scholarly journals Cell Morphology-Guided De Novo Hit Design by Conditioning Generative Adversarial Networks on Phenotypic Image Features

2020 ◽  
Author(s):  
Oscar Méndez-Lucio ◽  
Paula Andrea Marin Zapata ◽  
Joerg Wichard ◽  
David Rouquié ◽  
Djork-Arné Clevert
Keyword(s):  
Small Molecules ◽  
De Novo ◽  
Molecular Targets ◽  
De Novo Design ◽  
Image Features ◽  
Mitigation Strategy ◽  
Generative Adversarial Networks ◽  
Target Information ◽  
Adversarial Networks ◽  
First Time

Developing new small molecules that are bioactive is time-consuming, costly and rarely successful. As a mitigation strategy, we apply, for the first time, generative adversarial networks to de novo design of small molecules using a phenotype-based drug discovery approach. We trained our model on a set of 30,000 compounds and their respective morphological profiles extracted from high content images; no target information was used to train the model. Using this approach, we were able to automatically design agonist-like compounds of different molecular targets.

scholarly journals Cell Morphology-Guided De Novo Hit Design by Conditioning Generative Adversarial Networks on Phenotypic Image Features

2020 ◽  
Author(s):  
Oscar Méndez-Lucio ◽  
Paula Andrea Marin Zapata ◽  
Joerg Wichard ◽  
David Rouquié ◽  
Djork-Arné Clevert
Keyword(s):  
Small Molecules ◽  
De Novo ◽  
Molecular Targets ◽  
De Novo Design ◽  
Image Features ◽  
Mitigation Strategy ◽  
Generative Adversarial Networks ◽  
Target Information ◽  
Adversarial Networks ◽  
First Time

Developing new small molecules that are bioactive is time-consuming, costly and rarely successful. As a mitigation strategy, we apply, for the first time, generative adversarial networks to de novo design of small molecules using a phenotype-based drug discovery approach. We trained our model on a set of 30,000 compounds and their respective morphological profiles extracted from high content images; no target information was used to train the model. Using this approach, we were able to automatically design agonist-like compounds of different molecular targets.

scholarly journals De Novo Design of Bioactive Small Molecules by Artificial Intelligence

2018 ◽  
Vol 37 (1-2) ◽  
pp. 1700153 ◽  
Author(s):  
Daniel Merk ◽  
Lukas Friedrich ◽  
Francesca Grisoni ◽  
Gisbert Schneider
Keyword(s):  
Artificial Intelligence ◽  
Small Molecules ◽  
De Novo ◽  
De Novo Design

scholarly journals A defined structural unit enables de novo design of small-molecule–binding proteins

Science ◽  
2020 ◽  
Vol 369 (6508) ◽  
pp. 1227-1233 ◽  
Author(s):  
Nicholas F. Polizzi ◽  
William F. DeGrado
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Small Molecules ◽  
Structural Unit ◽  
De Novo ◽  
Computational Design ◽  
De Novo Design ◽  
X Ray ◽  
X Ray Crystallography ◽  
Chemical Groups

The de novo design of proteins that bind highly functionalized small molecules represents a great challenge. To enable computational design of binders, we developed a unit of protein structure—a van der Mer (vdM)—that maps the backbone of each amino acid to statistically preferred positions of interacting chemical groups. Using vdMs, we designed six de novo proteins to bind the drug apixaban; two bound with low and submicromolar affinity. X-ray crystallography and mutagenesis confirmed a structure with a precisely designed cavity that forms favorable interactions in the drug–protein complex. vdMs may enable design of functional proteins for applications in sensing, medicine, and catalysis.

scholarly journals De Novo Design of New Chemical Entities (NCEs) for SARS-CoV-2 Using Artificial Intelligence

2020 ◽  
Author(s):  
Navneet Bung ◽  
Sowmya Ramaswamy Krishnan ◽  
Gopalakrishnan Bulusu ◽  
Arijit Roy
Keyword(s):  
Artificial Intelligence ◽  
Small Molecules ◽  
De Novo ◽  
Chemical Space ◽  
De Novo Design ◽  
Protein Targets ◽  
Post Translational Modifications ◽  
Global Pandemic ◽  
New Chemical Entities ◽  
3Cl Protease

The novel SARS-CoV-2 is the source of a global pandemic COVID-19, which has severely affected the health and economy of several countries. Multiple studies are in progress, employing diverse approaches to design novel therapeutics against the potential target proteins in SARS-CoV-2. One of the well-studied protein targets for coronaviruses is the chymotrypsin-like (3CL) protease, responsible for post-translational modifications of viral polyproteins essential for its survival and replication in the host. There are ongoing attempts to repurpose the existing viral protease inhibitors against 3CL protease of SARS-CoV-2. Recent studies have proven the efficiency of artificial intelligence techniques in learning the known chemical space and generating novel small molecules. In this study, we employed deep neural network-based generative and predictive models for de novo design of new small molecules capable of inhibiting the 3CL protease. The generated small molecules were filtered and screened against the binding site of the 3CL protease structure of SARS-CoV-2. Based on the screening results and further analysis, we have identified 31 potential compounds as ideal candidates for further synthesis and testing against SARS-CoV-2. The generated small molecules were also compared with available natural products. Two of the generated small molecules showed high similarity to a plant natural product, Aurantiamide, which can be used for rapid testing during this time of crisis.

