scholarly journals Machine Learning for the Design and Development of Biofilm Regulators

2018 ◽  
Author(s):  
Benjamin Stone ◽  
Erik Sapper
Keyword(s):  
Machine Learning ◽  
Quorum Sensing ◽  
Computational Models ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Machine Learning Algorithms ◽  
Chemical Signaling ◽  
Biochemical System ◽  
Structure Activity ◽  
Qsar Models

Biofilms are congregations of bacteria on a surface, and they grow into obstacles for the functionalities of any device or machinery involves anything biological. Biofilms are developed through a biochemical system known as ‘Quorum Sensing’ that accounts for the chemical signaling that direct either biofilm formation or inhibition. Computational models that relate chemical and structural features of compounds to their performance properties have been used to aide in the discovery of active small molecules for many decades. These quantitative structure-activity relationship (QSAR) models are also important for predicting the activity of molecules that can have a range of effectiveness in biological systems. This study uses QSAR methodologies combined with and different machine learning algorithms to predict and assess the performance of several different compounds acting in Quorum Sensing. Through computational probing of the quorum sensing molecular interaction, new design rules can be elucidated for countering biofilms.

QSAR modeling for reaction rate constants of eaq− with diverse organic compounds in water

2020 ◽  
Vol 6 (7) ◽  
pp. 1931-1938
Author(s):  
Shanshan Zheng ◽  
Chao Li ◽  
Gaoliang Wei
Keyword(s):  
Organic Compounds ◽  
Reaction Rate ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Reaction Rate Constants ◽  
Structure Activity ◽  
Qsar Models ◽  
The Impact

Two quantitative structure–activity relationship (QSAR) models to predict keaq− of diverse organic compounds were developed and the impact of molecular structural features on eaq− reactivity was investigated.

scholarly journals Structure Features of Peptide-Type SARS-CoV Main Protease Inhibitors: Quantitative Structure Activity Relationship Study

2020 ◽  
Author(s):  
Vijay Masand ◽  
Ajaykumar Gandhi ◽  
Vesna Rastija ◽  
Meghshyam K. Patil
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Atom Pairs ◽  
Main Protease ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships

<div>In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 = 0.80–0.82, Q2loo = 0.74–0.77). The developed models identified interrelations of atom pairs as important molecular descriptors. Therefore, the present QSAR models have a good balance of Qualitative and Quantitative approaches, thereby, useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.</div><div><br></div>

scholarly journals Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads

Molecules ◽  
2021 ◽  
Vol 26 (16) ◽  
pp. 4795
Author(s):  
Ajaykumar Gandhi ◽  
Vijay Masand ◽  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Anis Ben Ghorbal ◽  
...  
Keyword(s):  
De Novo ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Structure Activity ◽  
Tnbc Cell ◽  
Qsar Models ◽  
Anti Tumor Activity

In the present endeavor, for the dataset of 219 in vitro MDA-MB-231 TNBC cell antagonists, a (QSAR) quantitative structure–activity relationships model has been carried out. The quantitative and explicative assessments were performed to identify inconspicuous yet pre-eminent structural features that govern the anti-tumor activity of these compounds. GA-MLR (genetic algorithm multi-linear regression) methodology was employed to build statistically robust and highly predictive multiple QSAR models, abiding by the OECD guidelines. Thoroughly validated QSAR models attained values for various statistical parameters well above the threshold values (i.e., R2 = 0.79, Q2LOO = 0.77, Q2LMO = 0.76–0.77, Q2-Fn = 0.72–0.76). Both de novo QSAR models have a sound balance of descriptive and statistical approaches. Decidedly, these QSAR models are serviceable in the development of MDA-MB-231 TNBC cell antagonists.

scholarly journals Structure Features of Peptide-Type SARS-CoV Main Protease Inhibitors: Quantitative Structure Activity Relationship Study

2020 ◽  
Author(s):  
Vijay Masand ◽  
Ajaykumar Gandhi ◽  
Vesna Rastija ◽  
Meghshyam K. Patil
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Atom Pairs ◽  
Main Protease ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships

<div>In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 = 0.80–0.82, Q2loo = 0.74–0.77). The developed models identified interrelations of atom pairs as important molecular descriptors. Therefore, the present QSAR models have a good balance of Qualitative and Quantitative approaches, thereby, useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.</div><div><br></div>

