Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks.

@article{Ghasemi2018NeuralNA,
  title={Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks.},
  author={Fahimeh Ghasemi and Alireza Mehridehnavi and Alfonso P{\'e}rez-Garrido and Horacio P{\'e}rez-S{\'a}nchez},
  journal={Drug discovery today},
  year={2018},
  volume={23 10},
  pages={
          1784-1790
        }
}
The past two decades are regarded as the golden age of using neural networks (NNs) in chemoinformatics. However, two major issues have arisen concerning their use: redundancy problems when dealing with small data sets, and the large number of compounds with thousands of descriptors, which gives rise to serious overfitting problems. Various NN algorithms, based on feature selection methods and learning algorithms, were devised to avoid these predicaments in drug discovery. Pruning the… CONTINUE READING
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