scholarly journals CReM: chemically reasonable mutations framework for structure generation

2020 ◽  
Author(s):  
Pavel Polishchuk
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
De Novo ◽  
De Novo Design ◽  
Learning Models ◽  
Structure Generation ◽  
Design Studies ◽  
Flexible Control

Abstract Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on deep learning models and conventional atom-based approaches may result in invalid structures and did not address their synthetic feasibility issues. Conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here, we developed a new fragment-based approach which results in chemically valid structures by design and gives flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The approach was implemented as an open-source Python module.

scholarly journals CReM: chemically reasonable mutations framework for structure generation

2020 ◽  
Author(s):  
Pavel Polishchuk
Keyword(s):  
Deep Learning ◽  
De Novo ◽  
Chemical Space ◽  
De Novo Design ◽  
The Other ◽  
Learning Models ◽  
Structure Generation ◽  
Design Studies ◽  
Flexible Control ◽  
New Framework

Abstract Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may result in invalid structures and fail to address their synthetic feasibility issues. On the other hand, conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide both better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here we developed a new framework of fragment-based structure generation that, by design, results in the chemically valid structures and provides flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The framework was implemented as an open-source Python module and can be used to create custom workflows for the exploration of chemical space.

Open-source deep learning models for acoustic detection and classification of orcas

2021 ◽  
Vol 150 (4) ◽  
pp. A286-A286
Author(s):  
Sadman Sakib ◽  
Steven Bergner ◽  
Dave Campbell ◽  
Mike Dowd ◽  
Fabio Frazao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Acoustic Detection ◽  
Learning Models

scholarly journals Pre-trained deep learning models in automatic COVID-19 diagnosis

2021 ◽  
Vol 22 (3) ◽  
pp. 1540
Author(s):  
Ahmed Wasif Reza ◽  
Md Mahamudul Hasan ◽  
Nazla Nowrin ◽  
Mir Moynuddin Ahmed Shibly
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Healthcare Professionals ◽  
Automated System ◽  
Primary Concern ◽  
X Rays ◽  
Learning Models ◽  
Efficient System ◽  
Empirical Results ◽  
History Of

Coronavirus Disease (COVID-19) is a devastating pandemic in the history of mankind. It is a highly contagious flu that can spread from human to human without revealing any symptoms. For being so contagious, detecting patients with it and isolating them has become the primary concern for healthcare professionals. This study presented an alternative way to identify COVID-19 patients by doing an automatic examination of chest X-rays of the patients. To develop such an efficient system, six pre-trained deep learning models were used. Those models were: VGG16, InceptionV3, Xception, DenseNet201, InceptionResNetV2, and EfficientNetB4. Those models were developed on two open-source datasets that have chest X-rays of patients diagnosed with COVID-19. Among the models, EfficientNetB4 achieved better performances on both datasets with 96% and 97% of accuracies. The empirical results were also exemplary. This type of automated system can help us fight this dangerous virus outbreak.

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 ◽  
Esben Jannik Bjerrum ◽  
Ola Engkvist ◽  
...  
Keyword(s):  
Deep Learning ◽  
De Novo ◽  
Chemical Space ◽  
Learning Method ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Structure Generation ◽  
Adversarial Network ◽  
Molecule Design ◽  
Novel Structures

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

scholarly journals Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design

Molecules ◽  
2020 ◽  
Vol 25 (14) ◽  
pp. 3250 ◽  
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Protein Design ◽  
De Novo ◽  
De Novo Design ◽  
Machine Learning Techniques ◽  
Generative Adversarial Networks ◽  
Future Research ◽  
Discovery Research ◽  
De Novo Peptide

A growing body of evidence now suggests that artificial intelligence and machine learning techniques can serve as an indispensable foundation for the process of drug design and discovery. In light of latest advancements in computing technologies, deep learning algorithms are being created during the development of clinically useful drugs for treatment of a number of diseases. In this review, we focus on the latest developments for three particular arenas in drug design and discovery research using deep learning approaches, such as generative adversarial network (GAN) frameworks. Firstly, we review drug design and discovery studies that leverage various GAN techniques to assess one main application such as molecular de novo design in drug design and discovery. In addition, we describe various GAN models to fulfill the dimension reduction task of single-cell data in the preclinical stage of the drug development pipeline. Furthermore, we depict several studies in de novo peptide and protein design using GAN frameworks. Moreover, we outline the limitations in regard to the previous drug design and discovery studies using GAN models. Finally, we present a discussion of directions and challenges for future research.

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 Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers

2020 ◽  
Author(s):  
Carly K. Schissel ◽  
Somesh Mohapatra ◽  
Justin M. Wolfe ◽  
Colin M. Fadzen ◽  
Kamela Bellovoda ◽  
...  
Keyword(s):  
Deep Learning ◽  
Antisense Oligonucleotides ◽  
De Novo ◽  
Search Space ◽  
Functional Polymers ◽  
De Novo Design ◽  
Human Learning ◽  
High Throughput Experimentation ◽  
Cell Penetrating ◽  
Unsupervised Deep Learning

AbstractThere are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we couple supervised and unsupervised deep learning with high-throughput experimentation to drive the design of high-activity, novel sequences reaching 10 kDa that deliver antisense oligonucleotides to the nucleus of cells. The models, in which natural and unnatural residues are represented as topological fingerprints, decipher and visualize sequence-activity predictions. The new variants boost antisense activity by 50-fold, are effective in animals, are nontoxic, and can also deliver proteins into the cytosol. Machine learning can discover functional polymers that enhance cellular uptake of biotherapeutics, with significant implications toward developing therapies for currently untreatable diseases.One sentence summaryDeep learning generates de novo large functional abiotic polymers that deliver antisense oligonucleotides to the nucleus.

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 ◽  
Esben Jannik Bjerrum ◽  
Ola Engkvist ◽  
...  
Keyword(s):  
Deep Learning ◽  
De Novo ◽  
Chemical Space ◽  
Learning Method ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Structure Generation ◽  
Adversarial Network ◽  
Molecule Design ◽  
Novel Structures

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

Sign in / Sign up

Export Citation Format

Share Document