STRUCTURAL FEATURE STUDY OF QUINOLINES DERIVATIVES WITH THERMODYNAMIC AND OTHER DESCRIPTORS: A QSAR APPROACH

INDIAN DRUGS ◽  
2021 ◽  
Vol 58 (09) ◽  
pp. 21-26
Author(s):  
Mukesh C. Sharma ◽  
Dharm V. Kohli ◽  
Keyword(s):  
Antihypertensive Agents ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Stepwise Multiple Regression ◽  
Angiotensin Ii Receptor ◽  
Method Of Least Squares ◽  
Chemical Thermodynamic ◽  
Relationship Analysis ◽  
Structure Activity ◽  
Qsar Models

Quantitative structure activity relationship analysis was performed on a series of thirty-three quinoline derivatives to establish the structural features required for angiotensin II receptor activity. QSAR models were derived by stepwise multiple regression analysis employing the method of least squares, using quantum chemical, thermodynamic, electronic and steric descriptors. Model showed best predictability of activity with cross validated value (q2 ) =0.7485, coeffi cient of determination (r2 ) =0.8734 and standard error of estimate (s) = 0.2690. These guidelines may be used to develop new antihypertensive agents based on the quinoline analogues scaffold.

scholarly journals Papyrus - A large scale curated dataset aimed at bioactivity predictions

2021 ◽  
Author(s):  
Olivier J. M. Béquignon ◽  
Brandon J. Bongers ◽  
Willem Jespers ◽  
Ad P. IJzerman ◽  
Bob van de Water ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Research Question ◽  
Quantitative Structure Activity Relationship ◽  
Machine Learning Algorithms ◽  
Quality Data ◽  
High Quality Data ◽  
Aggregated Data ◽  
Structure Activity ◽  
The Right

With the recent rapid growth of publicly available ligand-protein bioactivity data, there is a trove of viable data that can be used to train machine learning algorithms. However, not all data is equal in terms of size and quality, and a significant portion of researcher’s time is needed to adapt the data to their needs. On top of that, finding the right data for a research question can often be a challenge on its own. As an answer to that, we have constructed the Papyrus dataset (DOI: 10.4121/16896406), comprised of around 60 million datapoints. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with several smaller datasets containing high quality data. The aggregated data has been standardised and normalised in a manner that is suitable for machine learning. We show how data can be filtered in a variety of ways, and also perform some baseline quantitative structure-activity relationship analyses and proteochemometrics modeling. Our ambition is this pruned data collection constitutes a benchmark set that can be used for constructing predictive models, while also providing a solid baseline for related research.

scholarly journals The Psychonauts’ Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity

2021 ◽  
Vol 14 (8) ◽  
pp. 720
Author(s):  
Valeria Catalani ◽  
Michelle Botha ◽  
John Martin Corkery ◽  
Amira Guirguis ◽  
Alessandro Vento ◽  
...  
Keyword(s):  
Biological Activity ◽  
Computational Models ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Docking Studies ◽  
Health Concern ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
High Biological Activity ◽  
Structure Activity

Designer benzodiazepines (DBZDs) represent a serious health concern and are increasingly reported in polydrug consumption-related fatalities. When new DBZDs are identified, very limited information is available on their pharmacodynamics. Here, computational models (i.e., quantitative structure-activity relationship/QSAR and Molecular Docking) were used to analyse DBZDs identified online by an automated web crawler (NPSfinder®) and to predict their possible activity/affinity on the gamma-aminobutyric acid A receptors (GABA-ARs). The computational software MOE was used to calculate 2D QSAR models, perform docking studies on crystallised GABA-A receptors (6HUO, 6HUP) and generate pharmacophore queries from the docking conformational results. 101 DBZDs were identified online by NPSfinder®. The validated QSAR model predicted high biological activity values for 41% of these DBDZs. These predictions were supported by the docking studies (good binding affinity) and the pharmacophore modelling confirmed the importance of the presence and location of hydrophobic and polar functions identified by QSAR. This study confirms once again the importance of web-based analysis in the assessment of drug scenarios (DBZDs), and how computational models could be used to acquire fast and reliable information on biological activity for index novel DBZDs, as preliminary data for further investigations.

scholarly journals Pervasive Lying Posture Tracking

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5953 ◽  
Author(s):  
Parastoo Alinia ◽  
Ali Samadani ◽  
Mladen Milosevic ◽  
Hassan Ghasemzadeh ◽  
Saman Parvaneh
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Computational Models ◽  
Learning Algorithms ◽  
Pressure Sensors ◽  
Machine Learning Algorithms ◽  
Sensor System ◽  
Accurate Detection ◽  
Research Questions ◽  
Posture Tracking

Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

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