The document discusses QSAR analysis and validation studies conducted on aminoquinoline derivatives as inhibitors of the melanin-concentrating hormone receptor 1 (MCH1R) to treat obesity. Materials and methods describe using the TSAR software to build QSAR models to predict biological activity. The results and discussion are not shown but are expected to analyze relationships between the derivatives' structures and their MCH1R inhibition abilities. The goal is to develop effective and selective MCH1R inhibitor drugs for obesity treatment.
The document describes a molecular docking study of aspirin and aspirin derivatives using the HVR protein (HIV protease receptor). The study found that compounds SR-03, SR-02, and SR-04 showed the best docking scores and interactions with the HVR protein, indicating they may have potential anti-HIV activity. It was concluded that electron withdrawing groups attached to the aryl substituent of the carboxylic acid group in aspirin increase affinity for the HIV protein, while electron donating groups decrease affinity. Further studies are needed to determine the exact mechanisms of action.
CSC Supercomputing Services @ Vaasa University 18.2.2015Olli-Pekka Lehto
This document provides an overview of CSC Supercomputing Services in Finland. It discusses the evolution of supercomputers from specialized systems to clusters of commodity servers. CSC operates several supercomputing systems for academic users, including Sisu, a Cray XC40 with over 40,000 cores, and Taito, a cluster with over 18,000 cores. CSC has over 4,000 customers and provides high performance computing resources, storage, networking and support services to the research community in Finland.
From the Archives: Future of Supercomputing at Altparty 2009Olli-Pekka Lehto
This document summarizes the state of high performance computing (HPC) and looks towards the future. It discusses how HPC systems are transitioning to use more commodity components like standard servers and processors. Reaching exascale computing power, or 1018 floating point operations per second, by 2020 will be very challenging and require major innovations in hardware, software, and programming models. Emerging technologies like accelerators, 3D chip stacking, and new memory types have potential to help overcome current barriers and improve the efficiency of future exascale systems.
Open-Source Bioinformatics for Data Scientists with Amanda SchierzJessica Willis
This document discusses open source bioinformatics tools and resources for data scientists working in drug discovery. It provides an overview of recent projects involving druggability prediction, protein structure and function prediction, and identification of new targets for cancer. It also summarizes key steps in the drug discovery process and some of the main challenges, including drug resistance and tumor heterogeneity. Resources mentioned include databases of protein structures, drug data, gene expression and pathways involved in DNA damage response.
Type I diabetes is an autoimmune disease where the body destroys pancreatic beta cells and cannot produce insulin. It is treated through lifelong insulin injections or pumps and blood glucose monitoring. Type II diabetes occurs when the body does not properly use insulin and is mainly treated through diet, exercise, and oral medications. Hormone therapy is used to treat various cancers like breast and prostate cancer and involves drugs that block hormones from fueling cancer growth.
The document discusses the new Artistry Pure White skincare line. It provides an overview of the brand's research and development process, key ingredients and technologies used, clinical study results, and product benefits. The line aims to prevent, treat, and protect the skin through a holistic approach to balancing melanin production and maintaining natural white beauty. Common questions about the products are also addressed.
Soluble Lectin-Like Oxidized LDL Receptor-1 and High-Sensitivity Troponin T a...Nagi Abdalla
This study evaluated soluble lectin-like oxidized LDL receptor-1 (sLOX-1) and high-sensitivity troponin T (hs-TnT) as diagnostic biomarkers for acute coronary syndrome (ACS). sLOX-1 levels and hs-TnT levels were measured in 200 patients and found to be higher in ACS patients compared to those with non-ACS conditions. Both biomarkers showed diagnostic value for ACS, with sLOX-1 detecting some early-stage ACS cases that hs-TnT missed. However, neither biomarker was fully specific, as elevated levels were also seen in some non-ACS conditions. The combination of sLOX-1 and hs
Leptin is a hormone secreted by adipose tissue that regulates food intake and energy expenditure. It works through the hypothalamus, stimulating neurons that reduce appetite and increase metabolism, while inhibiting neurons that induce feeding. Conditions like lesions in the hypothalamus or leptin resistance can disrupt this system and lead to obesity or anorexia. Obese individuals often have high leptin levels but are resistant to its effects, causing further weight gain despite the body's attempts to reduce food intake through leptin signaling.
The document describes a molecular docking study of aspirin and aspirin derivatives using the HVR protein (HIV protease receptor). The study found that compounds SR-03, SR-02, and SR-04 showed the best docking scores and interactions with the HVR protein, indicating they may have potential anti-HIV activity. It was concluded that electron withdrawing groups attached to the aryl substituent of the carboxylic acid group in aspirin increase affinity for the HIV protein, while electron donating groups decrease affinity. Further studies are needed to determine the exact mechanisms of action.
CSC Supercomputing Services @ Vaasa University 18.2.2015Olli-Pekka Lehto
This document provides an overview of CSC Supercomputing Services in Finland. It discusses the evolution of supercomputers from specialized systems to clusters of commodity servers. CSC operates several supercomputing systems for academic users, including Sisu, a Cray XC40 with over 40,000 cores, and Taito, a cluster with over 18,000 cores. CSC has over 4,000 customers and provides high performance computing resources, storage, networking and support services to the research community in Finland.
From the Archives: Future of Supercomputing at Altparty 2009Olli-Pekka Lehto
This document summarizes the state of high performance computing (HPC) and looks towards the future. It discusses how HPC systems are transitioning to use more commodity components like standard servers and processors. Reaching exascale computing power, or 1018 floating point operations per second, by 2020 will be very challenging and require major innovations in hardware, software, and programming models. Emerging technologies like accelerators, 3D chip stacking, and new memory types have potential to help overcome current barriers and improve the efficiency of future exascale systems.
Open-Source Bioinformatics for Data Scientists with Amanda SchierzJessica Willis
This document discusses open source bioinformatics tools and resources for data scientists working in drug discovery. It provides an overview of recent projects involving druggability prediction, protein structure and function prediction, and identification of new targets for cancer. It also summarizes key steps in the drug discovery process and some of the main challenges, including drug resistance and tumor heterogeneity. Resources mentioned include databases of protein structures, drug data, gene expression and pathways involved in DNA damage response.
