Over the past decades, Creative Biolabs has become a leader in antibody drug discovery and manufacturing, providing high quality services to clients in academia and industry around the world. Now, we are able to provide solutions to accelerate drug discovery and development by deploying artificial intelligence technologies. Here, we will briefly introduce the basics of AI-augmented drug discovery, algorithm classification, common AI models, and related services.
ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY "AN OVERVIEW OF AWARENESS"FinianCN
ARTIFICIAL INTELLIGENT IN DRUG DISCOVERY:- AN OVERVIEW OF AWARENESS.
AI is showing the potential to be a faster and more efficient way to find and develop new drugs. A growing number of organizations and universities are focusing to minimize the complexities involved in the classical way of drug discovery by using AI computing to envisage which drug candidate are most likely to be effective treatments.
It is hard to measure the adoption of AI in drug discovery. Pharma and biotech companies tend to not publicly disclose competitive technology use.
While organizations are adopting the technology, there is significant untapped potential for those willing to be more aggressive. Which is depending on the realization of the potential with education and relevant success stories
Everything you want to know about role of artificial intelligence in drug discovery.
Artificial intelligence in health care and pharmacy, drug discovery, tensorflow, python,
deep neural network, GANs
AI in drug discovery and development
AI in clinical trials
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Even if a drug is approved in market, it could be withdrawn due to safety problems.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.
ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY "AN OVERVIEW OF AWARENESS"FinianCN
ARTIFICIAL INTELLIGENT IN DRUG DISCOVERY:- AN OVERVIEW OF AWARENESS.
AI is showing the potential to be a faster and more efficient way to find and develop new drugs. A growing number of organizations and universities are focusing to minimize the complexities involved in the classical way of drug discovery by using AI computing to envisage which drug candidate are most likely to be effective treatments.
It is hard to measure the adoption of AI in drug discovery. Pharma and biotech companies tend to not publicly disclose competitive technology use.
While organizations are adopting the technology, there is significant untapped potential for those willing to be more aggressive. Which is depending on the realization of the potential with education and relevant success stories
Everything you want to know about role of artificial intelligence in drug discovery.
Artificial intelligence in health care and pharmacy, drug discovery, tensorflow, python,
deep neural network, GANs
AI in drug discovery and development
AI in clinical trials
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.
Drug design is the inventive process of finding new medications based on the knowledge of the biological target.
In the most basic sense, drug design involves design of small molecules that are complementary in shape and charge to the bio-molecular target to which they interact and therefore will bind to it.
Drug design frequently but not necessarily relies on computer modeling techniques. This type of modeling is often referred to as computer-aided drug design.
Types;-
Random screening
Trial and error method
Ethnopharmacology approach
Serendipity method
Classical pharmacology
Chemical structure based drug discovery
The Role of Bioinformatics in The Drug Discovery ProcessAdebowale Qazeem
The Role of Bioinformatics in The Drug Discovery Process, is an undergraduate seminar presentation in the department of Biochemistry, Faculty of life Sciences, University of Ilorin, Ilorin.
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
Identifying drug targets and candidate sequences is an important process and an unmet challenge in drug development. Creative Biolabs has developed an original AI-augmented drug discovery platform to accelerate drug discovery.
https://ai.creative-biolabs.com/ai-augmented-drug-discovery.htm
Creative Biolabs offers a series of AI-based antibody screening services based on the prediction of antibody-antigen binding and a unique way to find rare antibody clusters and get more candidate antibody sequences by augmenting our data-driven AI screening services.
https://ai.creative-biolabs.com/ai-based-antibody-screening-services.htm
Drug design is the inventive process of finding new medications based on the knowledge of the biological target.
In the most basic sense, drug design involves design of small molecules that are complementary in shape and charge to the bio-molecular target to which they interact and therefore will bind to it.
Drug design frequently but not necessarily relies on computer modeling techniques. This type of modeling is often referred to as computer-aided drug design.
