Recurrent Neural Networks (RNNs) trained with a set of molecules represented as unique (canonical) SMILES strings, have shown the capacity to create large chemical spaces valid and meaningful structures. Herein we perform an extensive benchmark on models subsets GDB-13 different sizes (1 million, 10,000 1000), variants (canonical, randomized DeepSMILES), two recurrent cell types (LSTM GRU) hyperparameter combinations. To guide benchmarks new metrics were developed that define how well model...

Deep learning methods applied to drug discovery have been used generate novel structures. In this study, we propose a new deep architecture, LatentGAN, which combines an autoencoder and generative adversarial neural network for de novo molecular design. We the method in two scenarios: one random drug-like compounds another target-biased compounds. Our results show that works well both cases. Sampled from trained model can largely occupy same chemical space as training set also substantial...

Recurrent Neural Networks (RNNs) trained with a set of molecules represented as unique (canonical) SMILES strings, have shown the capacity to create large chemical spaces valid and meaningful structures. Herein we perform an extensive benchmark on models subsets GDB-13 different sizes (1 million , 10,000 1,000), variants (canonical, randomized DeepSMILES), two recurrent cell types (LSTM GRU) hyperparameter combinations. To guide benchmarks new metrics were developed that define generated...

<p> </p><p>Deep learning methods applied to drug discovery have been used generate novel structures. In this study, we propose a new deep architecture, LatentGAN, which combines an autoencoder and generative adversarial neural network for de novo molecular design. We the method in two scenarios: one random drug-like compounds another target-biased compounds. Our results show that works well both cases: sampled from trained model can largely occupy same chemical space as...

Deep learning methods applied to drug discovery have been used generate novel structures. In this study, we propose a new deep architecture, LatentGAN, which combines an autoencoder and generative adversarial neural network for de novo molecular design. We the method in two scenarios: one random drug-like compounds another target-biased compounds. Our results show that works well both cases: sampled from trained model can largely occupy same chemical space as training set also substantial...

Recurrent Neural Networks (RNNs) trained with a set of molecules represented as unique (canonical) SMILES strings, have shown the capacity to create large chemical spaces valid and meaningful structures. Herein we perform an extensive benchmark on models subsets GDB-13 different sizes (1 million , 10,000 1,000), variants (canonical, randomized DeepSMILES), two recurrent cell types (LSTM GRU) hyperparameter combinations. To guide benchmarks new metrics were developed that define generated...

Deep learning methods applied to drug discovery have been used generate novel structures. In this study, we propose a new deep architecture, LatentGAN, which combines an autoencoder and generative adversarial neural network for de novo molecular design. We the method in two scenarios: one random drug-like compounds another target-biased compounds. Our results show that works well both cases: sampled from trained model can largely occupy same chemical space as training set also substantial...

Recently deep learning method has been used for generating novel structures. In the current study, we proposed a new method, LatentGAN, which combine an autoencoder and generative adversarial neural network doing de novo molecule design. We applied structure generation in two scenarios, one is to generate random drug-like compounds other target biased compounds. Our results show that works well both cases, sampled from trained model can largely occupy same chemical space of training set...

Recently deep learning method has been used for generating novel structures. In the current study, we proposed a new method, LatentGAN, which combine an autoencoder and generative adversarial neural network doing de novo molecule design. We applied structure generation in two scenarios, one is to generate random drug-like compounds other target biased compounds. Our results show that works well both cases, sampled from trained model can largely occupy same chemical space of training set...

In recent years, deep learning for de novo molecular generation has become a rapidly growing research area. Recurrent neural networks (RNN) using the SMILES representation is one of most common approaches used. Recent study shows that differentiable computer (DNC) can make considerable improvement over RNN modeling sequential data. current study, DNC been implemented as an extension to REINVENT, RNN-based model already used successfully design. The was benchmarked on its capacity learn...

Recurrent Neural Networks (RNNs) trained with a set of molecules represented as unique (canonical) SMILES strings, have shown the capacity to create large chemical spaces valid and meaningful structures. Herein we perform an extensive benchmark on models subsets GDB-13 different sizes (1 million , 10,000 1,000), variants (canonical, randomized DeepSMILES), two recurrent cell types (LSTM GRU) hyperparameter combinations. To guide benchmarks new metrics were developed that define generated...