Type I diabetes is an autoimmune disease where the body destroys pancreatic beta cells and cannot produce insulin. It is treated through lifelong insulin injections or pumps and blood glucose monitoring. Type II diabetes occurs when the body does not properly use insulin and is mainly treated through diet, exercise, and oral medications. Hormone therapy is used to treat various cancers like breast and prostate cancer and involves drugs that block hormones from fueling cancer growth.
The document discusses the new Artistry Pure White skincare line. It provides an overview of the brand's research and development process, key ingredients and technologies used, clinical study results, and product benefits. The line aims to prevent, treat, and protect the skin through a holistic approach to balancing melanin production and maintaining natural white beauty. Common questions about the products are also addressed.
Soluble Lectin-Like Oxidized LDL Receptor-1 and High-Sensitivity Troponin T a...Nagi Abdalla
This study evaluated soluble lectin-like oxidized LDL receptor-1 (sLOX-1) and high-sensitivity troponin T (hs-TnT) as diagnostic biomarkers for acute coronary syndrome (ACS). sLOX-1 levels and hs-TnT levels were measured in 200 patients and found to be higher in ACS patients compared to those with non-ACS conditions. Both biomarkers showed diagnostic value for ACS, with sLOX-1 detecting some early-stage ACS cases that hs-TnT missed. However, neither biomarker was fully specific, as elevated levels were also seen in some non-ACS conditions. The combination of sLOX-1 and hs
Leptin is a hormone secreted by adipose tissue that regulates food intake and energy expenditure. It works through the hypothalamus, stimulating neurons that reduce appetite and increase metabolism, while inhibiting neurons that induce feeding. Conditions like lesions in the hypothalamus or leptin resistance can disrupt this system and lead to obesity or anorexia. Obese individuals often have high leptin levels but are resistant to its effects, causing further weight gain despite the body's attempts to reduce food intake through leptin signaling.
The document discusses docking, which predicts the optimal binding configuration between two molecules by optimizing their orientation and interaction energy. It describes protein-protein docking where both molecules are rigid, and protein-ligand docking where the ligand is flexible but the protein is rigid. It also discusses the AutoDock software, which uses grids and heuristic search algorithms like genetic algorithms to model docking. It provides examples of docking interleukin-10 to an alkaloid ligand, and nuclear factor kappa-B to a ligand. The document advises considering compounds with binding energies close to or better than a positive control as potential hits.
The endocrine system helps regulate body activities through hormones. The hypothalamus and pituitary gland control other endocrine glands like the thyroid, parathyroid, adrenals, pancreas, gonads, thymus and pineal gland. The hypothalamus secretes hormones that signal the pituitary gland, which then secretes hormones that signal other glands. These glands secrete hormones like insulin, glucagon, thyroid hormones, estrogen and testosterone to regulate processes in the body including metabolism, growth, reproduction and behavior. Hormone levels are regulated through feedback mechanisms to maintain homeostasis.
Ab initio protein structure prediction uses computational methods to predict a protein's 3D structure from its amino acid sequence. It relies on conformational searching to generate structure decoys and selecting native-like models. The key factors for success are an accurate energy function, efficient search methods like molecular dynamics or genetic algorithms, and effective selection of models close to the native structure. Model selection approaches include energy evaluations, compatibility scores, clustering of similar decoys, and identifying the lowest energy conformations.
This document discusses carbon abatement technology, including capturing carbon through methods like carbon capture and storage (CCS) and biomass co-firing. It also discusses reducing CO2 through processes like bio-energy with CCS and biochar. Additional topics covered include scrubbing flue gases to separate CO2, transporting captured CO2 through pipelines or ships, and storing carbon through geological sequestration. The document concludes that carbon abatement technologies have been demonstrated but major costs come from equipment, energy penalties of CCS, and transporting and storing CO2.
The document provides an overview of protein-ligand docking, which is a computational method used in structure-based drug design to predict how small molecules bind to proteins. It discusses key components of docking software including search algorithms that generate poses of ligands in the binding site and scoring functions that calculate binding affinity scores. The document also touches on uses of docking like virtual screening and pose prediction, as well as considerations like flexible docking and handling protein conformations.
Protein 3D structure and classification database nadeem akhter
This document discusses various aspects of protein structure and modeling techniques. It begins with an introduction to proteins and their basic structures. It then discusses the primary structures of proteins including amino acids. Later, it describes different levels of protein structure such as secondary structure involving alpha helices and beta sheets, tertiary structure involving the overall shape of the protein, and quaternary structure involving multiple polypeptide chains. The document also discusses modeling techniques like threading/fold recognition to predict structure based on sequence similarity and ab initio modeling to predict structure from sequence alone.
The intention of this resource is to provide you with enough information to produce a high quality reports and literature reviews.
You may need to produce several small reports during the course of your undergraduate study as part of group coursework assignments. This guide along with other provide support.
protein structure prediction methods. homology modelling, fold recognition, threading, ab initio methods. in short and easy form slides. after one time read you can easily understand methods for protein structure prediction.
This document discusses disorders of pigmentation. It begins by explaining that skin color is determined primarily by melanin, which is produced by melanocytes and transferred to keratinocytes. It then covers an overview of pigmentation disorders and their social implications. The rest of the document delves into specific hyperpigmentation and hypopigmentation disorders, providing details on classification, causes, characteristics, and examples of each type.
protein sturcture prediction and molecular modellingDileep Paruchuru
This document discusses molecular modeling and protein structure prediction. It begins by introducing molecular modeling as a combination of computational chemistry and computer graphics that allows scientists to generate and present molecular data. It then discusses the two main computational methods for molecular modeling - molecular mechanics and quantum mechanics. The document goes on to discuss molecular mechanics in more detail and its applications. It also discusses protein structure and function, the challenges of protein structure prediction, and the goals of protein structure prediction.
Cut out in Carbon Emisson is one of the most important topic amongst all the countries.This presentation emphasis on methods by which Carbon emssion can be reduce..
Molecular docking is a method that predicts the preferred orientation of one molecule to another when bound to form a stable complex. It involves finding the best "fit" between a small molecule ligand and a protein receptor binding site. The key stages are target selection and preparation, ligand selection and preparation, docking, and evaluation. Docking software uses scoring functions to evaluate the strength of interaction and identify the best binding orientation. Applications include virtual screening in drug discovery and predicting enzyme-substrate interactions in bioremediation.