Types;-
Random screening
Trial and error method
Ethnopharmacology approach
Serendipity method
Classical pharmacology
Chemical structure based drug discovery
The Role of Bioinformatics in The Drug Discovery ProcessAdebowale Qazeem
The Role of Bioinformatics in The Drug Discovery Process, is an undergraduate seminar presentation in the department of Biochemistry, Faculty of life Sciences, University of Ilorin, Ilorin.
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
Identifying drug targets and candidate sequences is an important process and an unmet challenge in drug development. Creative Biolabs has developed an original AI-augmented drug discovery platform to accelerate drug discovery.
https://ai.creative-biolabs.com/ai-augmented-drug-discovery.htm
Creative Biolabs offers a series of AI-based antibody screening services based on the prediction of antibody-antigen binding and a unique way to find rare antibody clusters and get more candidate antibody sequences by augmenting our data-driven AI screening services.
https://ai.creative-biolabs.com/ai-based-antibody-screening-services.htm
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
Effect of Data Size on Feature Set Using Classification in Health Domaindbpublications
In health domain, the major critical issue is prediction of disease in early stage. Prediction of disease is mainly based on the experience of physician so many machine learning approach contribute their work in the prediction of disease. In existing approaches, either prediction or feature selection has been concentrated. The aim of this paper is to present the effect of data size and set of features in the prediction of disease in health domain using Naïve Bayes. This shows how each attribute or combination of attribute behaves on different size of dataset.
Myself Omkar B. Tipugade ,M-Pharm Sem II, Department of Pharmaceutics , Today I upload the presentation on Artificial Intelligene , In that I discuss about the definition of AI as well as their important in Pharmaceutical field . Also give brief information about the Neural networking & fuzzy logic with diagrammatic presentation And also application of AI in product formulation. I highlight the important words.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
ARTIFICIAL NEURAL NETWORKING.
FIRST STEP TO KNOWLEDGE IS TO KNOW THAT we are ignorant
Knowledge in medical field is characterized by uncertanity and vagueness
Historically as well as currently this fact remains a motivation for the development of medical decision support system are based on fuzzy logics
Greek philosopher visualized a basic model of brain function as early as 300 bc
Till date nervous system is not completely understood to human kind.
Predicting disease at an early stage becomes critical, and the most difficult challenge is to predict it correctly along with the sickness. The prediction happens based on the symptoms of an individual. The model presented can work like a digital doctor for disease prediction, which helps to timely diagnose the disease and can be efficient for the person to take immediate measures. The model is much more accurate in the prediction of potential ailments. The work was tested with four machine learning algorithms and got the best accuracy with Random Forest.
Branch: An interactive, web-based tool for building decision tree classifiersBenjamin Good
A crucial task in modern biology is the prediction of complex phenotypes, such as breast cancer prognosis, from genome-wide measurements. Machine learning algorithms can sometimes infer predictive patterns, but there is rarely enough data to train and test them effectively and the patterns that they identify are often expressed in forms (e.g. support vector machines, neural networks, random forests composed of 10s of thousands of trees) that are highly difficult to understand. In addition, it is generally unclear how to include prior knowledge in the course of their construction.
Decision trees provide an intuitive visual form that can capture complex interactions between multiple variables. Effective methods exist for inferring decision trees automatically but it has been shown that these techniques can be improved upon via the manual interventions of experts. Here, we introduce Branch, a new Web-based tool for the interactive construction of decision trees from genomic datasets. Branch offers the ability to: (1) upload and share datasets intended for classification tasks (in progress), (2) construct decision trees by manually selecting features such as genes for a gene expression dataset, (3) collaboratively edit decision trees, (4) create feature functions that aggregate content from multiple independent features into single decision nodes (e.g. pathways) and (5) evaluate decision tree classifiers in terms of precision and recall. The tool is optimized for genomic use cases through the inclusion of gene and pathway-based search functions.
