Introduction of QSAR, Steps involved in QSAR, Hansch Analysis, Free Wilson Analysis, Mixed Approach method, Advantage,Disadvantage and Application of QSAR.
A QSAR is a mathematical relationship between a biological activity of a molecular system and its geometric and chemical characteristics.
QSAR attempts to find consistent relationship between biological activity and molecular properties, so that these “rules” can be used to evaluate the activity of new compounds.
SAR versus QSAR, History and development of QSAR, Types of physicochemical
parameters, experimental and theoretical approaches for the determination of
physicochemical parameters such as Partition coefficient, Hammet’s substituent
constant and Taft’s steric constant. Hansch analysis, Free Wilson analysis, 3D-QSAR
approaches like COMFA and COMSIA.
Introduction of QSAR, Steps involved in QSAR, Hansch Analysis, Free Wilson Analysis, Mixed Approach method, Advantage,Disadvantage and Application of QSAR.
A QSAR is a mathematical relationship between a biological activity of a molecular system and its geometric and chemical characteristics.
QSAR attempts to find consistent relationship between biological activity and molecular properties, so that these “rules” can be used to evaluate the activity of new compounds.
SAR versus QSAR, History and development of QSAR, Types of physicochemical
parameters, experimental and theoretical approaches for the determination of
physicochemical parameters such as Partition coefficient, Hammet’s substituent
constant and Taft’s steric constant. Hansch analysis, Free Wilson analysis, 3D-QSAR
approaches like COMFA and COMSIA.
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
What is QSAR?, introduction to 3D QSAR, CoMFA, CoMSIA, Case Study on CoMFA contour maps analysis and CoMSIA interactive forces between ligand and receptor, various Statistical techniques involved in QSAR
The screening of chemical libraries with traditional methods, such as high-throughput screening (HTS), is expensive and time consuming. Quantitative structure–activity relation (QSAR) modeling is an alternative method that can assist in the selection of lead molecules by using the information from
reference active and inactive compounds. This approach requires good molecular descriptors that are representative of the molecular features responsible for the relevant molecular activity.
Chemical risk assessment is often limited by the lack of experimental toxicity data for a large number of diverse chemicals. In the absence of experimental data, potential chemical hazard is often predicted using data gap filling techniques such as quantitative structure activity relationship (QSAR) models. QSARs are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR tools are a widely utilized alternative to time-consuming clinical and animal testing methods, yet concerns over reliability and uncertainty limit application of QSAR models for regulatory chemical risk assessments. The reliability of a QSAR model depends on the quality and quantity of experimental training data and the applicability domain of the model. This talk will describe the basics concepts and best practices in QSAR modeling, principles associated with validation of QSAR models, summary of available QSAR tools, limitations and challenges in the acceptance of QSAR models, and the current status and prospects of QSAR modeling methods in the medical devices community.
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
What is QSAR?, introduction to 3D QSAR, CoMFA, CoMSIA, Case Study on CoMFA contour maps analysis and CoMSIA interactive forces between ligand and receptor, various Statistical techniques involved in QSAR
The screening of chemical libraries with traditional methods, such as high-throughput screening (HTS), is expensive and time consuming. Quantitative structure–activity relation (QSAR) modeling is an alternative method that can assist in the selection of lead molecules by using the information from
reference active and inactive compounds. This approach requires good molecular descriptors that are representative of the molecular features responsible for the relevant molecular activity.
Chemical risk assessment is often limited by the lack of experimental toxicity data for a large number of diverse chemicals. In the absence of experimental data, potential chemical hazard is often predicted using data gap filling techniques such as quantitative structure activity relationship (QSAR) models. QSARs are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR tools are a widely utilized alternative to time-consuming clinical and animal testing methods, yet concerns over reliability and uncertainty limit application of QSAR models for regulatory chemical risk assessments. The reliability of a QSAR model depends on the quality and quantity of experimental training data and the applicability domain of the model. This talk will describe the basics concepts and best practices in QSAR modeling, principles associated with validation of QSAR models, summary of available QSAR tools, limitations and challenges in the acceptance of QSAR models, and the current status and prospects of QSAR modeling methods in the medical devices community.
Course Outline:
The need for predictive methods
Basic terminology in QSAR development
Selecting biological endpoints for modeling
Using trends to define chemical categories
Chemical categories for filling data gaps
Overview of the QSAR Toolbox
Effect of substituents and functions on drug structure activity relationshipsOmar Sokkar
The replacement, in an active molecule, of a hydrogen atom by a substituent (alkyl, halogen, hydroxyl, nitro, cyano, alkoxy, amino, carboxylate, etc.) or a functional group can deeply modify The potency, The duration, Perhaps even the nature of the pharmacological effect.