This literature review examines key performance indicators (KPIs) for adult and community education (ACE) organizations. It discusses the challenges of evaluating outcomes for ACE providers given their focus on both educational and social outcomes. It reviews management tools from industry, services, and education to identify an approach suitable for voluntary ACE providers. The balanced scorecard is discussed as a potential tool, but the review argues KPIs for voluntary ACE must focus on quality as an outcome and avoid interfering with volunteers' work. Further research is needed on measuring social outcomes and adapting evaluation methods for the ACE sector.
A lecture on molecular docking that I give for master students at University Paris Diderot.
Warning: this presentation has numerous animations which are not included in the slideshare document.
https://florentbarbault.wordpress.com/
The experimental methods used by biotechnologists to determine the structures of proteins demand sophisticated equipment and time.
A host of computational methods are developed to predict the location of secondary structure elements in proteins for complementing or creating insights into experimental results.
Chou-Fasman algorithm is an empirical algorithm developed for the prediction of protein secondary structure
Insilico methods for design of novel inhibitors of Human leukocyte elastaseJayashankar Lakshmanan
Oral contributed paper “Insilico methods for design of novel inhibitors of Human leukocyte elastase” in the International conference on Systemics, Cybernetics and Informatics-2006
This document provides an overview of quantitative structure-property relationship (QSPR) modeling for drug disposition prediction. It discusses why QSPRs are useful, the general methodology used in QSPR modeling including descriptor generation, statistical analysis and model validation. Specific approaches covered include multiple linear regression, partial least squares, artificial neural networks and internal/external validation techniques. The overall goal of a QSPR approach is to mathematically relate molecular descriptors to physicochemical properties and pharmacokinetic parameters to allow for drug property prediction without additional experiments.
Qsar Studies on Gallic Acid Derivatives and Molecular Docking Studies of Bace...bioejjournal
It is reported that Alzheimer disease is linked with hypertension, diabetes type 2 and high cholesterolemia.
The underlying genetic cause relating these diseases are not well studied clinically. But it has been widely
accepted that beta secretase (BACE1) is the main culprit of causing Alzheimer disease. This enzyme comes
under peptidase A1 family. In the present work, ligand based and structure based drug designing have been
reported. QSAR studies were done using 21 gallic acid derivatives dataset to develop good predictive
model in order to predict biological activity and certain descriptors was reported to further enhance the
analgesic activity of gallic acid derivatives. Molecular docking studies were performed in order to find structure based drug design. Two natural gallic acid derivative have been repoted as a potent inhibitor to beta secretase enzyme.
Qsar studies on gallic acid derivatives and molecular docking studies of bace...bioejjournal
It is reported that Alzheimer disease is linked with hypertension, diabetes type 2 and high cholesterolemia. The underlying genetic cause relating these diseases are not well studied clinically. But it has been widely
accepted that beta secretase (BACE1) is the main culprit of causing Alzheimer disease. This enzyme comes under peptidase A1 family. In the present work, ligand based and structure based drug designing have been reported. QSAR studies were done using 21 gallic acid derivatives dataset to develop good predictive
model in order to predict biological activity and certain descriptors was reported to further enhance the
analgesic activity of gallic acid derivatives. Molecular docking studies were performed in order to find
structure based drug design. Two natural gallic acid derivative have been repoted as a potent inhibitor to beta secretase enzyme.
Qsar Studies on Gallic Acid Derivatives and Molecular Docking Studies of Bace...bioejjournal
It is reported that Alzheimer disease is linked with hypertension, diabetes type 2 and high cholesterolemia. The underlying genetic cause relating these diseases are not well studied clinically. But it has been widely accepted that beta secretase (BACE1) is the main culprit of causing Alzheimer disease. This enzyme comes under peptidase A1 family. In the present work, ligand based and structure based drug designing have been
reported. QSAR studies were done using 21 gallic acid derivatives dataset to develop good predictive model in order to predict biological activity and certain descriptors was reported to further enhance the analgesic activity of gallic acid derivatives. Molecular docking studies were performed in order to find
structure based drug design. Two natural gallic acid derivative have been repoted as a potent inhibitor to beta secretase enzyme.
The document discusses docking, which predicts the optimal binding configuration between two molecules by optimizing their orientation and interaction energy. It describes protein-protein docking where both molecules are rigid, and protein-ligand docking where the ligand is flexible but the protein is rigid. It also discusses the AutoDock software, which uses grids and heuristic search algorithms like genetic algorithms to model docking. It provides examples of docking interleukin-10 to an alkaloid ligand, and nuclear factor kappa-B to a ligand. The document advises considering compounds with binding energies close to or better than a positive control as potential hits.
The endocrine system helps regulate body activities through hormones. The hypothalamus and pituitary gland control other endocrine glands like the thyroid, parathyroid, adrenals, pancreas, gonads, thymus and pineal gland. The hypothalamus secretes hormones that signal the pituitary gland, which then secretes hormones that signal other glands. These glands secrete hormones like insulin, glucagon, thyroid hormones, estrogen and testosterone to regulate processes in the body including metabolism, growth, reproduction and behavior. Hormone levels are regulated through feedback mechanisms to maintain homeostasis.
Ab initio protein structure prediction uses computational methods to predict a protein's 3D structure from its amino acid sequence. It relies on conformational searching to generate structure decoys and selecting native-like models. The key factors for success are an accurate energy function, efficient search methods like molecular dynamics or genetic algorithms, and effective selection of models close to the native structure. Model selection approaches include energy evaluations, compatibility scores, clustering of similar decoys, and identifying the lowest energy conformations.
This document discusses carbon abatement technology, including capturing carbon through methods like carbon capture and storage (CCS) and biomass co-firing. It also discusses reducing CO2 through processes like bio-energy with CCS and biochar. Additional topics covered include scrubbing flue gases to separate CO2, transporting captured CO2 through pipelines or ships, and storing carbon through geological sequestration. The document concludes that carbon abatement technologies have been demonstrated but major costs come from equipment, energy penalties of CCS, and transporting and storing CO2.