Branch enables expert biologists to easily engage directly with high-throughput datasets without the need for a team of bioinformaticians. The tree building process allows researchers to rapidly test hypotheses about interactions between biological variables and phenotypes in ways that would otherwise require extensive computational sophistication. In so doing, this tool can both inform biological research and help to produce more accurate, more meaningful classifiers.
A prototype of Branch is available at http://biobranch.org/
Design of Organ-On-A-Chip - Creative Biolabs.pptxCreative-Biolabs
Creative Biolabs has developed an extensive microfluidic technology platform, offering customers one-stop services covering all aspects of microfluidic research and evaluation. This includes the design and manufacturing of organ-on-a-chip (OOC) systems, as well as personalized, customized solutions tailored to individual needs.
This slide briefly introduces our OOC design concepts. If you require further details, products, and services related to OOC, please follow us to stay updated and informed.
Introduction of Organ-On-A-Chip - Creative BiolabsCreative-Biolabs
Creative Biolabs has established a complete microfluidic technology platform. We provide customers with one-stop services in all aspects of microfluidic research and evaluation, including Organ-on-a-Chip design and manufacturing and personalized, customized solutions.
This slide provides a brief introduction to OOC. If you need more information, products, and services about OOC, please follow us to stay informed.
Advances in Oncolytic Virotherapy - Creative BiolabsCreative-Biolabs
Oncolytic virotherapy, an innovative approach leverages the natural ability of viruses to infect and kill tumor cells, offering a beacon of hope for patients battling cancer. Our journey through this domain uncovers the mechanisms of action of oncolytic viruses, explores representative examples, discusses strategic modifications and combinations with other therapies, addresses challenges, and reviews the clinical status of this cutting-edge treatment.
This slide provides a comprehensive overview of lipid nanoparticle-based mRNA Vaccine development, detailing the technological timeline, the 2023 Nobel Prize-winning science behind the vaccines, and the specifics of COVID-19 vaccine candidates BNT162b2 and mRNA-1273. It also explores the advantages of liposomes in mRNA delivery, the intricate mechanisms of LNP-based vaccines, their therapeutic potential beyond COVID-19, and the rigorous development process. Creative Biolabs supports these innovations with specialized services and products, pushing the boundaries of medical science.
After decades of development and innovation, Creative Biolabs is committed to demonstrating our strong expertise and capabilities in the field of ribosome research, as well as expanding our research and service capabilities to multiple fields. This five-part slide we will briefly introduce ribosome analysis technology, polysome profiling, ribosome profiling, ribosome affinity purification, and highlight the ribosome analysis solutions provided by Creative Biolabs.
Advances in CAR-T Cell Therapy - Creative BiolabsCreative-Biolabs
Various barriers restrict the efficacy and/or prevent the widespread use of CAR-T cell therapies in these patients as well as in those with other diseases, particularly solid tumors. The evolution of CAR designs beyond conventional structures will be necessary to address these limitations and expand the use of CAR-T cells to a wider range of diseases. In this presentation, we discuss the progress in the development of innovative designs for new CAR-T cell products to increase and expand the clinical benefits of these treatments for patients with various cancers, and we explore the potential of CAR-T cell therapies in addressing diseases beyond cancer.
Vaccine for Cancer Immunotherapy - Creative BiolabsCreative-Biolabs
Cancer vaccines are designed to stimulate anti-tumor immunity through active immunization with tumor antigens and has long been envisioned as a key tool of effective cancer immunotherapy. Today we will go through the history, formulations, mechanisms of action, and clinical status of cancer vaccines. Creative Biolabs has been effectively supporting the cancer vaccine industry with our one-stop solutions for cancer vaccine discovery and development. We will accommodate the specific properties and clinical purpose of your vaccine candidate, and take careful scientific considerations to ensure the most appropriate solutions to develop your projects.