Presentation on using the Discovery Bus to develop a new field of research, "Meta QSAR" the comparative study of QSAR modelling methodology. Given at UK QSAR Society meeting at Syngenta October 22nd 2009
Sustainable chemistry is the design and use of chemicals that minimize impacts to human health, ecosystems and the environment. To assess sustainability, chemicals must be evaluated not only for their toxicity to humans and other species, but also for environmental persistence and potential formation of toxic products as a result of biotic and abiotic transformations. Traditional approaches to evaluate these characteristics are resource intensive and normally lack biologically mechanistic information that might facilitate a “safety by design” approach. A more promising approach would exploit recent advances in high-throughput (HT) and high-content (HC) screening methods coupled with computational methods for data analysis and predictive modelling. The elements of a framework to assess sustainable chemistry could rely on integration of non-testing approaches such as (Q)SAR and read-across, coupled with prediction models derived from HT/HC methods anchored to biological pathways (eg., Adverse Outcome Pathways). Acceptance and use of such integrated approaches necessitates a level of validation that demonstrates scientific confidence for specific decision contexts. Here we illustrate a scientific confidence framework for Tox21 approaches underpinned by a mechanistic basis, and illustrate how this will drive the development of enhanced non-testing approaches. This framework also focuses development of prediction models that are hybrid yet local in terms of their chemistry in nature. Specific examples highlight how the extensive testing library within ToxCast was profiled with respect to its chemistry, resulting in new insights that direct strategic testing as well as formulate new predictive models specifically SARs. This abstract does not necessarily reflect U.S. EPA policy.
IOSR Journal of Applied Chemistry (IOSR-JAC) is an open access international journal that provides rapid publication (within a month) of articles in all areas of applied chemistry and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Chemical Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
PRESENTED BY: HARSHPAL SINGH WAHI, SHIKHA D. POPALI
USEFUL FOR PHARMACY STUDENTS AND ACADEMICS, INDUSTRIALS FOR MOLECULE DEVELOPMENT, MODELING, DRUG DISCOVERY, COMPUTATIONAL TOOLS, MOLECULAR DOCKING ITS TYPES, FACTORS AFFECTING, DIFFERENT STAGES, QSAR ADVANTAGES, NEED
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.
THE DRUG DESIGN AND DEVELOPMENT BASED ON DRUG DISCOVERY ,HERE ITS NEED RATIONALE ARE EXPLAINED ALSO QSAR, MOLECULAR DOCKING ITS HISTORY NEED, STRUCTURE BASED DRUG DESIGN IN EASY WAY WE HAVE MENTIONED. THIS WILL MAKE READERS EASY TO COLLECT DATA AT A PLACE ALL OVER THIS IS FOR PHARMA STUDENTS, ACADEMICS, PROFESSIONL AND OST USEFUL FOR RESEARCHERS.
THANK YOU
HOPE YOU WILL LIKE AND SHARE
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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
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During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
32. Annex IX of REACH Substances whose physicochemical, toxicological and ecotoxicological properties are likely to besimilar or follow a regular patternas a result of structural similarity may be considered as a group, or “category” of substances. Application of the group concept requires that physicochemical properties, human health effects and environmental effects or environmental fate may bepredicted from data for a reference substancewithin the group by interpolation to other substances in the group (read-across approach). Thisavoids the need to test every substance for every endpoint.
33.
34. These structural similarities may create a predictable pattern in any or all of the following parameters: physicochemical properties, environmental fate and environmental effects, and human health effectsOECD Manual for Investigation of High Production Volume (HPV) Chemicals.
35. Forming Chemical Categories Chemical categories have boundary conditions which vary with endpoints Without detailed understanding of metabolism or mechanisms, grouping similarity of behavior is difficult to define. Ironically, examining data trends with different category boundaries is a flexible way to define categories
36. Canonical Ordering Chemical Amyl amine Amyl chloride Dibromobenzene Ethyl bromide n-Heptanol Methacrolein Methyl-p-anisylketone n-Octane n-Nonane Boiling Point °C 103-4 98-9 219-2 38.4 192 68 267-9 126 151
37. Canonical Ordering Chemical Ethyl bromide Methacrolein Amyl chloride Amyl amine n-Octane n-Nonane n-Heptanol Dibromobenzene Methyl-p-anisylketone Boiling Point °C 38.4 68 98-9 103-4 126 151 192 219-2 267-9
38. Modeling Chemical Potency 10+2 10 0 10_2 1/LC50 (Moles/L) It is not uncommon to find endpoint values spanning 6-10 orders for a single toxicity mechanism 10_4 10_6 10-8 1 2 3 4 5 N < 10,000 ….... TOXICITY “MECHANISMS”
40. QSAR Methods QSAR fills data gaps by first grouping chemicals and then using existing data within a group to estimate missing values When the chemical group is identified by a common mechanism, QSAR models can accurately describe the trends
41. Why Do We Need the QSAR Toolbox Defining category boundaries requires the calculation of complex attributes of chemicals to determine which best explains available data In many cases, metabolic simulators are needed to provide metabolic maps and active metabolites To do trend analysis, hundreds of available data must be compiled and flexibly analyzed for trends