The document provides an overview of protein-ligand docking, which is a computational method used in structure-based drug design to predict how small molecules bind to proteins. It discusses key components of docking software including search algorithms that generate poses of ligands in the binding site and scoring functions that calculate binding affinity scores. The document also touches on uses of docking like virtual screening and pose prediction, as well as considerations like flexible docking and handling protein conformations.
Protein 3D structure and classification database nadeem akhter
This document discusses various aspects of protein structure and modeling techniques. It begins with an introduction to proteins and their basic structures. It then discusses the primary structures of proteins including amino acids. Later, it describes different levels of protein structure such as secondary structure involving alpha helices and beta sheets, tertiary structure involving the overall shape of the protein, and quaternary structure involving multiple polypeptide chains. The document also discusses modeling techniques like threading/fold recognition to predict structure based on sequence similarity and ab initio modeling to predict structure from sequence alone.
The intention of this resource is to provide you with enough information to produce a high quality reports and literature reviews.
You may need to produce several small reports during the course of your undergraduate study as part of group coursework assignments. This guide along with other provide support.
protein structure prediction methods. homology modelling, fold recognition, threading, ab initio methods. in short and easy form slides. after one time read you can easily understand methods for protein structure prediction.
This document discusses disorders of pigmentation. It begins by explaining that skin color is determined primarily by melanin, which is produced by melanocytes and transferred to keratinocytes. It then covers an overview of pigmentation disorders and their social implications. The rest of the document delves into specific hyperpigmentation and hypopigmentation disorders, providing details on classification, causes, characteristics, and examples of each type.
protein sturcture prediction and molecular modellingDileep Paruchuru
This document discusses molecular modeling and protein structure prediction. It begins by introducing molecular modeling as a combination of computational chemistry and computer graphics that allows scientists to generate and present molecular data. It then discusses the two main computational methods for molecular modeling - molecular mechanics and quantum mechanics. The document goes on to discuss molecular mechanics in more detail and its applications. It also discusses protein structure and function, the challenges of protein structure prediction, and the goals of protein structure prediction.
Cut out in Carbon Emisson is one of the most important topic amongst all the countries.This presentation emphasis on methods by which Carbon emssion can be reduce..
Molecular docking is a method that predicts the preferred orientation of one molecule to another when bound to form a stable complex. It involves finding the best "fit" between a small molecule ligand and a protein receptor binding site. The key stages are target selection and preparation, ligand selection and preparation, docking, and evaluation. Docking software uses scoring functions to evaluate the strength of interaction and identify the best binding orientation. Applications include virtual screening in drug discovery and predicting enzyme-substrate interactions in bioremediation.
This literature review examines key performance indicators (KPIs) for adult and community education (ACE) organizations. It discusses the challenges of evaluating outcomes for ACE providers given their focus on both educational and social outcomes. It reviews management tools from industry, services, and education to identify an approach suitable for voluntary ACE providers. The balanced scorecard is discussed as a potential tool, but the review argues KPIs for voluntary ACE must focus on quality as an outcome and avoid interfering with volunteers' work. Further research is needed on measuring social outcomes and adapting evaluation methods for the ACE sector.
A lecture on molecular docking that I give for master students at University Paris Diderot.
Warning: this presentation has numerous animations which are not included in the slideshare document.
https://florentbarbault.wordpress.com/
The experimental methods used by biotechnologists to determine the structures of proteins demand sophisticated equipment and time.
A host of computational methods are developed to predict the location of secondary structure elements in proteins for complementing or creating insights into experimental results.
Chou-Fasman algorithm is an empirical algorithm developed for the prediction of protein secondary structure
Insilico methods for design of novel inhibitors of Human leukocyte elastaseJayashankar Lakshmanan
Oral contributed paper “Insilico methods for design of novel inhibitors of Human leukocyte elastase” in the International conference on Systemics, Cybernetics and Informatics-2006
This document provides an overview of quantitative structure-property relationship (QSPR) modeling for drug disposition prediction. It discusses why QSPRs are useful, the general methodology used in QSPR modeling including descriptor generation, statistical analysis and model validation. Specific approaches covered include multiple linear regression, partial least squares, artificial neural networks and internal/external validation techniques. The overall goal of a QSPR approach is to mathematically relate molecular descriptors to physicochemical properties and pharmacokinetic parameters to allow for drug property prediction without additional experiments.
Qsar Studies on Gallic Acid Derivatives and Molecular Docking Studies of Bace...bioejjournal
It is reported that Alzheimer disease is linked with hypertension, diabetes type 2 and high cholesterolemia.
The underlying genetic cause relating these diseases are not well studied clinically. But it has been widely
accepted that beta secretase (BACE1) is the main culprit of causing Alzheimer disease. This enzyme comes
under peptidase A1 family. In the present work, ligand based and structure based drug designing have been
reported. QSAR studies were done using 21 gallic acid derivatives dataset to develop good predictive
model in order to predict biological activity and certain descriptors was reported to further enhance the
analgesic activity of gallic acid derivatives. Molecular docking studies were performed in order to find structure based drug design. Two natural gallic acid derivative have been repoted as a potent inhibitor to beta secretase enzyme.
Qsar studies on gallic acid derivatives and molecular docking studies of bace...bioejjournal
It is reported that Alzheimer disease is linked with hypertension, diabetes type 2 and high cholesterolemia. The underlying genetic cause relating these diseases are not well studied clinically. But it has been widely
accepted that beta secretase (BACE1) is the main culprit of causing Alzheimer disease. This enzyme comes under peptidase A1 family. In the present work, ligand based and structure based drug designing have been reported. QSAR studies were done using 21 gallic acid derivatives dataset to develop good predictive
model in order to predict biological activity and certain descriptors was reported to further enhance the
analgesic activity of gallic acid derivatives. Molecular docking studies were performed in order to find
structure based drug design. Two natural gallic acid derivative have been repoted as a potent inhibitor to beta secretase enzyme.