PROTAC Technology in Tumor Targeted Therapy - Creative BiolabsCreative-Biolabs
Today, we will explore the protac technology applied in tumor-targeted therapy. The following will be presented to you, such as a brief introduction to Protac, the mechanism of action, the advantages and disadvantages of protac as a drug, and the core content: the application of protac technology in tumor therapy. And the last part, the protac solutions provided by Creative Biolabs. If you have any questions about the PROCT development, please email us.
Email: info@creative-biolabs.com
Macrophages as Targets in Cancer Immunotherapy - Creative BiolabsCreative-Biolabs
Due to the limitations and shortages of traditional cancer treatments, immunotherapy has become the most promising cancer treatment. Various cancer immunotherapy strategies have emerged. These include adoptive cellular immunotherapy, tumor vaccines, antibodies, immune checkpoint inhibitors, and small molecule inhibitors. Although most of these strategies are not meant to target macrophages directly or originally, macrophages contribute significantly to the final outcomes.
As a CRO company, Creative Biolabs offers first-in-class macrophage therapeutic development services. Please don’t hesitate to contact us if you are interested in our services or if you have any questions.
Monkeypox Drug and Vaccine Discovery - Creative BiolabsCreative-Biolabs
Recently, we are experiencing rapid globalization of the monkeypox virus in a short time. Monkeypox has infected more than 77,000 people in more than 100 countries worldwide. Mutations have enabled the virus to grow stronger and smarter, evading antiviral drugs and vaccines. To better identify and control the current monkeypox outbreak, efforts to develop drugs and vaccines are critical. This slide presents some important information about monkeypox drug and vaccine discovery.
Recently, we are experiencing rapid globalization of the monkeypox virus in a short time. Monkeypox has infected more than 77,000 people in more than 100 countries worldwide. Mutations have enabled the virus to grow stronger and smarter, evading antiviral drugs and vaccines. To better identify and control the current monkeypox outbreak, efforts to develop drugs and vaccines are critical. This slide presents some important information about monkeypox drug and vaccine discovery.
Creative Biolabs has extensive experience in coronavirus research and can provide a comprehensive range of high-quality services and products related to SARS-CoV-2 and its variants.
This slide provides a brief introduction to the SARS-CoV-2 variant, Omicron. If you need more information, products, and services about Omicron, please follow us to stay up-to-date.
Hello, everyone. This is Creative Biolabs. Today we will learn about myeloid leukemia vaccines. Our contents here include the Introduction to Myeloid Leukemia, Current Treatment of Myeloid Leukemia, Obstacles to Cancer Vaccine, Targets of Myeloid Leukemia Vaccine, Myeloid Leukemia vaccine types, Examples of Myeloid Leukemia Vaccines, and related services you can find at Creative Biolabs.
Immune Cell Migration in Cancer and Immunotherapy - Creative BiolabsCreative-Biolabs
There is a growing appreciation that “immune contexture” has a significant impact on the clinical outcome for cancer patients. Thus, immunotherapies targeting immune cell migration can be developed to modulate the immune contexture in tumor microenvironment. In this slide you will learn about immune cell migration and its application in cancer immunotherapy development.
As a CRO company, Creative Biolabs offers immunotherapy preclinical development services, and a full range of T cell services. Please don’t hesitate to contact us if you have any questions.
Liposomal Delivery Systems in Cancer Therapy - Creative BiolabsCreative-Biolabs
The lipid-based drug delivery system is a newly developed drug carrier that can be applied for various cancer-targeted treatments with many superiorities. This video briefly introduces various types of liposomes, the principles of liposomal drug delivery systems for cancer therapy, and liposome development services and products provided by Creative Biolabs.
A brief introduction to non-IgG antibody - Creative BiolabsCreative-Biolabs
Creative Biolabs has been a leading bio-company in immunotherapy and pharmaceuticals, especially in non-IgG antibody development, through more than a decade of exploration and expansion. Here, we will give a brief introduction to non-IgG antibodies, understand the characteristics and functions of various antibodies, and show some related products and services of Creative Biolabs.
