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Even if a drug is approved in market, it could be withdrawn due to safety problems.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.
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
Tutorial delivered at ECML-PKDD 2021.
TL;DR: This tutorial reviews recent developments on drug discovery using machine learning methods.
Powered by neural networks, modern machine learning has enjoyed great successes in data-intensive domains such as computer vision and languages where human can naturally perform well. Machine learning equipped with reasoning is now accelerating fields that traditionally require deep expertise such as physics, chemistry and biomedicine. This tutorial provides an overview of how machine learning and reasoning are speeding up and lowering the cost of drug discovery. This includes how machine learning can help in wide range of areas such as novel molecule identification, protein representation, drug-target binding, drug re-purposing, generative drug design, chemical reaction, retrosynthesis planning, drug-drug interaction, and safety assessment. We will also discuss relevant machine learning models for graph classification, molecular graph transformation, drug generation using deep generative models and reinforcement learning, and chemical reasoning.
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.
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
Tutorial delivered at ECML-PKDD 2021.
TL;DR: This tutorial reviews recent developments on drug discovery using machine learning methods.
Powered by neural networks, modern machine learning has enjoyed great successes in data-intensive domains such as computer vision and languages where human can naturally perform well. Machine learning equipped with reasoning is now accelerating fields that traditionally require deep expertise such as physics, chemistry and biomedicine. This tutorial provides an overview of how machine learning and reasoning are speeding up and lowering the cost of drug discovery. This includes how machine learning can help in wide range of areas such as novel molecule identification, protein representation, drug-target binding, drug re-purposing, generative drug design, chemical reaction, retrosynthesis planning, drug-drug interaction, and safety assessment. We will also discuss relevant machine learning models for graph classification, molecular graph transformation, drug generation using deep generative models and reinforcement learning, and chemical reasoning.
How Artificial Intelligence in Transforming PharmaTyrone Systems
Artificial intelligence in Pharma refers to the use of automated algorithms to perform tasks which traditionally rely on human intelligence. Over the last five years, the use of artificial intelligence in the pharma and biotech industry has redefined how scientists develop new drugs, tackle disease, and more.
Given the growing importance of Artificial Intelligence for the pharma industry, we wanted to create a comprehensive report which helps every business leader understand the biggest breakthroughs in the biotech space which are assisted by the deployment of artificial intelligence technologies.
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
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
Drug discovery and development is and always has been the most exciting part of clinical pharmacology. It is my attempt to compile the basic concepts from various books, articles and online journals. Feel free to comment.
molecular docking its types and de novo drug design and application and softw...GAUTAM KHUNE
This ppt deals with all the aspects related to molecular docking ,its types(rigid ,flexible and manual) and screening based on it and also deals with de novo drug design , various softwares available for docking methodologies and applications for molecular docking in new drug design
Computer-aided design (CAD) is the use of computers (or workstations) to aid in the creation, modification, analysis, or optimization of a design: 3 This software is used to increase the productivity of the designer, improve the quality of design, improve communications through documentation, and to create a database for manufacturing: 4 Designs made through CAD software are helpful in protecting products and inventions when used in patent applications. CAD output is often in the form of electronic files for print, machining, or other manufacturing operations. The terms computer-aided drafting (CAD) and computer-aided design and drafting (CADD) are also used
Role of Target Identification and Target Validation in Drug Discovery ProcessPallavi Duggal
Target identification and Validation tells about the how target is neccesary for new drug discovery and its development to reach into market for rare diseases.
How Artificial Intelligence in Transforming PharmaTyrone Systems
Artificial intelligence in Pharma refers to the use of automated algorithms to perform tasks which traditionally rely on human intelligence. Over the last five years, the use of artificial intelligence in the pharma and biotech industry has redefined how scientists develop new drugs, tackle disease, and more.
Given the growing importance of Artificial Intelligence for the pharma industry, we wanted to create a comprehensive report which helps every business leader understand the biggest breakthroughs in the biotech space which are assisted by the deployment of artificial intelligence technologies.
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
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
Drug discovery and development is and always has been the most exciting part of clinical pharmacology. It is my attempt to compile the basic concepts from various books, articles and online journals. Feel free to comment.
molecular docking its types and de novo drug design and application and softw...GAUTAM KHUNE
This ppt deals with all the aspects related to molecular docking ,its types(rigid ,flexible and manual) and screening based on it and also deals with de novo drug design , various softwares available for docking methodologies and applications for molecular docking in new drug design
Computer-aided design (CAD) is the use of computers (or workstations) to aid in the creation, modification, analysis, or optimization of a design: 3 This software is used to increase the productivity of the designer, improve the quality of design, improve communications through documentation, and to create a database for manufacturing: 4 Designs made through CAD software are helpful in protecting products and inventions when used in patent applications. CAD output is often in the form of electronic files for print, machining, or other manufacturing operations. The terms computer-aided drafting (CAD) and computer-aided design and drafting (CADD) are also used
Role of Target Identification and Target Validation in Drug Discovery ProcessPallavi Duggal
Target identification and Validation tells about the how target is neccesary for new drug discovery and its development to reach into market for rare diseases.