Qsar Studies on Gallic Acid Derivatives and Molecular Docking Studies of Bace...bioejjournal
It is reported that Alzheimer disease is linked with hypertension, diabetes type 2 and high cholesterolemia. The underlying genetic cause relating these diseases are not well studied clinically. But it has been widely accepted that beta secretase (BACE1) is the main culprit of causing Alzheimer disease. This enzyme comes under peptidase A1 family. In the present work, ligand based and structure based drug designing have been
reported. QSAR studies were done using 21 gallic acid derivatives dataset to develop good predictive model in order to predict biological activity and certain descriptors was reported to further enhance the analgesic activity of gallic acid derivatives. Molecular docking studies were performed in order to find
structure based drug design. Two natural gallic acid derivative have been repoted as a potent inhibitor to beta secretase enzyme.
This document provides an introduction to the process of drug design given by Subhasis Banerjee. It discusses key areas of drug design including target identification and validation, lead finding and optimization, and ligand-based and structure-based drug design. It also describes the application of cheminformatics in drug design for data mining large databases of small molecules and proteins. The goal of drug design is to progress from initial hits identified through screening to optimized lead compounds through iterative chemical modifications and testing.
Computational Modeling of Drug Disposition bhupenkalita7
This document discusses in silico modeling techniques for predicting absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of drug candidates. It describes quantitative approaches like pharmacophore modeling and docking studies, as well as qualitative approaches like quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies. Specific techniques are discussed for modeling various ADMET properties like solubility, permeability, plasma protein binding, blood-brain barrier penetration, and clearance. Transporters, ionization, and data quality are also mentioned as important factors. Commercial software packages are noted that can simulate these processes.
Novel Approaches to Elucidating Structure Activity RelationshipsChristopher Petersen
The document describes new approaches for displaying structure-activity relationship data using Chemoprints and HierS scaffolds. Chemoprints aggregate compound activity data by target, allowing biologists to discover off-target activities. HierS scaffolds classify structural elements to help chemists discover actively features. Together these enable navigation between compound activities, targets, experiments, and structural features to gain insights into activity relationships.
This document describes the Reaxys Medicinal Chemistry database and how it can be used for drug discovery tasks. The database contains over 5 million substances and 25 million biological data points. It organizes and normalizes data to calculate pX values for comparing biological results. A heatmap allows comparing pX values by filtering and adjusting axes. The document demonstrates searches for in vitro activity data, cytochrome P450 inhibition data, PK data, and toxicity data. It also shows how to find substances active against a target but not others.
The document discusses quantitative structure-activity relationship (QSAR) modeling techniques. It defines QSAR as theoretical models that correlate biological activity or properties of molecules to their physicochemical properties. The goal of QSAR is to develop mathematical functions that describe the relationship between biological activity and properties. It discusses advantages of QSAR, classical/2D and 3D-QSAR modeling procedures, and provides examples of specific 3D-QSAR techniques like comparative molecular field analysis.
Ligand based drug design relies on knowledge of molecules that bind to biological targets of interest. Key approaches include molecular fingerprint and structure searches to identify structurally similar molecules, pharmacophore modeling to describe common physicochemical properties responsible for binding, and QSAR to quantify relationships between biological activity and measurable molecular properties. QSAR uses descriptors like molecular weight, logP, and charge to develop mathematical models that can predict activity of new compounds. Proper data processing is important for building reliable QSAR models. These ligand based techniques provide predictive models for lead identification and optimization in drug design.
The document discusses approaches to novel drug development, specifically quantitative structure-activity relationship (QSAR) modeling and high-throughput screening (HTS). It provides background on QSAR, describing how it establishes mathematical relationships between molecular properties and biological activity. It also outlines the history, goals and process of HTS, noting it allows rapid testing of large numbers of compounds against biological targets to identify initial hits for further development.
Scoring and ranking of metabolic trees to computationally prioritize chemical...Kamel Mansouri
The aim of this work was to design an in silico and in vitro approach to prioritize compounds and perform a quantitative safety assessment. To this end, we pursue a tiered approach taking into account bioactivity and bioavailability of chemicals and their metabolites using a human uterine epithelial cell (Ishikawa)-based assay. This biologically relevant fit-for-purpose assay was designed to quantitatively recapitulate in vivo human response and establish a margin of safety.
This document discusses the process of drug design and development, beginning with identifying lead compounds that can bind to protein receptors and modify their function. It then outlines the steps of target validation, high-throughput screening, lead optimization, preclinical and clinical drug development. Specific techniques discussed include structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs) to help modify lead compounds. The document also briefly covers pharmacokinetics, the formulation of an HIV-1 protease inhibitor, and its mechanism of binding to the active site of the protease enzyme to lower viral levels.
Basics of QSAR Modeling by Prof Rahul D. Jawarkar.pptxRahul Jawarkar
This document provides an overview of quantitative structure-activity relationship (QSAR) modeling. It defines QSAR as a multivariate mathematical relationship between molecular descriptors (e.g. physicochemical properties) and biological activity. The document outlines the important steps in QSAR analysis, including experimental data collection, structure drawing, descriptor calculation, model building, and model validation. It also discusses trends in QSAR like the use of artificial intelligence and provides examples of successful QSAR-based virtual screening.
Chemical prioritization using in silico modeling. SOT 2018 (San Antonio, USA)Kamel Mansouri
The aim of this work was to design an in silico and in vitro approach to prioritize compounds and perform a quantitative safety assessment. To this end, we pursue a tiered approach taking into account bioactivity and bioavailability of chemicals and their metabolites using a human uterine epithelial cell (Ishikawa)-based assay. This biologically relevant fit-for-purpose assay was designed to quantitatively recapitulate in vivo human response and establish a margin of safety.
Cadd and molecular modeling for M.PharmShikha Popali
THE CADD IS FOR THE DRUG DEVELOPMENT THE DIFFERENT STRATEGIES ARE MENTIONED LIKE QSAR MOLECULAR DOCKING, THE DIFFERENT DIMNSIONAL FORMS OF QSAR , THE ADVANCE SAR of it.
Molecular modelling for in silico drug discoveryLee Larcombe
This document provides an overview of molecular modelling techniques used for in silico drug discovery. It discusses using computational approaches to model small molecule and protein interactions to assess drug safety and efficacy. The key techniques covered include obtaining protein structures from databases like PDB, simulating molecular interactions through docking and screening, and considering factors like binding affinity, pharmacokinetics and toxicity during the drug design process. Computational protein structure prediction is also discussed as an important technique when experimental structures are unavailable.