This slide is about the basics of mRNA-based therapy. The content includes: definition of mRNA, timeline of mRNA therapeutics, action mechanism and development strategies of mRNA drugs, therapeutic mRNA applications, and the related services provided by Creative Biolabs.
Conventional monoclonal antibodies cannot reach adequate therapeutic effects in some cases. Enhancing the effector functions is one of the major strategies for the development of engineered monoclonal antibodies to make up for the deficiencies. Here, we will discuss the development of effector function-enhanced antibodies, and the solutions provided by Creative Biolabs.
Stem Cells in A New Era of Cell based Therapies - Creative BiolabsCreative-Biolabs
A stem cell can replicate itself or differentiate into cells that carry out the specific functions of the body. The application of stem cells in regenerative medicine and disease therapeutics is one of the most exciting advances in medical science today. In cell-based therapies, stem cells may play two roles. The first role is as drug-delivery vehicles. The second role is as therapeutic agents themselves. Stem cells also offer opportunities for scientific advances that go far beyond cell-based therapies. Creative Biolabs is dedicated to facilitate the research of stem cells in both basic science and therapeutics development. Please contact us if you are interested in our services or products.
Anti Virus Biomolecular Discovery - Creative BiolabsCreative-Biolabs
Creative Biolabs is the leading custom service provider in antibody development and engineering. Our scientists bring state-of-the-art technology to support functional antibody and peptide discovery services against viruses. Here, we will introduce the virus and its pathogenic mechanism in detail. At the same time, you will learn how to obtain functional anti-virus antibodies and peptides through our technology platform.
Introduction of Microfluidics - Creative BiolabsCreative-Biolabs
Microfluidics is a technology that precisely controls and manipulates micro-scale fluids, especially sub-micron structures. It is also called Lab-on-a-Chip or microfluidic chip technology.
Creative Biolabs has established a comprehensive microfluidics technology platform. We offer our customers with one-stop-shop of all aspects of microfluidics research and evaluation, including microfluidic chip design and manufacture, microfluidic chip products, as well as personalized customized solutions.
This video briefly introduces information about microfluidics. If you need more information about microfluidics, please follow us.
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
ISI 2024: Application Form (Extended), Exam Date (Out), EligibilitySciAstra
The Indian Statistical Institute (ISI) has extended its application deadline for 2024 admissions to April 2. Known for its excellence in statistics and related fields, ISI offers a range of programs from Bachelor's to Junior Research Fellowships. The admission test is scheduled for May 12, 2024. Eligibility varies by program, generally requiring a background in Mathematics and English for undergraduate courses and specific degrees for postgraduate and research positions. Application fees are ₹1500 for male general category applicants and ₹1000 for females. Applications are open to Indian and OCI candidates.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
1. AI-Augmented
Drug Discovery
Creative Biolabs provides innovative drug discovery services
based on our original Artificial Intelligence-augmented
technology, especially for the discovery of therapeutic
antibodies and small molecules.
Email: info@creative-biolabs.com
Address: SUITE 203, 17 Ramsey Road, Shirley, NY 11967, USA
Web: www.creative-biolabs.com
2. CONTENTS
0 1
02
03
04
AI IN DRUG DISCOVERY
HOW AI WORKS
AI MODELS USED IN DRUG DISCOVERY
AI IN CREATIVE BIOLABS
4. Introducing a new drug to market
can cost pharmaceutical
companies an average $2.6 billion
and 11-15 years of research and
development.
Even once new drug candidates
show potential in laboratory
testing, less than 10% of drug
candidates make it to market
following Phase I trials.
Between 2010 and 2017, 76% of
new drugs approved by the US
Food and Drug Administration
(FDA) are small molecules.
$2.6 B 10% 76%
WHY USE AI IN DRUG
DISCOVERY?
5. After making it through the preclinical development
phase, and receiving approval from the FDA,
researchers begin testing the drug with human
participants. AI can facilitate participant monitoring
during clinical trials—generating a larger set of data
more quickly—and aid in participant retention by
personalizing the trial experience.
