scholarly journals Deep Generative Models for Ligand-based de Novo Design Applied to Multi-parametric Optimization

2021 ◽  
Author(s):  
Quentin Perron ◽  
Olivier Mirguet ◽  
Hamza Tajmouati ◽  
Adam Skiredj ◽  
Anne Rojas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
New Technology ◽  
De Novo Design ◽  
Generative Models ◽  
De Novo Molecular Design

<div> <div> <div> <p>Multi-Parameter Optimization (MPO) is a major challenge in New Chemical Entity (NCE) drug discovery projects, and the inability to identify molecules meeting all the criteria of lead optimization (LO) is an important cause of NCE project failure. Several ligand- and structure-based de novo design methods have been published over the past decades, some of which have proved useful multiobjective optimization. However, there is still need for improvement to better address the chemical feasibility of generated compounds as well as increasing the explored chemical space while tackling the MPO challenge. Recently, promising results have been reported for deep learning generative models applied to de novo molecular design, but until now, to our knowledge, no report has been made of the value of this new technology for addressing MPO in an actual drug discovery project. Our objective in this study was to evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the discovery of an optimized lead compound meeting all in vitro late stage LO criteria. </p> </div> </div> </div>

scholarly journals Deep Generative Models for Ligand-based de Novo Design Applied to Multi-parametric Optimization

2021 ◽  
Author(s):  
Quentin Perron ◽  
Olivier Mirguet ◽  
Hamza Tajmouati ◽  
Adam Skiredj ◽  
Anne Rojas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
New Technology ◽  
De Novo Design ◽  
Generative Models ◽  
De Novo Molecular Design

<div> <div> <div> <p>Multi-Parameter Optimization (MPO) is a major challenge in New Chemical Entity (NCE) drug discovery projects, and the inability to identify molecules meeting all the criteria of lead optimization (LO) is an important cause of NCE project failure. Several ligand- and structure-based de novo design methods have been published over the past decades, some of which have proved useful multiobjective optimization. However, there is still need for improvement to better address the chemical feasibility of generated compounds as well as increasing the explored chemical space while tackling the MPO challenge. Recently, promising results have been reported for deep learning generative models applied to de novo molecular design, but until now, to our knowledge, no report has been made of the value of this new technology for addressing MPO in an actual drug discovery project. Our objective in this study was to evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the discovery of an optimized lead compound meeting all in vitro late stage LO criteria. </p> </div> </div> </div>

scholarly journals Deep Generative Models for Ligand-based de Novo Design Applied to Multi-parametric Optimization

2021 ◽  
Author(s):  
Quentin Perron ◽  
Olivier Mirguet ◽  
Hamza Tajmouati ◽  
Adam Skiredj ◽  
Anne Rojas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
New Technology ◽  
De Novo Design ◽  
Generative Models ◽  
De Novo Molecular Design

<div> <div> <div> <p>Multi-Parameter Optimization (MPO) is a major challenge in New Chemical Entity (NCE) drug discovery projects, and the inability to identify molecules meeting all the criteria of lead optimization (LO) is an important cause of NCE project failure. Several ligand- and structure-based de novo design methods have been published over the past decades, some of which have proved useful multiobjective optimization. However, there is still need for improvement to better address the chemical feasibility of generated compounds as well as increasing the explored chemical space while tackling the MPO challenge. Recently, promising results have been reported for deep learning generative models applied to de novo molecular design, but until now, to our knowledge, no report has been made of the value of this new technology for addressing MPO in an actual drug discovery project. Our objective in this study was to evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the discovery of an optimized lead compound meeting all in vitro late stage LO criteria. </p> </div> </div> </div>

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>

scholarly journals A de novo molecular generation method using latent vector based generative adversarial network

2019 ◽  
Vol 11 (1) ◽  
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

AbstractDeep 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.

scholarly journals Comparative Study of Deep Generative Models on Chemical Space Coverage (v18)

2021 ◽  
Author(s):  
Jie Zhang ◽  
Rocío Mercado ◽  
Ola Engkvist ◽  
Hongming Chen
Keyword(s):  
Deep Learning ◽  
Functional Groups ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Structures ◽  
Reference Database ◽  
Ring Systems ◽  
Adversarial Networks

<p>In recent years, deep molecular generative models have emerged as novel methods for <i>de novo</i> molecular design. Thanks to the rapid advance of deep learning techniques, deep learning architectures such as recurrent neural networks, generative autoencoders, and adversarial networks, to give a few examples, have been employed for constructing generative models. However, so far the metrics used to evaluate these deep generative models are not discriminative enough to separate the performance of various state-of-the-art generative models. This work presents a novel metric for evaluating deep molecular generative models; this new metric is based on the chemical space coverage of a reference database, and compares not only the molecular structures, but also the ring systems and functional groups, reproduced from a reference dataset of 1M structures. In this study, the performance of 7 different molecular generative models was compared by calculating their structure and substructure coverage of the GDB-13 database while using a 1M subset of GDB-13 for training. Our study shows that the performance of various generative models varies significantly using the benchmarking metrics introduced herein, such that generalization capability of the generative model can be clearly differentiated. Additionally, the coverage of ring systems and functional groups existing in GDB-13 was also compared between the models. Our study provides a useful new metric that can be used for evaluating and comparing generative models.</p>