The document discusses various topics related to drug design and discovery including structure-based drug design, quantitative structure-activity relationships (QSAR), molecular docking, and de novo drug design. It provides details on the drug discovery process, strategies for structure-based design including pharmacophore identification and docking simulations, factors that govern drug design such as physicochemical properties, and methods for QSAR model development, validation, and applications in drug design.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
Abhishek seminar
1. PROJECT NAMEPROJECT NAME
QSAR ANALYSIS ANDQSAR ANALYSIS AND
VALIDATION STUDIES ONVALIDATION STUDIES ON
AMINOQUINOLINE DERIVATIVESAMINOQUINOLINE DERIVATIVES
AS MELANIN CONCENTRATINGAS MELANIN CONCENTRATING
HORMONE-1R INHIBITORSHORMONE-1R INHIBITORS
BYBY
M.ABHISHEKM.ABHISHEK
2. INTRODUCTION TO DRUGINTRODUCTION TO DRUG
DESIGNINGDESIGNING
DEFFINITIONDEFFINITION
Designing of drug molecules basing upon theirDesigning of drug molecules basing upon their
biological targets.biological targets.
It is mainly of three typesIt is mainly of three types
Structure based drugdesignStructure based drugdesign
Denovo based drugdesignDenovo based drugdesign
Analog based drugdesignAnalog based drugdesign
Analog based drug design alsi called QSARAnalog based drug design alsi called QSAR
analysis.analysis.
3. About QSAR STUDIESAbout QSAR STUDIES
QSAR is qualitative structure activityQSAR is qualitative structure activity
relation shiprelation ship
Uses of qsar analysisUses of qsar analysis
Mainly useful for the determinationMainly useful for the determination
physiochemical properties.physiochemical properties.
Useful to predict the biological value of theUseful to predict the biological value of the
molculesmolcules
5. INTRODUCTIONINTRODUCTION
ABOUT MCHABOUT MCH
The underlying causes of obesity are poorly understood butThe underlying causes of obesity are poorly understood but
probably involve complex interactions between manyprobably involve complex interactions between many
neurotransmitter and neuropeptide systems involved in theneurotransmitter and neuropeptide systems involved in the
regulation of food intake and energy balance. Three pieces ofregulation of food intake and energy balance. Three pieces of
evidence indicate that the neuropeptide melanin-concentratingevidence indicate that the neuropeptide melanin-concentrating
hormone (MCH) is an important component of this system.hormone (MCH) is an important component of this system.
Melanin-concentrating hormone (MCH) is a cyclic neuropeptideMelanin-concentrating hormone (MCH) is a cyclic neuropeptide
(human/rat 19 aa) that regulates a variety of functions in mammalian(human/rat 19 aa) that regulates a variety of functions in mammalian
brain, in particular feeding behavior .brain, in particular feeding behavior .
MCH is synthesized in mainly in the lateral hypothalamus and zonaMCH is synthesized in mainly in the lateral hypothalamus and zona
incerta. MCH stimulates feeding,incerta. MCH stimulates feeding,
Recently, an orphan G-protein coupled receptor (SLC-1, GPR24)Recently, an orphan G-protein coupled receptor (SLC-1, GPR24)
has been identified as the receptor of MCH. MCH receptor ishas been identified as the receptor of MCH. MCH receptor is
predicted to contain 7 transmembrane domains, a feature typical ofpredicted to contain 7 transmembrane domains, a feature typical of
G-protein coupled receptorsG-protein coupled receptors
6. Recently, a novel second human MCH receptorRecently, a novel second human MCH receptor
(MCH2R) has been cloned and characterized. MCH2R(MCH2R) has been cloned and characterized. MCH2R
gene encodes a 340 aa protein with 38% identity withgene encodes a 340 aa protein with 38% identity with
MCH1RMCH1R
MOLECULAR CHARACTERIZATIONMOLECULAR CHARACTERIZATION
Orphan G-protein-coupled receptors (GPCRs) areOrphan G-protein-coupled receptors (GPCRs) are
cloned proteins with structural characteristics common tocloned proteins with structural characteristics common to
the GPCRs but that bind unidentified ligands. Orphanthe GPCRs but that bind unidentified ligands. Orphan
GPCRs have been used as targets to identify novelGPCRs have been used as targets to identify novel
transmitter moleculestransmitter molecules
We demonstrate that nanomolar concentrationsWe demonstrate that nanomolar concentrations
of MCH strongly activate SLC-1-relatedof MCH strongly activate SLC-1-related
pathways through G(alpha)i and/or G(alpha)qpathways through G(alpha)i and/or G(alpha)q
proteinsproteins
8. FUNCTION OF MCHFUNCTION OF MCH
Melanin-concentrating hormone (MCH) is a cyclic neuropeptide, whichMelanin-concentrating hormone (MCH) is a cyclic neuropeptide, which
centrally regulates food intake and stress. MCH induces food intake incentrally regulates food intake and stress. MCH induces food intake in
rodents and, more generally, acts as an anabolic signal in energyrodents and, more generally, acts as an anabolic signal in energy
regulation.regulation.
Two receptors for MCH in humans have very recently been characterised,Two receptors for MCH in humans have very recently been characterised,
namely, MCH-R1 and MCH-R2. MCH-R1 has received considerablenamely, MCH-R1 and MCH-R2. MCH-R1 has received considerable
attention, as potent and selective antagonists acting at that receptor displayattention, as potent and selective antagonists acting at that receptor display
anxiolytic, antidepressant and/or anorectic properties.anxiolytic, antidepressant and/or anorectic properties.