scholarly journals De Novo Design of New Chemical Entities (NCEs) for SARS-CoV-2 Using Artificial Intelligence

2020 ◽  
Author(s):  
Navneet Bung ◽  
Sowmya Ramaswamy Krishnan ◽  
Gopalakrishnan Bulusu ◽  
Arijit Roy
Keyword(s):  
Artificial Intelligence ◽  
Small Molecules ◽  
De Novo ◽  
Chemical Space ◽  
De Novo Design ◽  
Protein Targets ◽  
Post Translational Modifications ◽  
Global Pandemic ◽  
New Chemical Entities ◽  
3Cl Protease

The novel SARS-CoV-2 is the source of a global pandemic COVID-19, which has severely affected the health and economy of several countries. Multiple studies are in progress, employing diverse approaches to design novel therapeutics against the potential target proteins in SARS-CoV-2. One of the well-studied protein targets for coronaviruses is the chymotrypsin-like (3CL) protease, responsible for post-translational modifications of viral polyproteins essential for its survival and replication in the host. There are ongoing attempts to repurpose the existing viral protease inhibitors against 3CL protease of SARS-CoV-2. Recent studies have proven the efficiency of artificial intelligence techniques in learning the known chemical space and generating novel small molecules. In this study, we employed deep neural network-based generative and predictive models for de novo design of new small molecules capable of inhibiting the 3CL protease. The generated small molecules were filtered and screened against the binding site of the 3CL protease structure of SARS-CoV-2. Based on the screening results and further analysis, we have identified 31 potential compounds as ideal candidates for further synthesis and testing against SARS-CoV-2. The generated small molecules were also compared with available natural products. Two of the generated small molecules showed high similarity to a plant natural product, Aurantiamide, which can be used for rapid testing during this time of crisis.

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.

De novo design approaches targeting an envelope protein pocket to identify small molecules against dengue virus

2019 ◽  
Vol 182 ◽  
pp. 111628 ◽  
Author(s):  
Emilse S. Leal ◽  
Natalia S. Adler ◽  
Gabriela A. Fernández ◽  
Leopoldo G. Gebhard ◽  
Leandro Battini ◽  
...  
Keyword(s):  
Dengue Virus ◽  
Small Molecules ◽  
Envelope Protein ◽  
De Novo ◽  
De Novo Design ◽  
Protein Pocket

scholarly journals De Novo Design of New Chemical Entities (NCEs) for SARS-CoV-2 Using Artificial Intelligence

2020 ◽  
Author(s):  
Navneet Bung ◽  
Sowmya Ramaswamy Krishnan ◽  
Gopalakrishnan Bulusu ◽  
Arijit Roy
Keyword(s):  
Artificial Intelligence ◽  
Small Molecules ◽  
De Novo ◽  
Chemical Space ◽  
De Novo Design ◽  
Protein Targets ◽  
Post Translational Modifications ◽  
Global Pandemic ◽  
New Chemical Entities ◽  
3Cl Protease

The novel SARS-CoV-2 is the source of a global pandemic COVID-19, which has severely affected the health and economy of several countries. Multiple studies are in progress, employing diverse approaches to design novel therapeutics against the potential target proteins in SARS-CoV-2. One of the well-studied protein targets for coronaviruses is the chymotrypsin-like (3CL) protease, responsible for post-translational modifications of viral<br>polyproteins essential for its survival and replication in the host. There are ongoing attempts to repurpose the existing viral protease inhibitors against 3CL protease of SARS-<br>CoV-2. Recent studies have proven the efficiency of artificial intelligence techniques in learning the known chemical space and generating novel small molecules. In this study,<br>we employed deep neural network-based generative and predictive models for de novo design of new small molecules capable of inhibiting the 3CL protease. The generated<br>small molecules were filtered and screened against the binding site of the 3CL protease structure of SARS-CoV-2. Based on the screening results and further analysis, we have<br>identified 31 potential compounds as ideal candidates for further synthesis and testing against SARS-CoV-2.

scholarly journals De novo design of new chemical entities for SARS-CoV-2 using artificial intelligence

2021 ◽  
Author(s):  
Navneet Bung ◽  
Sowmya R Krishnan ◽  
Gopalakrishnan Bulusu ◽  
Arijit Roy
Keyword(s):  
Small Molecules ◽  
Predictive Models ◽  
Deep Neural Network ◽  
De Novo ◽  
Chemical Space ◽  
De Novo Design ◽  
The Novel ◽  
New Chemical Entities ◽  
Novel Coronavirus ◽  
3Cl Protease

Background: The novel coronavirus SARS-CoV-2 has severely affected the health and economy of several countries. Multiple studies are in progress to design novel therapeutics against the potential target proteins in SARS-CoV-2, including 3CL protease, an essential protein for virus replication. Materials & methods: In this study we employed deep neural network-based generative and predictive models for de novo design of small molecules capable of inhibiting the 3CL protease. The generative model was optimized using transfer learning and reinforcement learning to focus around the chemical space corresponding to the protease inhibitors. Multiple physicochemical property filters and virtual screening score were used for the final screening. Conclusion: We have identified 33 potential compounds as ideal candidates for further synthesis and testing against SARS-CoV-2.

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