QSAR models for predicting cathepsin B inhibition by small molecules—Continuous and binary QSAR models to classify cathepsin B inhibition activities of small molecules

Since December 2019, a novel beta coronavirus has spread around the world. This virus can cause severe acute respiratory syndrome (SARS). In this study, we reviewed proteases of SARS-CoV-2 based on related articles published in journals indexed by Scopus, PubMed, and Google Scholar from December 2019 to April 2020. Based on this study, we can claim that this coronavirus has about 76% genotype similarity to SARS coronavirus (SARS-CoV). Also, similarities between these two viruses have been found in the mechanism of entry into host cells and pathogenicity. ACE 2, the angiotensin convertase enzyme 2, plays a role in the Renin-Angiotensin-Aldosterone system (RAAS) and blood pressure regulation. Some mechanisms have been reported for the role of ACE 2 in the pathogenicity of SARS-CoV-2. For example, the interaction between the ACE 2 receptor and spike protein mediated by TMPRSS2, Cathepsin B/L, and other enzymes is responsible for the entry of the virus into human cells and pathogenicity. Some host cell endosomal enzymes are necessary to cleavage coronavirus spike protein and cause binding to their common receptor. So, we conclude that molecules like antibodies or small molecules like ACE 2 antagonists and soluble ACE 2 can be used as a good therapeutic candidate to prevent SARS-CoV-2.

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Quantitative Structure Activity Relationship (QSAR) study predicts small molecule binding to RNA structure

10.33774/chemrxiv-2021-czl9p-v2 ◽

2021 ◽

Author(s):

Zhengguo Cai ◽

Martina Zafferani ◽

Olanrewaju Akande ◽

Amanda Hargrove

Keyword(s):

High Resolution ◽

Small Molecules ◽

Small Molecule ◽

Rna Structure ◽

Quantitative Structure ◽

Rna Structures ◽

Qsar Study ◽

Structure Activity ◽

Qsar Models ◽

Hiv 1

The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure-activity relationships (QSAR). Herein, we developed QSAR models that quantitatively predict both thermodynamic and kinetic-based binding parameters of small molecules and the HIV-1 TAR model RNA system. A set of small molecules bearing diverse scaffolds was screened against the HIV-1-TAR construct using surface plasmon resonance, which provided the binding kinetics and affinities. The data was then analyzed using multiple linear regression (MLR) combined with feature selection to afford robust models for binding of diverse RNA-targeted scaffolds. The predictivity of the model was validated on untested small molecules. The QSAR models presented herein represent the first application of validated and predictive 2D-QSAR using multiple scaffolds against an RNA target. We expect the workflow to be generally applicable to other RNA structures, ultimately providing essential insight into the small molecule descriptors that drive selective binding interactions and, consequently, providing a platform that can exponentially increase the efficiency of ligand design and optimization without the need for high-resolution RNA structures.

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Drug Re‐Positioning Studies for Novel HIV‐1 Inhibitors Using Binary QSAR Models and Multi‐Target‐Driven In Silico Studies

Molecular Informatics ◽

10.1002/minf.202000012 ◽

2020 ◽

Author(s):

Berna Dogan ◽

Serdar Durdagi

Keyword(s):

In Silico ◽

In Silico Studies ◽

Qsar Models ◽

Binary Qsar ◽

Hiv 1

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Integration of Text Mining and Binary QSAR Models for Novel Anti-Hypertensive Antagonist Scaffolds

Biophysical Journal ◽

10.1016/j.bpj.2018.11.2583 ◽

2019 ◽

Vol 116 (3) ◽

pp. 478a ◽

Cited By ~ 1

Author(s):

Serdar Durdagi ◽

Ismail Erol ◽

Berna Dogan ◽

Taha Berkay Sen

Keyword(s):

Text Mining ◽

Qsar Models ◽

Binary Qsar

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The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis

Molecules ◽

10.3390/molecules25112615 ◽

2020 ◽

Vol 25 (11) ◽

pp. 2615 ◽

Cited By ~ 2

Author(s):

Kwang-Eun Choi ◽

Anand Balupuri ◽

Nam Sook Kang

Keyword(s):

Small Molecules ◽

Quantitative Structure Activity Relationship ◽

Training Set ◽

Cardiac Side Effects ◽

Chemical Structures ◽

Structure Activity ◽

Channel Blockage ◽

Qsar Models ◽

Herg Channels ◽

Selection Of

Human ether-a-go-go-related gene (hERG) potassium channel blockage by small molecules may cause severe cardiac side effects. Thus, it is crucial to screen compounds for activity on the hERG channels early in the drug discovery process. In this study, we collected 5299 hERG inhibitors with diverse chemical structures from a number of sources. Based on this dataset, we evaluated different machine learning (ML) and deep learning (DL) algorithms using various integer and binary type fingerprints. A training set of 3991 compounds was used to develop quantitative structure–activity relationship (QSAR) models. The performance of the developed models was evaluated using a test set of 998 compounds. Models were further validated using external set 1 (263 compounds) and external set 2 (47 compounds). Overall, models with integer type fingerprints showed better performance than models with no fingerprints, converted binary type fingerprints or original binary type fingerprints. Comparison of ML and DL algorithms revealed that integer type fingerprints are suitable for ML, whereas binary type fingerprints are suitable for DL. The outcomes of this study indicate that the rational selection of fingerprints is important for hERG blocker prediction.

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Machine learning-based QSAR models to predict sodium ion channel (Nav 1.5) blockers

Future Medicinal Chemistry ◽

10.4155/fmc-2020-0156 ◽

2020 ◽

Vol 12 (20) ◽

pp. 1829-1843

Author(s):

Noorain Khalifa ◽

Leela Sarath Kumar Konda ◽

Rajendra Kristam

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Ion Channel ◽

Small Molecules ◽

Sodium Ion ◽

Cardiac Complications ◽

Machine Learning Classification ◽

Qsar Models ◽

Experimental Approaches ◽

Integral Element

Aim: Conventional experimental approaches used for the evaluation of the proarrhythmic potential of compounds in the drug discovery process are expensive and time consuming but an integral element in the safety profile required for a new drug to be approved. The voltage-gated sodium ion channel 1.5 (Nav 1.5), a target known for arrhythmic drugs, causes adverse cardiac complications when the channel is blocked. Results: Machine learning classification and regression models were built to predict the possibility of blocking these channels by small molecules. The finalized models tested with balanced accuracies of 0.88, 0.93 and 0.94 at three thresholds (1, 10 and 30 µmol, respectively). The regression model built to predict the pIC50 of compounds had q2 of 0.84 (root-mean-square error = 0.46). Conclusion: The machine learning models that have been built can act as effective filters to screen out the potentially toxic compounds in the early stages of drug discovery.

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