AI in Clinical Trials
(Phase III)
The drug discovery process ranges from reading and analyzing
already existing literature, to testing the ways potential drugs
interact with targets. According to report, AI could curb drug
discovery costs for companies by as much as 70%.
AI in Drug Discovery
(Phase I)
The preclinical development phase of drug discovery involves
testing potential drug targets on animal models. Utilizing AI
during this phase could help trials run smoothly and enable
researchers to more quickly and successfully predict how a
drug might interact with the animal model.
AI in Preclinical Development
(Phase II)
AI in Drug
Development
Process
6. Predicting 3D structure of
target protein
Predicting drug-protein
interactions
AI in determining drug
activity
AI in de novo drug design
AI in
drug design
AI In Drug Discovery
AI in
polypharmacology
Designing biospecific
drug molecules
Designing multitarget
drug molecules
AI in
chemical synthesis
Predicting reaction yield
Predicting retrosynthesis
pathways
Developing insights into
reaction mechanisms
Designing synthetic route
AI in
drug repurposing
Identification of
therapeutic target
Prediction of new
therapeutic use
AI in
drug screening
Prediction of toxicity
Prediction of bioactivity
Prediction of
physicochemical property
Identification and
classification of target cells
8. Classes of Learning Tasks and Techniques
Mix of supervised and unsupervised learning, where less expensive and more abundant unlabeled
data can be utilized to train a classifier.
Semisupervised Learning (Fig. A)
A learning algorithm can interactively query the user to determine labels for unlabeled data in the
regions of the input space about which the model is least certain.
Active Learning (Fig. B)
Describes a family of algorithms that relax the common assumption that the training and test data
should be in the same feature space and follow the same distribution.
Transfer Learning (Fig. D)
Can be treated as a geometric or topological problem, the goal is to find similarities and differences
between data points used to spatially order data.
Unsupervised Learning
The goal is to reconstruct the unknown function f that assigns output values y to data points x.
Supervised Learning
Instead of learning only one task at a time, as in single-task learning, several different but
conceptually related tasks are learned in parallel and make use of a shared internal representation.
Multitask Learning (Fig. E)
To some extent strives to emulate reward-driven learning, and in its simplest configuration, an agent
attempts to find the optimal set of actions to promote some outcome.
Reinforcement Learning (Fig. C)
Xin Y,et al. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem. Rev. 2019, 119 (18): 10520-10594.
9. Bayesian methods are those that explicitly
apply Bayes’ theorem to classification and
regression problems.
Bayesian Algorithms
It is called instance-based because it builds
the hypotheses from the training instances.
It is also known as memory-based learning
or lazy-learning.
Instance-Based Methods
Algorithms for constructing decision trees
usually work top-down, by choosing a
variable at each step that best splits the
set of items.
Decision Tree Algorithms
In statistics and machine learning,
ensemble methods use multiple
learning algorithms to obtain better
predictive performance than could be
obtained from any of the constituent
learning algorithms alone.
Ensemble Algorithms
Dimensionality reduction seeks a lower-
dimensional representation of numerical
input data that preserves the salient
relationships in the data.
Dimensionality Reduction
Artificial neural networks (ANNs) consist of
input, hidden, and output layers with
connected neurons (nodes) to simulate the
human brain.
Artificial Neural Networks
Common Learning Algorithms
10. Bayesian Algorithms
Liu ZH,et al. ChemStable: A web server for rule-embedded naïve Bayesian learning approach to predict
compound stability. J. Comput. Aided Mol. Des. 2014, 28: 941-950.
11. Instance-Based Methods
SVM is a supervised machine learning algorithm used for both classification
and regression. The objective of SVM algorithm is to find a hyperplane in an
N-dimensional space that distinctly classifies the data points.
Support Vector Machine
A SOM or self-organizing feature map is an unsupervised machine learning
technique used to produce a low-dimensional representation of a higher
dimensional data set while preserving the topological structure of the data.
