DenovoProfiling: a webserver for de novo generated molecule library profiling

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.

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DenovoProfiling: A Webserver for De Novo Generated Molecule Library Profiling

10.21203/rs.3.rs-142605/v1 ◽

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 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 create good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a web server dedicated for 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) target & pathways, and (6) drugs mapping. DenovoProfiling could provide structural identification, chemical space exploration, drugs mapping, and targets & pathways. The comprehensive annotated information could give user a clear picture of their de novo library and could provide guidance in the further selection of candidates for synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.

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

10.26434/chemrxiv.8299544.v3 ◽

2019 ◽

Cited By ~ 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

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

Journal of Cheminformatics ◽

10.1186/s13321-019-0397-9 ◽

2019 ◽

Vol 11 (1) ◽

Cited By ~ 24

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.

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

10.26434/chemrxiv.8299544.v4 ◽

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

10.26434/chemrxiv.13622417.v1 ◽

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>

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

10.26434/chemrxiv.13622417.v2 ◽

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>

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

10.26434/chemrxiv.13622417 ◽

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>

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

10.26434/chemrxiv.8299544 ◽

2019 ◽

Cited By ~ 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

<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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De Novo Drug Design Using Artificial Intelligence Applied on SARS-CoV-2 Viral Proteins ASYNT-GAN

BioChem ◽

10.3390/biochem1010004 ◽

2021 ◽

Vol 1 (1) ◽

pp. 36-48

Author(s):

Ivan Jacobs ◽

Manolis Maragoudakis

Keyword(s):

Deep Learning ◽

Small Molecules ◽

Natural Product ◽

De Novo ◽

Viral Proteins ◽

Computer Assisted ◽

De Novo Drug Design ◽

Learning Techniques ◽

Latent Space ◽

Synthetic Molecule

Computer-assisted de novo design of natural product mimetics offers a viable strategy to reduce synthetic efforts and obtain natural-product-inspired bioactive small molecules, but suffers from several limitations. Deep learning techniques can help address these shortcomings. We propose the generation of synthetic molecule structures that optimizes the binding affinity to a target. To achieve this, we leverage important advancements in deep learning. Our approach generalizes to systems beyond the source system and achieves the generation of complete structures that optimize the binding to a target unseen during training. Translating the input sub-systems into the latent space permits the ability to search for similar structures, and the sampling from the latent space for generation.

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