scholarly journals Open Science Discovery of Oral Non-Covalent SARS-CoV-2 Main Protease Inhibitors

2021 ◽  
Author(s):  
The COVID Moonshot Consortium ◽  
John Chodera ◽  
Alpha Lee ◽  
Nir London ◽  
Frank von Delft
Keyword(s):  
Drug Discovery ◽  
Open Science ◽  
Knowledge Bases ◽  
Biochemical Activity ◽  
Activity Data ◽  
X Ray ◽  
Structure Activity ◽  
Main Protease ◽  
Orally Bioavailable ◽  
Clinical Potential

The COVID-19 pandemic is a stark reminder that a barren global antiviral pipeline has grave humanitarian consequences. Future pandemics could be prevented by accessible, easily deployable broad-spectrum oral antivirals and open knowledge bases that derisk and accelerate novel antiviral discovery and development. Here, we report the results of the COVID Moonshot, a fully open-science structure-enabled drug discovery campaign targeting the SARS-CoV-2 main protease. We discovered a novel chemical scaffold that is differentiated to current clinical candidates in terms of toxicity and pharmacokinetics liabilities, and developed it into orally-bioavailable inhibitors with clinical potential. Our approach leverages crowdsourcing, high throughput structural biology, machine learning, and exascale molecular simulations. In the process, we generated a detailed map of the structural plasticity of the main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. In a first for a structure-based drug discovery campaign, all compound designs (>18,000 designs), crystallographic data (>500 ligand-bound X-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2,400 compounds) for this campaign were shared rapidly and openly, creating a rich open and IP-free knowledgebase for future anti-coronavirus drug discovery.

scholarly journals Structure-based drug design: aiming for a perfect fit

2017 ◽  
Vol 61 (5) ◽  
pp. 431-437 ◽  
Author(s):  
Rob L.M. van Montfort ◽  
Paul Workman
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Structural Biology ◽  
Protein Structures ◽  
Three Dimensional ◽  
Integral Membrane Protein ◽  
Open Science ◽  
Dimensional Structure ◽  
Structure Based Drug Design ◽  
X Ray

Knowledge of the three-dimensional structure of therapeutically relevant targets has informed drug discovery since the first protein structures were determined using X-ray crystallography in the 1950s and 1960s. In this editorial we provide a brief overview of the powerful impact of structure-based drug design (SBDD), which has its roots in computational and structural biology, with major contributions from both academia and industry. We describe advances in the application of SBDD for integral membrane protein targets that have traditionally proved very challenging. We emphasize the major progress made in fragment-based approaches for which success has been exemplified by over 30 clinical drug candidates and importantly three FDA-approved drugs in oncology. We summarize the articles in this issue that provide an excellent snapshot of the current state of the field of SBDD and fragment-based drug design and which offer key insights into exciting new developments, such as the X-ray free-electron laser technology, cryo-electron microscopy, open science approaches and targeted protein degradation. We stress the value of SBDD in the design of high-quality chemical tools that are used to interrogate biology and disease pathology, and to inform target validation. We emphasize the need to maintain the scientific rigour that has been traditionally associated with structural biology and extend this to other methods used in drug discovery. This is particularly important because the quality and robustness of any form of contributory data determines its usefulness in accelerating drug design, and therefore ultimately in providing patient benefit.

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

scholarly journals Targeting the SARS-CoV-2 main protease using FDA-approved Isavuconazonium, a P2–P3 α-ketoamide derivative and Pentagastrin: An in-silico drug discovery approach

2020 ◽  
Vol 101 ◽  
pp. 107730 ◽  
Author(s):  
Ikechukwu Achilonu ◽  
Emmanuel Amarachi Iwuchukwu ◽  
Okechinyere Juliet Achilonu ◽  
Manuel Antonio Fernandes ◽  
Yasien Sayed
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Main Protease

scholarly journals The Inhibitory Effects of Plant Derivate Polyphenols on the Main Protease of SARS Coronavirus 2 and Their Structure–Activity Relationship

Molecules ◽  
2021 ◽  
Vol 26 (7) ◽  
pp. 1924
Author(s):  
Thi Thanh Hanh Nguyen ◽  
Jong-Hyun Jung ◽  
Min-Kyu Kim ◽  
Sangyong Lim ◽  
Jae-Myoung Choi ◽  
...  
Keyword(s):  
Structure Activity Relationship ◽  
Garlic Extract ◽  
Activity Relationship ◽  
Hydroxyl Group ◽  
Inhibitory Effects ◽  
Structure Activity ◽  
Main Protease ◽  
Ic50 Values ◽  
Km Value ◽  
Novel Coronavirus

The main protease (Mpro) is a major protease having an important role in viral replication of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the novel coronavirus that caused the pandemic of 2020. Here, active Mpro was obtained as a 34.5 kDa protein by overexpression in E. coli BL21 (DE3). The optimal pH and temperature of Mpro were 7.5 and 37 °C, respectively. Mpro displayed a Km value of 16 μM with Dabcyl-KTSAVLQ↓SGFRKME-Edans. Black garlic extract and 49 polyphenols were studied for their inhibitory effects on purified Mpro. The IC50 values were 137 μg/mL for black garlic extract and 9–197 μM for 15 polyphenols. The mixtures of tannic acid with puerarin, daidzein, and/or myricetin enhanced the inhibitory effects on Mpro. The structure–activity relationship of these polyphenols revealed that the hydroxyl group in C3′, C4′, C5′ in the B-ring, C3 in the C-ring, C7 in A-ring, the double bond between C2 and C3 in the C-ring, and glycosylation at C8 in the A-ring contributed to inhibitory effects of flavonoids on Mpro.

scholarly journals Fusarins G–L with Inhibition of NO in RAW264.7 from Marine-Derived Fungus Fusarium solani 7227

Marine Drugs ◽  
2021 ◽  
Vol 19 (6) ◽  
pp. 305
Author(s):  
Guangyuan Luo ◽  
Li Zheng ◽  
Qilin Wu ◽  
Senhua Chen ◽  
Jing Li ◽  
...  
Keyword(s):  
Fusarium Solani ◽  
2D Nmr ◽  
Inflammatory Activity ◽  
Raw264.7 Cells ◽  
Spectroscopic Methods ◽  
X Ray ◽  
X Ray Crystallography ◽  
Structure Activity ◽  
Ic50 Values ◽  
New Compounds

Six new fusarin derivatives, fusarins G–L (1–6), together with five known compounds (5–11) were isolated from the marine-derived fungus Fusarium solani 7227. The structures of the new compounds were elucidated by means of comprehensive spectroscopic methods (1D and 2D NMR, HRESIMS, ECD, and ORC) and X-ray crystallography. Compounds 5–11 exhibited potent anti-inflammatory activity by inhibiting the production of NO in RAW264.7 cells activated by lipopolysaccharide, with IC50 values ranging from 3.6 to 32.2 μM. The structure–activity relationships of the fusarins are discussed herein.

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