scholarly journals Comparison Between SMILES-Based Differential Neural Computer and Recurrent Neural Network Architectures for De Novo Molecule Design

2019 ◽  
Author(s):  
Simon Johansson ◽  
Oleksii Ptykhodko ◽  
Josep Arús-Pous ◽  
Ola Engkvist ◽  
Hongming Chen
Keyword(s):  
De Novo ◽  
Molecular Design ◽  
Research Area ◽  
Computational Time ◽  
Test Cases ◽  
Sequential Data ◽  
Network Architectures ◽  
De Novo Molecular Design ◽  
Molecule Design ◽  
The Cost

In recent years, deep learning for de novo molecular generation has become a rapidly growing research area. Recurrent neural networks (RNN) using the SMILES molecular representation is one of the most common approaches used. Recent study shows that the differentiable neural computer (DNC) can make considerable improvement over the RNN for modeling of sequential data. In the current study, DNC has been implemented as an extension to REINVENT, an RNN-based model that has already been used successfully to make de novo molecular design. The model was benchmarked on its capacity to learn the SMILES language on the GDB-13 and MOSES datasets. The DNC shows improvement on all test cases conducted at the cost of significantly increased computational time and memory consumption.

scholarly journals Comparison Between SMILES-Based Differential Neural Computer and Recurrent Neural Network Architectures for De Novo Molecule Design

2019 ◽  
Author(s):  
Simon Johansson ◽  
Oleksii Ptykhodko ◽  
Josep Arús-Pous ◽  
Ola Engkvist ◽  
Hongming Chen
Keyword(s):  
De Novo ◽  
Molecular Design ◽  
Research Area ◽  
Computational Time ◽  
Test Cases ◽  
Sequential Data ◽  
Network Architectures ◽  
De Novo Molecular Design ◽  
Molecule Design ◽  
The Cost

In recent years, deep learning for de novo molecular generation has become a rapidly growing research area. Recurrent neural networks (RNN) using the SMILES molecular representation is one of the most common approaches used. Recent study shows that the differentiable neural computer (DNC) can make considerable improvement over the RNN for modeling of sequential data. In the current study, DNC has been implemented as an extension to REINVENT, an RNN-based model that has already been used successfully to make de novo molecular design. The model was benchmarked on its capacity to learn the SMILES language on the GDB-13 and MOSES datasets. The DNC shows improvement on all test cases conducted at the cost of significantly increased computational time and memory consumption.

scholarly journals Attention-based generative models for de novo molecular design

2021 ◽  
Author(s):  
Orion Dollar ◽  
Nisarg Joshi ◽  
David Beck ◽  
Jim Pfaendtner
Keyword(s):  
De Novo ◽  
Molecular Design ◽  
Data Modeling ◽  
Generative Models ◽  
Sequential Data ◽  
De Novo Molecular Design ◽  
Generative Algorithms ◽  
The Impact

Attention mechanisms have led to many breakthroughs in sequential data modeling but have yet to be incorporated into any generative algorithms for molecular design. Here we explore the impact of...

scholarly journals De novo molecular design and generative models

2021 ◽  
Author(s):  
Joshua Meyers ◽  
Benedek Fabian ◽  
Nathan Brown
Keyword(s):  
De Novo ◽  
Molecular Design ◽  
Generative Models ◽  
De Novo Molecular Design

PRO_LIGAND: An Approach to de Novo Molecular Design. 2. Design of Novel Molecules from Molecular Field Analysis (MFA) Models and Pharmacophores

1994 ◽  
Vol 37 (23) ◽  
pp. 3994-4002 ◽  
Author(s):  
Bohdan Waszkowycz ◽  
David E. Clark ◽  
David Frenkel ◽  
Jin Li ◽  
Christopher W. Murray ◽  
...  
Keyword(s):  
De Novo ◽  
Molecular Design ◽  
Field Analysis ◽  
Molecular Field ◽  
Molecular Field Analysis ◽  
De Novo Molecular Design

PRO_LIGAND: An approach to de novo molecular design. 1. Application to the design of organic molecules

1995 ◽  
Vol 9 (1) ◽  
pp. 13-32 ◽  
Author(s):  
David E. Clark ◽  
David Frenkel ◽  
Stephen A. Levy ◽  
Jin Li ◽  
Christopher W. Murray ◽  
...  
Keyword(s):  
Organic Molecules ◽  
De Novo ◽  
Molecular Design ◽  
De Novo Molecular Design

scholarly journals De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping

2019 ◽  
Vol 59 (3) ◽  
pp. 1182-1196 ◽  
Author(s):  
Boris Sattarov ◽  
Igor I. Baskin ◽  
Dragos Horvath ◽  
Gilles Marcou ◽  
Esben Jannik Bjerrum ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
De Novo ◽  
Molecular Design ◽  
Topographic Mapping ◽  
Generative Topographic Mapping ◽  
De Novo Molecular Design

scholarly journals Memory-assisted reinforcement learning for diverse molecular de novo design

2020 ◽  
Author(s):  
Thomas Blaschke ◽  
Ola Engkvist ◽  
Jürgen Bajorath ◽  
Hongming Chen
Keyword(s):  
Reinforcement Learning ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Scoring Function ◽  
Chemical Diversity ◽  
Chemical Structures ◽  
Machine Learning Model ◽  
Low Diversity ◽  
De Novo Molecular Design

Abstract In de novo molecular design, recurrent neural networks (RNN) have been shown to be effective methods for sampling and generating novel chemical structures. Using a technique called reinforcement learning (RL), an RNN can be tuned to target a particular section of chemical space with optimized desirable properties using a scoring function. However, ligands generated by current RL methods so far tend to have relatively low diversity, and sometimes even result in duplicate structures when optimizing towards desired properties. Here, we propose a new method to address the low diversity issue in RL for molecular design. Memory-assisted RL is an extension of the known RL, with the introduction of a so-called memory unit. As proof of concept, we applied our method to generate structures with a desired AlogP value. In a second case study, we applied our method to design ligands for the dopamine type 2 receptor and the 5-hydroxytryptamine type 1A receptor. For both receptors, a machine learning model was developed to predict whether generated molecules were active or not for the receptor. In both case studies, it was found that memory-assisted RL led to the generation of more compounds predicted to be active having higher chemical diversity, thus achieving better coverage of chemical space of known ligands compared to established RL methods.

scholarly journals A De Novo Molecular Generation Method Using Latent Vector Based Generative Adversarial Network

2019 ◽  
Author(s):  
Oleksii Prykhodko ◽  
Simon Viet Johansson ◽  
Panagiotis-Christos Kotsias ◽  
Josep Arús-Pous ◽  
Esben Jannik Bjerrum ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
De Novo Molecular Design ◽  
Novel Structures

<p> </p><p>Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generative adversarial neural network for de novo molecular design. We applied the method in two scenarios: one to generate random drug-like compounds and another to generate target-biased compounds. Our results show that the method works well in both cases: sampled compounds from the trained model can largely occupy the same chemical space as the training set and also generate a substantial fraction of novel compounds. Moreover, the drug-likeness score of compounds sampled from LatentGAN is also similar to that of the training set. Lastly, generated compounds differ from those obtained with a Recurrent Neural Network-based generative model approach, indicating that both methods can be used complementarily.</p><p> </p>

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