scholarly journals Systemic Evolutionary Chemical Space Exploration For Drug Discovery

Author(s):  
Chong Lu ◽  
Shien Liu ◽  
Weihua Shi ◽  
Jun Yu ◽  
Zhou Zhou ◽  
...  

Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a “lego-building” process within the pocket of a certain target. The key of virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against PHGDH, proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE.

2020 ◽  
Author(s):  
Navneet Bung ◽  
Sowmya Ramaswamy Krishnan ◽  
Gopalakrishnan Bulusu ◽  
Arijit Roy

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.


Author(s):  
Navneet Bung ◽  
Sowmya Ramaswamy Krishnan ◽  
Gopalakrishnan Bulusu ◽  
Arijit Roy

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.


2020 ◽  
Author(s):  
Navneet Bung ◽  
Sowmya Ramaswamy Krishnan ◽  
Gopalakrishnan Bulusu ◽  
Arijit Roy

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.


Author(s):  
Navneet Bung ◽  
Sowmya R Krishnan ◽  
Gopalakrishnan Bulusu ◽  
Arijit Roy

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.


2018 ◽  
Vol 37 (1-2) ◽  
pp. 1700153 ◽  
Author(s):  
Daniel Merk ◽  
Lukas Friedrich ◽  
Francesca Grisoni ◽  
Gisbert Schneider

Science ◽  
2020 ◽  
Vol 369 (6508) ◽  
pp. 1227-1233 ◽  
Author(s):  
Nicholas F. Polizzi ◽  
William F. DeGrado

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.


2016 ◽  
Vol 56 (10) ◽  
pp. 1885-1893 ◽  
Author(s):  
Shunichi Takeda ◽  
Hiromasa Kaneko ◽  
Kimito Funatsu

2020 ◽  
Author(s):  
Oscar Méndez-Lucio ◽  
Paula Andrea Marin Zapata ◽  
Joerg Wichard ◽  
David Rouquié ◽  
Djork-Arné Clevert

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.


Author(s):  
Thomas Blaschke ◽  
Josep Arús-Pous ◽  
Hongming Chen ◽  
Christian Margreitter ◽  
Christian Tyrchan ◽  
...  

With this application note we aim to offer the community a production-ready tool for de novo design. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. By releasing the code we are aiming to facilitate the research on using generative methods on drug discovery problems and to promote the collaborative efforts in this area so that it can be used as an interaction point for future scientific collaborations.


2020 ◽  
Author(s):  
Thomas Blaschke ◽  
Josep Arús-Pous ◽  
Hongming Chen ◽  
Christian Margreitter ◽  
Christian Tyrchan ◽  
...  

With this application note we aim to offer the community a production-ready tool for de novo design. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. By releasing the code we are aiming to facilitate the research on using generative methods on drug discovery problems and to promote the collaborative efforts in this area so that it can be used as an interaction point for future scientific collaborations.


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