42. Which Metabolite should we use in modeling interactions? Simulated 2-Acetylaminofluorene Metabolism
43. Adverse Outcome Pathway For A Well-Defined Endpoint Molecular Initiating Event Speciation, Metabolism Reactivity Etc. In Vitro and System Effects In Vivo Adverse Outcomes Parent Chemical Up-Stream Down-Stream CHEMISTRYBIOLOGY Structure-Activity Levels of Organization
44. MolecularInitiating Event Macro -Molecular Interactions Toxicant Chemical Reactivity Profiles Receptor, DNA, Protein Interactions Biological Responses Mechanistic Profiling The Adverse Outcome Pathway
45. MolecularInitiating Event Biological Responses Macro -Molecular Interactions Toxicant Cellular Gene Activation Protein Production Signal Alteration Chemical Reactivity Profiles Receptor, DNA, Protein Interactions NRC Toxicological Pathway The Adverse Outcome Pathway
46. MolecularInitiating Event Biological Responses Macro -Molecular Interactions Tissue/ Organ Toxicant Cellular Gene Activation Protein Production Signal Alteration Receptor, DNA, Protein Interactions Altered Function Altered Development Chemical Reactivity Profiles Mechanistic Profiling In Vitro & HTP Screening The Adverse Outcome Pathway
47. MolecularInitiating Event Biological Responses Macro -Molecular Interactions Toxicant Cellular Organism Organ Population Lethality Sensitization Birth Defect Reproductive Impairment Cancer Gene Activation Protein Production Signal Alteration Altered Function Altered Development Chemical Reactivity Profiles Receptor, DNA, Protein Interactions Structure Extinction Mechanistic Profiling In Vivo Testing In Vitro & HTP Screening The Adverse Outcome Pathway
48. Major Pathways for Reactive Toxicity from Moderate Electrophiles Interaction Mechanisms Molecular Initiating Events In vivo Endpoints Exposed Surface Irritation Michael Addition Schiff base Formation SN2 Acylation Atom Centered Irreversible (Covalent) Binding Necrosis Which Tissues? Pr-S Adducts GSH Oxidation GSH Depletion NH2 Adducts RN Adducts DNA Adducts Oxidative Stress Systemic Responses Skin Liver Lung Systemic Immune Responses Dose-Dependent Effects
49. Organization for Economic Co-operation and Development QSARApplication Toolbox -filling data gaps using available information- Training Workshop Barcelona
58. Assist in the estimation of missing values for chemicals-ENV/JM(2006)47
59. Typical queries included in the (Q)SAR Application Toolbox Is the chemical included in regulatory inventories or existing chemical categories? Has the chemical already been assessed by other agencies/organisations? Would you like to search for available data on assessment endpoints for each chemical?
60. Typical Queries included in the (Q)SAR Application Toolbox Explore a chemical list for possible analogues using predefined, mechanistic, empiric and custom built categorization schemes? Group chemicals based on common chemical/toxic mechanism and/or metabolism? Design a data matrix of a chemical category?
61. QSAR Toolbox Workflow The workflow in the first version of the QSAR Toolbox is to facilitate hazard assessors in the creating of chemical categories which enable data to be extrapolated from tested chemicals to untested members of categories
62. Logical sequence of components usage Chemical input Profiling Category Definition Filling data gap Report Endpoints
The simplest exercise in QSAR is canonical ordering which starts with choosing a group of chemicals, and a selected property or biological activity for each. In this slide, nine chemicals are listed with their boiling points. If we think we understand how chemical structure relates to boiling point, we would expect that those molecular descriptors would place the chemicals in the same order as would the boiling point.
In this slide, the chemicals are sorted by increasing boiling point. Can we identify molecular descriptors that create the same order. If not, we do understand the inter and intramolecular forces that control boiling point. If QSAR can order them properly, the task is then to find chemicals that fit between these values an test the QSAR model. Through numerous iterations , theoretical explanations can be evaluated for relevance and the important molecular descriptors are discovered. This came approach can be used for toxicity data provided a similar toxicity mechanism can be expected for the chemicals.
In this example, I am illustrating that there are many toxicity mechanisms, and if all the chemicals having the same mechanism are compiled, it would not be unusual for the potency of those chemicals to range over 8-10 orders of magnitude. Even if the range were much less, the first challenge for QSAR would be to identify a molecular descriptor that places the chemicals in the same order as the potency measures (LC50). To illustrate, I am using aquatic lethality with fish just to move away from the rodent inhalation example, but keep in mind that a fish test is just an inhalation test with aquatic organisms.
For many mechanisms, uptake of the chemicals is controlled by passive transport and one would expect the octanol/water partition coefficient to covary with passive transport. When the entire range of potency values are plotted vesus Log Ko/w, the chemicals remain in the same order and quantitative relationship between LC50 and Ko/w can be derived exactly like that for the rodent inhalation data.
![Introduction to QSAR ,[object Object]](https://image.slidesharecdn.com/toolboxonlineintro-100121175539-phpapp02/85/Introduction-to-OECD-QSAR-Toolbox-1-320.jpg)
