzamide, and the molecular formula is C7H5Cl2NO, SDS of cas: 2447-79-2.
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