Self-organizing Map
KNN is a simple, supervised machine learning algorithm that can be used to
solve both classification and regression problems.
K-nearest Neighbor
Xin Y,et al. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem. Rev. 2019,
119 (18): 10520-10594.
12. Decision Tree Algorithms
Random forests or random decision forests is an ensemble learning method for
classification, regression and other tasks that operates by constructing a multitude of
decision trees at training time.
Random Forest
A decision tree is a decision support tool that uses a tree-like model of decisions and their
possible consequences, including chance event outcomes, resource costs, and utility.
Decision Tree
Xin Y,et al. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem. Rev. 2019, 119 (18): 10520-10594.
13. Ensemble Algorithms
Boosting is an ensemble learning method that combines a set of weak
learners into a strong learner to minimize training errors. In boosting, a
random sample of data is selected, fitted with a model and then
trained sequentially—that is, each model tries to compensate for the
weaknesses of its predecessor.
Boosting
Bagging, is the ensemble learning method that is commonly used
to reduce variance within a noisy dataset. In bagging, a random
sample of data in a training set is selected with replacement—
meaning that the individual data points can be chosen more than
once.
Bagging
Xin Y,et al. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem. Rev. 2019, 119 (18): 10520-10594.
14. Dimensionality Reduction
LDA is a generalization of Fisher's linear discriminant, a method used in
statistics, pattern recognition and machine learning to find a linear
combination of features that characterizes or separates two or more classes
of objects or events.
Linear Discriminant Analysis
Image From Wikipedia
A visual depiction of the resulting PCA projection for a set of 2D points. A visual depiction of the resulting LDA projection for a set of 2D points.
PCA is a popular technique for analyzing large datasets containing a high
number of dimensions/features per observation, increasing the
interpretability of data while preserving the maximum amount of
information, and enabling the visualization of multidimensional data.
Principal Component Analysis
15. Artificial Neural Networks
DNN refers to an ANN that has several hidden layers with several
differences. Deep nets process data in complex ways by employing
sophisticated math modeling.
Deep Neural Networks
ANNs are computing systems inspired by the biological neural networks
that constitute animal brains. A typical ANN architecture contains many
artificial neurons arranged in a series of layers: the input layer, an output
layer, i.e., the top layer, which generates a desired prediction ( ADMET
properties, activity, a vector of fingerprint etc.), and one or more hidden
layer where the intermediate representations of the input data are
transformed.
Artificial neural networks
Image From Wikipedia
17. DeepVS: Boosting Docking-Based Virtual
Screening with DL
Pereira J.C. Boosting docking-based virtual screening with deep learning. J. Chem. Inf. Model. 2016;56:2495–2506.
Mostafa K. DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks. Bioinformatics. 2019, 35(18):3329–3338.
The deep neural network that is introduced, DeepVS, uses the output of a
docking program and learns how to extract relevant features from basic
data. The approach introduces the use of atom and amino acid
embeddings and implements an effective way of creating distributed
vector representations of protein–ligand complexes by modeling the
compound as a set of atom contexts that is further processed by a
convolutional layer.
DeepVS
18. DeepAffinity: DL Method
Used to Measure DTBA
Mostafa K. DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks. Bioinformatics. 2019, 35(18):3329–3338.
DeepAffinity is a deep learning methods used to measure drug
target binding affinity. Under novel representations of
structurally-annotated protein sequences, a semi-supervised
deep learning model that unifies recurrent and convolutional
neural networks has been proposed to exploit both unlabeled
and labeled data, for jointly encoding molecular
representations and predicting affinities. Performances for new
protein classes with few labeled data are further improved by
transfer learning.
DeepAffinity
19. DeepTox: Toxicity Prediction Using Deep Learning
Mayr A. DeepTox: toxicity prediction using deep learning. Front. Environ. Sci. 2016, 3:80.
Representation of a toxicophore by hierarchically related features.
22. • High throughput, screen large numbers of clones
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