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
        }
}
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131 Citations
Highly Influential Citations
4
Background Citations
25
Methods Citations
15

131 Citations

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The power of deep learning to ligand-based novel drug discovery

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Deep learning in drug discovery: opportunities, challenges and future prospects.

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Descriptor Free QSAR Modeling Using Deep Learning With Long Short-Term Memory Neural Networks

It is possible to build QSAR models using LSTMs without using pre-computed traditional descriptors, and models are far from being “black box,” according to this study.

Deep Learning Techniques and COVID-19 Drug Discovery: Fundamentals, State-of-the-Art and Future Directions

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