DRUG DESIGN BASED ON BIOINFORMATICS TOOLSNIPER MOHALI
Drug design is a very complex process it takes many more times but using the these specific tools we can reduce complex process and save the time and produce a effective new drug that will be helpful in heath environment.
DRUG DESIGN BASED ON BIOINFORMATICS TOOLSNIPER MOHALI
Drug design is a very complex process it takes many more times but using the these specific tools we can reduce complex process and save the time and produce a effective new drug that will be helpful in heath environment.
Computational Drug Discovery: Machine Learning for Making Sense of Big Data i...Chanin Nantasenamat
In this lecture, I provide an overview on how computers can be instrumental in drug discovery efforts. Topics covered includes: big data as a result of omics effort; bioinformatics; cheminformatics; biological space; chemical space; how computers particularly machine learning (and data science) can be applied in the context of drug discovery.
A video of this lecture is also provided on the "Data Professor" YouTube channel available at http://bit.ly/dataprofessor
If you are fascinated about data science, it would mean the world to me if you would consider subscribing to this channel (by clicking the link below):
http://bit.ly/dataprofessor
In silico drug designing is the drug design which can be carried out in silicon chip,i.e., within computers. The slides are helpful to know a brief description about in silico drug designing.
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
The slide provides a basic understanding about Clinical Research process and the various Phases of Drug Discovery and Development. It also explains about the various trial designs and techniques in research such as blinding and randomization. It may be useful for giving a basic class for Fourth Year B.Pharm Students.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Computational Drug Discovery: Machine Learning for Making Sense of Big Data i...Chanin Nantasenamat
In this lecture, I provide an overview on how computers can be instrumental in drug discovery efforts. Topics covered includes: big data as a result of omics effort; bioinformatics; cheminformatics; biological space; chemical space; how computers particularly machine learning (and data science) can be applied in the context of drug discovery.
A video of this lecture is also provided on the "Data Professor" YouTube channel available at http://bit.ly/dataprofessor
If you are fascinated about data science, it would mean the world to me if you would consider subscribing to this channel (by clicking the link below):
http://bit.ly/dataprofessor
In silico drug designing is the drug design which can be carried out in silicon chip,i.e., within computers. The slides are helpful to know a brief description about in silico drug designing.
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
The slide provides a basic understanding about Clinical Research process and the various Phases of Drug Discovery and Development. It also explains about the various trial designs and techniques in research such as blinding and randomization. It may be useful for giving a basic class for Fourth Year B.Pharm Students.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
1. Khwaja Moinuddin Chishti
Language University
Department of Biotechnology
Bioinformatics in Drug designing
Subject Head-Dr.Mamta Shukla
Presented By- SparshTiwari
(3rd Year // 5th sem)
2. Important Points in Drug Design based on
Bioinformatics Tools
History of Drug/Vaccine development
– Plants or Natural Product
• Plant and Natural products were source for medical substance
• Example: foxglove used to treat congestive heart failure
• Foxglove contain digitalis and cardiotonic glycoside
• Identification of active component
– Accidental Observations
• Penicillin is one good example
• Alexander Fleming observed the effect of mold
• Mold(Penicillium) produce substance penicillin
• Discovery of penicillin lead to large scale screening
• Soil micoorganism were grown and tested
• Streptomycin, neomycin, gentamicin, tetracyclines etc.
3. Important Points in Drug Design based on
Bioinformatics Tools
• Chemical Modification of Known Drugs
– Drug improvement by chemical modification
– Pencillin G -> Methicillin; morphine->nalorphine
• Receptor Based drug design
– Receptor is the target (usually a protein)
– Drug molecule binds to cause biological effects
– It is also called lock and key system
– Structure determination of receptor is important
• Ligand-based drug design
– Search a lead ocompound or active ligand
– Structure of ligand guide the drug design process
10. Important Points in Drug Design based on
Bioinformatics Tools
• Application of Genome
– 3 billion bases pair
– 30,000 unique genes
– Any gene may be a potential drug target
– ~500 unique target
– Their may be 10 to 100 variants at each target gene
– 1.4 million SNP
– 10200 potential small molecules
11. Important Points in Drug Design based on
Bioinformatics Tools
• Refinement of compounds
– Refine lead compounds using laboratory techniques
– Greater drug activity and fewer side effects
– Compute change required to design better drug
• Quantitative Structure Activity Relationships (QSAR)
– Compute functional group in compound
– QSAR compute every possible number
– Enormous curve fitting to identify drug activity
– chemical modifications for synthesis and testing.