ACTIVE SITE AND INACTIVE SITE OF MCHACTIVE SITE AND INACTIVE SITE OF MCH
Human melanin-concentrating hormone (hMCH) and many of its analoguesHuman melanin-concentrating hormone (hMCH) and many of its analogues
are potent but nonspecific ligands for human melanin-concentratingare potent but nonspecific ligands for human melanin-concentrating
hormone receptors 1 and 2 (hMCH-1R and hMCH-2R). To differentiatehormone receptors 1 and 2 (hMCH-1R and hMCH-2R). To differentiate
between the physiological functions of these receptors, selectivebetween the physiological functions of these receptors, selective
antagonists are needed. In this study, analogues of Ac-Arg(6)-cyclo(S-S)antagonists are needed. In this study, analogues of Ac-Arg(6)-cyclo(S-S)
(Cys(7)-Met(8)-Leu(9)-Gly(10)-Arg(11)-Val(12)-Tyr(13)-Arg(14)-Pro(15)-(Cys(7)-Met(8)-Leu(9)-Gly(10)-Arg(11)-Val(12)-Tyr(13)-Arg(14)-Pro(15)-
Cys(16))-NH(2), a high affinity but nonselective agonist at hMCH-1R andCys(16))-NH(2), a high affinity but nonselective agonist at hMCH-1R and
hMCH-2R, were prepared and tested in binding and functional assays onhMCH-2R, were prepared and tested in binding and functional assays on
cells expressing these receptorscells expressing these receptors
9. MATERIALS&METHODSMATERIALS&METHODS
Tsar (Tools for Structure Activity Relationship) is a program used toTsar (Tools for Structure Activity Relationship) is a program used to
investigates quantitative structure activity relationships (QSAR).investigates quantitative structure activity relationships (QSAR).
Tsar is an integrated analysis package for interactive investigationTsar is an integrated analysis package for interactive investigation
of Quantitative Structure-Activity Relationship (QSARs )of Quantitative Structure-Activity Relationship (QSARs )
The major functional areas of Tsar and their significance in theThe major functional areas of Tsar and their significance in the
investigation of quantitative structure-activity relationship (QSARs)investigation of quantitative structure-activity relationship (QSARs)
and is intended to provide all the function require to carry out anyand is intended to provide all the function require to carry out any
QSAR investigation,QSAR investigation,
TSAR uses an integrated approach to provide all componentsTSAR uses an integrated approach to provide all components
together.together.
It uses a chemically aware spreadsheet to store and manipulateIt uses a chemically aware spreadsheet to store and manipulate
different type of data, including:different type of data, including:
Molecular descriptionMolecular description
3D structures3D structures
Activity dataActivity data
Computed dataComputed data
10. SOFTWARE USED INSOFTWARE USED IN
ANALYSIS:ANALYSIS:
The software are: TSAR software and ISIS/DRAW softwareThe software are: TSAR software and ISIS/DRAW software
TSAR software: TSAR software of version 3.3 was used to study theTSAR software: TSAR software of version 3.3 was used to study the
QSAR derivatives. It has TSAR project window, to which molecularQSAR derivatives. It has TSAR project window, to which molecular
data is entered through import/export file system. Multipledata is entered through import/export file system. Multiple
regression analysis is done by taking physiochemical propertiesAregression analysis is done by taking physiochemical propertiesA
description of the basic operation of Tsar and fundamental aspectsdescription of the basic operation of Tsar and fundamental aspects
of the application with which you need to familiar, including the Tsarof the application with which you need to familiar, including the Tsar
interface, how to work with projects, data and views. When you startinterface, how to work with projects, data and views. When you start
with Tsar graphical interface, the first screen that is displayed is thewith Tsar graphical interface, the first screen that is displayed is the
main Tsar window and biological activity. Then a graph was plottedmain Tsar window and biological activity. Then a graph was plotted
in between actual values and predicted values.in between actual values and predicted values.
CORINACORINA: The 3D structure of a molecule is closely related to a: The 3D structure of a molecule is closely related to a
large variety of chemical, physical and biological propertlarge variety of chemical, physical and biological propert
This introduction to CORINA contains the following topics:This introduction to CORINA contains the following topics:
Automatic generation of high quality 3D molecular models providesAutomatic generation of high quality 3D molecular models provides
an introduction to the use of predicting a 3D structurean introduction to the use of predicting a 3D structure
11. ISIS/DRAWISIS/DRAW: This software has several tools,: This software has several tools,
which are used to draw the chemical structure ofwhich are used to draw the chemical structure of
QSAR derivatives. About 88 molecules wereQSAR derivatives. About 88 molecules were
drawn using ISIS Draw 2.3 software and thedrawn using ISIS Draw 2.3 software and the
descriptors were calculated using Tsar 3.3descriptors were calculated using Tsar 3.3
software.software.
QSAR regression analysis for this set ofQSAR regression analysis for this set of
molecules was carried out by considering allmolecules was carried out by considering all
molecules as complete set and removing outliermolecules as complete set and removing outlier
component from complete set to generatecomponent from complete set to generate
training set and test set respectively.training set and test set respectively.
12. STRUCTURE OF SOMESTRUCTURE OF SOME
MOLECULESMOLECULES
N
H
O
F
F
F
N N
N
H
O
F
F
F
N N
N
H
O
F
F
F
N N
N
H
O
F
F
F
N N
N
H
O
F
F
F
N N
N
H
O
F
F
F
N N
N
H
O
F
F
F
N N
Compound s_11_9 Compound s_11_10
Compound s_11_11 Compound s_11_12
Compound s_11_13 Compound s_11_14
Compound s_11_15
13. RESULTS ANDRESULTS AND
DISCUSSIONDISCUSSION
MOLECLE ANALYSISMOLECLE ANALYSIS::
To cover the whole activity range, the data set was randomly divided into training setTo cover the whole activity range, the data set was randomly divided into training set
and test set .QSAR model was constructed based on training set and then validatedand test set .QSAR model was constructed based on training set and then validated
internally using Leave One Out (LOO) technique and extremely by predicting theinternally using Leave One Out (LOO) technique and extremely by predicting the
activity of test set. The relationship between dependent variable (-log 1/C) andactivity of test set. The relationship between dependent variable (-log 1/C) and
independent variable (physiochemical properties) was established by using linearindependent variable (physiochemical properties) was established by using linear
multiple regression analysis using TSAR 3.3 software. Then significant descriptorsmultiple regression analysis using TSAR 3.3 software. Then significant descriptors
are chosen based on the statistical data analysis.are chosen based on the statistical data analysis.
COMPLETE SET:COMPLETE SET:
70 molecules are appended to multiple regression analysis.70 molecules are appended to multiple regression analysis.
