Abstract
During the last years artificial neural networks (ANN) have been applied successfully in the QSAR field. It has been demonstrated that this new technique is often superior to the traditional Hansch approach, providing more accurate predictions. The advantage of ANN is that with the presence of hidden layers, neural networks are able to perform nonlinear mapping of the physicochemical parameters and of the corresponding biological activity.
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López-Rodríguez, M.L., Rosado, M.L., Morcillo, M.J., Fernández, E., Schaper, KJ. (2000). Application of Artificial Neural Networks in QSAR of a New Model of Phenylpiperazine Derivatives1 with Affinity for 5-HT1A and αl Receptors: A Comparision of ANN Models. In: Gundertofte, K., Jørgensen, F.S. (eds) Molecular Modeling and Prediction of Bioactivity. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4141-7_113
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DOI: https://doi.org/10.1007/978-1-4615-4141-7_113
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