• Solubility of Molecule
• Drug Testing
12. Drug Discovery & Development
Identify disease
Isolate protein
involved in
disease (2-5 years)
Find a drug effective
against disease protein
(2-5 years)
Preclinical testing
(1-3 years)
Formulation
Human clinical trials
(2-10 years)
Scale-up
FDA approval
(2-3 years)
13. Techology is impacting this process
Identify disease
Isolate protein
Find drug
Preclinical testing
GENOMICS, PROTEOMICS & BIOPHARM.
HIGH THROUGHPUT SCREENING
MOLECULAR MODELING
VIRTUAL SCREENING
COMBINATORIAL CHEMISTRY
IN VITRO & IN SILICO ADME MODELS
Potentially producing many more targets
and “personalized” targets
Screening up to 100,000 compounds a
day for activity against a target protein
Using a computer to
predict activity
Rapidly producing vast numbers
of compounds
Computer graphics & models help improve activity
Tissue and computer models begin to replace animal testing
14. 1. Gene Chips
• “Gene chips” allow us
to look for changes in
protein expression for
different people with a
variety of conditions,
and to see if the
presence of drugs
changes that expression
• Makes possible the
design of drugs to
target different
phenotypes
compounds administered
people / conditions
e.g. obese, cancer,
caucasian
expression profile
(screen for 35,000 genes)
15. Biopharmaceuticals
• Drugs based on proteins, peptides or natural
products instead of small molecules (chemistry)
• Pioneered by biotechnology companies
• Biopharmaceuticals can be quicker to discover
than traditional small-molecule therapies
Biotechs now paring up with major pharmaceutical
companies
16. High-Throughput Screening
• Drug companies now have millions of samples of
chemical compounds
• High-throughput screening can test 100,000
compounds a day for activity against a protein target
• Maybe tens of thousands of these compounds will
show some activity for the protei
• The chemist needs to intelligently select the 2 - 3
classes of compounds that show the most promise for
being drugs to follow-up
17. Informatics Implications
• Need to be able to store chemical structure and biological data for
millions of datapoints
– Computational representation of 2D structure
• Need to be able to organize thousands of active compounds into
meaningful groups
– Group similar structures together and relate to activity
• Need to learn as much information as possible from the data (data
mining)
– Apply statistical methods to the structures and related information
18. 3. Computational Models of Activity
• Machine Learning Methods
– E.g. Neural nets, Bayesian nets, SVMs, Kahonen nets
– Train with compounds of known activity
– Predict activity of “unknown” compounds
• Scoring methods
– Profile compounds based on properties related to target
• Fast Docking
– Rapidly “dock” 3D representations of molecules into 3D
representations of proteins, and score according to how well
they bind
19. 4. Combinatorial Chemistry
• By combining molecular “building blocks”, we
can create very large numbers of different
molecules very quickly.
• Usually involves a “scaffold” molecule, and sets
of compounds which can be reacted with the
scaffold to place different structures on
“attachment points”.
20. Combinatorial Chemistry Issues
• Which R-groups to choose
• Which libraries to make
– “Fill out” existing compound collection?
– Targeted to a particular protein?
– As many compounds as possible?
• Computational profiling of libraries can help
– “Virtual libraries” can be assessed on computer
21. 5. Molecular Modeling
• 3D Visualization of interactions between compounds and proteins
• “Docking” compounds into proteins computationally
22. 3D Visualization
• X-ray crystallography and NMR Spectroscopy can
reveal 3D structure of protein and bound
compounds
• Visualization of these “complexes” of proteins and
potential drugs can help scientists understand the
mechanism of action of the drug and to improve
the design of a drug
• Visualization uses computational “ball and stick”
model of atoms and bonds, as well as surfaces
• Stereoscopic visualization available
24. 6. In Vitro & In Silico ADME
models
• Traditionally, animals were used for pre-human testing.
However, animal tests are expensive, time consuming and
ethically undesirable
• ADME (Absorbtion, Distribution, Metabolism, Excretion)
techniques help model how the drug will likely act in the
body
• These methods can be experemental (in vitro) using
cellular tissue, or in silico, using computational models
25. In Silico ADME Models
• Computational methods can predict compound
properties important to ADME, e.g.
– LogP, a liphophilicity measure
– Solubility
– Permeability
– Cytochrome p450 metabolism
• Means estimates can be made for millions of
compouds, helping reduce “atrittion” – the failure
rate of compounds in late stage