EquationsEquationsOriginal Data : Y = 0.055812515*X6 - 2.7095358*X35 - 0.75705647*X39 -Original Data : Y = 0.055812515*X6 - 2.7095358*X35 - 0.75705647*X39 -
1.8342798*X44 - 2.0094008Standardized Data : Y = 0.81183285*S6 -1.8342798*X44 - 2.0094008Standardized Data : Y = 0.81183285*S6 -
0.35177225*S35 - 0.45452115*S39 - 0.61900699*S44 - 1.21242860.35177225*S35 - 0.45452115*S39 - 0.61900699*S44 - 1.2124286CalculationCalculation
InformationInformation70 rows included in model0 rows excluded because of missing data4970 rows included in model0 rows excluded because of missing data49
independent variables considered0 independent variables excluded because ofindependent variables considered0 independent variables excluded because of
missing data0 independent variables in initial model4 variables included in final modelmissing data0 independent variables in initial model4 variables included in final model
using F-test steppingStandardized by mean/SDCross validated leaving out one rowusing F-test steppingStandardized by mean/SDCross validated leaving out one row
randomly over 2 random trialsCorrelation limit of 0.9 applied4 steps to generate finalrandomly over 2 random trialsCorrelation limit of 0.9 applied4 steps to generate final
modelF to enter = 4, F to leave = 4modelF to enter = 4, F to leave = 4Variance AnalysisVariance AnalysisRegression: 4 degrees ofRegression: 4 degrees of
freedom, sum of squares = 63.947Residual: 65 degrees of freedom, sum of squaresfreedom, sum of squares = 63.947Residual: 65 degrees of freedom, sum of squares
= 13.901Total: 69 degrees of freedom, sum of squares = 77.847= 13.901Total: 69 degrees of freedom, sum of squares = 77.847Statistical TestsStatistical Tests
14. QSAR EQUATION:QSAR EQUATION:
log (1/IC50) =log (1/IC50) = + 0.055205099* Inertia Moment 1+ 0.055205099* Inertia Moment 1
LengthLength
- 2.6556225* Balaban Topological index- 2.6556225* Balaban Topological index
- 0.7120384* ADME H-bond Acceptors- 0.7120384* ADME H-bond Acceptors
- 1.8028219* VAMP LUMO- 1.8028219* VAMP LUMO
- 2.0724609- 2.0724609
r = 0.890, r2 = 0.793, cvr2 = 0.700, F = 50.7138, n = 58,r = 0.890, r2 = 0.793, cvr2 = 0.700, F = 50.7138, n = 58,
PRESS = 19.4898, Residual sum = 13.4604.PRESS = 19.4898, Residual sum = 13.4604.
Once the multiple regression analysis is performed onOnce the multiple regression analysis is performed on
the complete set and a statistically significant result isthe complete set and a statistically significant result is
obtained, the next step is to perform multiple regressionobtained, the next step is to perform multiple regression
analysis on training set and test set data.analysis on training set and test set data.
15. TEST SET:TEST SET:
The test set consists of 12 compounds that are separated from theThe test set consists of 12 compounds that are separated from the
complete set of 58 compounds. The test set compounds arecomplete set of 58 compounds. The test set compounds are
selected based on the hierarchical clustering data so that the totalselected based on the hierarchical clustering data so that the total
biological activity range of the complete set is covered.biological activity range of the complete set is covered.
The regression equation obtained from the training set is appendedThe regression equation obtained from the training set is appended
to the test set. Thus the activity of the test set is predicted. Theto the test set. Thus the activity of the test set is predicted. The
predictive ability of the model is estimated from the graph plottedpredictive ability of the model is estimated from the graph plotted
from these values. The predicted values and their correspondingfrom these values. The predicted values and their corresponding
actual value is given below in a table:actual value is given below in a table:
Molecule No.Actual ValuePredicted ValueMolecule No.Actual ValuePredicted Value 1-2.38-2.1105-1-2.38-2.1105-
1.94-1.9768-1.25-1.12216-2.9-2.54118-1.08-0.95219-0.7-0.51022-1.94-1.9768-1.25-1.12216-2.9-2.54118-1.08-0.95219-0.7-0.51022-
0.48-0.33533-1.65-1.53944-3.6-3.19850-0.9-0.6930.48-0.33533-1.65-1.53944-3.6-3.19850-0.9-0.693
16. CONCLUSIONCONCLUSION
QSAR analysis was performed on 70 aminoquinoline MCH 1RQSAR analysis was performed on 70 aminoquinoline MCH 1R
molecules.Training set (58 molecules) , test set (12 molecules) and outliersmolecules.Training set (58 molecules) , test set (12 molecules) and outliers
(18 molecule ) was generated from the complete set of 70 molecules, each(18 molecule ) was generated from the complete set of 70 molecules, each
containing a set of active ,moderately active and inactive molecules. Acontaining a set of active ,moderately active and inactive molecules. A
regression equation was generated using multiple regression analysis onregression equation was generated using multiple regression analysis on
training set. This regression equation was applied on the test set to predicttraining set. This regression equation was applied on the test set to predict
biological activity of test set molecules. The predicted activity was obtainedbiological activity of test set molecules. The predicted activity was obtained
through the regression equation. The QSAR equation generated bythrough the regression equation. The QSAR equation generated by
considering training set molecules resulted identifying Inertia Moment 1considering training set molecules resulted identifying Inertia Moment 1
Length , Balaban Topological Index , ADME H-bond acceptors , VAMPLength , Balaban Topological Index , ADME H-bond acceptors , VAMP
LUMO .LUMO .
Eq. 1 accounts for the significant correlation of the descriptors withEq. 1 accounts for the significant correlation of the descriptors with
biological activity and displayed good internal predictivity as shown by q2biological activity and displayed good internal predictivity as shown by q2
value of 0.700 and was able to explain 79.3% variance of inhibitory activitiesvalue of 0.700 and was able to explain 79.3% variance of inhibitory activities
of MCH-1R inhibitors. The predictive ability of QSAR model illustrated theof MCH-1R inhibitors. The predictive ability of QSAR model illustrated the
accuracy and robustness of QSAR model on test set molecules. Therefore,accuracy and robustness of QSAR model on test set molecules. Therefore,
considering the contributions of these descriptors on aminoquinolineconsidering the contributions of these descriptors on aminoquinoline
derivatives would help in designing novel compounds that enhance MCH-1Rderivatives would help in designing novel compounds that enhance MCH-1R
inhibitioninhibition
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