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>

scholarly journals Comparative Study of Deep Generative Models on Chemical Space Coverage

2020 ◽  
Author(s):  
Jie Zhang ◽  
Rocío Mercado ◽  
Ola Engkvist ◽  
Hongming Chen
Keyword(s):  
Deep Learning ◽  
Functional Groups ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Structures ◽  
Reference Database ◽  
Ring Systems ◽  
Adversarial Networks

<p>In recent years, deep molecular generative models have emerged as novel methods for <i>de novo</i> molecular design. Thanks to the rapid advance of deep learning techniques, deep learning architectures such as recurrent neural networks, generative autoencoders, and adversarial networks, to give a few examples, have been employed for constructing generative models. However, so far the metrics used to evaluate these deep generative models are not discriminative enough to separate the performance of various state-of-the-art generative models. This work presents a novel metric for evaluating deep molecular generative models; this new metric is based on the chemical space coverage of a reference database, and compares not only the molecular structures, but also the ring systems and functional groups, reproduced from a reference dataset of 1M structures. In this study, the performance of 7 different molecular generative models was compared by calculating their structure and substructure coverage of the GDB-13 database while using a 1M subset of GDB-13 for training. Our study shows that the performance of various generative models varies significantly using the benchmarking metrics introduced herein, such that generalization capability of the generative model can be clearly differentiated. Additionally, the coverage of ring systems and functional groups existing in GDB-13 was also compared between the models. Our study provides a useful new metric that can be used for evaluating and comparing generative models.</p>

scholarly journals Comparative Study of Deep Generative Models on Chemical Space Coverage

2020 ◽  
Author(s):  
Jie Zhang ◽  
Rocío Mercado ◽  
Ola Engkvist ◽  
Hongming Chen
Keyword(s):  
Deep Learning ◽  
Functional Groups ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Structures ◽  
Reference Database ◽  
Ring Systems ◽  
Adversarial Networks

<p>In recent years, deep molecular generative models have emerged as novel methods for <i>de novo</i> molecular design. Thanks to the rapid advance of deep learning techniques, deep learning architectures such as recurrent neural networks, generative autoencoders, and adversarial networks, to give a few examples, have been employed for constructing generative models. However, so far the metrics used to evaluate these deep generative models are not discriminative enough to separate the performance of various state-of-the-art generative models. This work presents a novel metric for evaluating deep molecular generative models; this new metric is based on the chemical space coverage of a reference database, and compares not only the molecular structures, but also the ring systems and functional groups, reproduced from a reference dataset of 1M structures. In this study, the performance of 7 different molecular generative models was compared by calculating their structure and substructure coverage of the GDB-13 database while using a 1M subset of GDB-13 for training. Our study shows that the performance of various generative models varies significantly using the benchmarking metrics introduced herein, such that generalization capability of the generative model can be clearly differentiated. Additionally, the coverage of ring systems and functional groups existing in GDB-13 was also compared between the models. Our study provides a useful new metric that can be used for evaluating and comparing generative models.</p>

scholarly journals DenovoProfiling: a webserver for de novo generated molecule library profiling

2021 ◽  
Author(s):  
Zhihong Liu ◽  
Jiewen Du ◽  
Bingdong Liu ◽  
Zongbin Cui ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Deep Learning ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Drug Discovery Process ◽  
De Novo Drug Design ◽  
Learning Techniques ◽  
Functional Profiling ◽  
De Novo Molecular Design ◽  
Selection Of

Abstract With the advances of deep learning techniques, various architectures for molecular generation have been proposed for de novo drug design. Successful cases from academia and industrial demonstrated that the deep learning-based de novo molecular design could efficiently accelerate the drug discovery process. The flourish of the de novo molecular generation methods and applications created a great demand for the visualization and functional profiling for the de novo generated molecules. The rising of publicly available chemogenomic databases lays good foundations and creates good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a webserver dedicated to de novo library visualization and functional profiling. Currently, DenovoProfiling contains six modules: (1) identification & visualization, (2) chemical space, (3) scaffold analysis, (4) molecular alignment, (5) drugs mapping, and (6) target & pathway. DenovoProfiling could provide structural identification, chemical space exploration, drug mapping, and target & pathway information. The comprehensive annotated information could give users a clear picture of their de novo library and could guide the further selection of candidates for synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.

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 ◽  
De Novo Design ◽  
Memory Unit ◽  
Chemical Structures ◽  
Low Diversity ◽  
De Novo Molecular Design

<div><div><div><p>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 particular properties. Here, we propose a new method to address the low diversity issue in RL. Memory-assisted RL is an extension of the known RL, with the introduction of a so-called memory unit.</p></div></div></div>

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