AI-powered Drug Discovery: Revolutionizing Precision MedicineClinosolIndia
The convergence of artificial intelligence (AI) and drug discovery has ushered in a new era in healthcare, promising groundbreaking advancements in precision medicine. AI, with its ability to analyze vast datasets, identify patterns, and predict outcomes, is revolutionizing the drug discovery process. This transformative approach not only accelerates the development of novel therapeutics but also enhances the customization of treatments, leading to more targeted and effective medical interventions.
IVF is stressful and expensive and there is a continued need to improve outcome using all information technology available to improve outcomes , meet expectations and review management.
Discovery on Target 2014 - The Industry's Preeminent Event on Novel Drug TargetsJaime Hodges
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This document presents an overview of the AI applications in life sciences. The presentation highlights various steps in drug development and AI applications. Also, discusses Alzheimer’s disease and obstacles to develop drugs. Finally, presents details of AI in target identification for AD.
This disclaimer informs readers know that the views, thoughts, and opinions expressed in the presentation belong solely to the author, and not to the author’s employer, organization, committee or other group or individual.
Clinicians and healthcare professionals need to familiarize themselves with AI, including its applications and appropriate implementation. Here I am explaining about AI in the context of the disease life cycle.
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Role of AI in Drug Discovery and Development
1. Artificial Intelligence in
Drug Discovery &
Development
Dr. Manu Kumar Shetty
Associate Professor
Department of Pharmacology
Maulana Azad Medical College
New Delhi
2. AI is changing drug discovery
Time 40min
Why we need AI
What are AI Models
Advantages
Present status &
Challenges
3. AI/ML in Drug development,
Need?
“ Evolution is not a force but a process “
High attrition rate
Increased expenditure
Increase volume of data
Increase global regulatory requirement
Challenge in timely processing
4. Next Generation Drug
Development
“We’re really at the cusp of delivering huge improvements in drug discovery
and development”
Shift from traditional model to Next-Gen
automated and intelligent model
Next Generation
Drug Development
AI/ML/DL
NLP CNN
Big Data
Analytics
5.
6. Role of AI in Drug-Discovery
Better compounds going into clinical
trials
Better target understanding
Better conductance of trials
Better Post Marketing surveillance
7. Drug Develop
1. Target protein properties
2. Ligand/drug properties
3. Target-ligand interaction
4. Drug repurposing
5. Clinical trails
6. Pharmacovigilance
18. 4). Drug Repurposing
Drug repositioning, drug retasking, drug
reprofiling, drug rescuing, drug
recycling, drug redirection, and
therapeutic switching
Process of identifying new therapeutic
use(s) for old/existing/available drugs
Highly efficient, time saving, low-cost
and minimum risk of failure, increases
the success rate
19.
20. 5). Clinical Trial
Half of time and investment
86% of all trials do not meet enrolment
timelines
1/3rd of all Phase III trials fail owing to
enrolment problems
Patient recruitment takes up 1/3rd of
duration
A 32% failure rate because of patient
recruitment problems
21. AI models used to enhance
patient cohort selection
By reducing population heterogeneity
Prognostic enrichment
Predictive enrichment
Electronic phenotyping
22. AI techniques used to automatically
analyze EMR and clinical trial eligibility
databases, find matches between
specific patients and recruiting trials,
and recommend these matches to
doctors and patients
Predict the risk of dropout for a
specific patient
23. 6). Pharmacovigilance
Insertion of structured and unstructured content: NLP
and ML are used to extract ICSR information in a
regulatory compliant manner
AI for decision-making: AI may play an important role
in predicting the new ADR
30. Advantages
Improve the quality and accuracy
Cost-effective
Timely manner
Can handle diverse types data formats
Transparent data sharing with
regulators, prescribers
Prediction of new ADR
Digital transformation has long been a buzzword in healthcare, although it is already advanced in other sector- healthcare sector is very slow adopter of new technology. Few years ago, there was little interest in automation but, today, pharma companies want to adopt tech
https://www.labiotech.eu/in-depth/ai-drug-development-covid/
With the global Covid-19 outbreak in early 2020, pharma companies and biotechs have increasingly turned to artificial intelligence to improve precision and speed in drug development.
Before COVID era- knowing AI and learning was a passion, but now it is necessitity in many indursrty.
Artificial intelligence (AI), the ability of machines to learn from new input, is a broad term for a range of computing methods. Recommendation engines used by online shopping or streaming services use forms of AI to learn consumer preferences and tailor recommendations accordingly. This same technology can be used to predict which drugs are more likely to be effective against a specific target without causing severe side effects.
AI also gives researchers the power to analyze disparate datasets. For example, it can combine vast libraries of chemical compounds, biomedical data from the literature, and patient health data into knowledge graphs. This data model creates new connections and insights into previously unrelated information, which researchers can use to make predictions, model novel pathways and disease states, and test their findings.
why- atrrition rate, cost, time , huge data, demand and expectations
The vast chemical space, comprising >1060 molecules, drug development process, making it a time-consuming and expensive task, which can be addressed by using AI. AI can recognize hit and lead compounds, and provide a quicker validation of the drug target and optimization of the drug structure design
Despite its advantages,
a sample of 406,038 entries of clinical trial data for over 21,143 compounds from years 2000–2015, only a small percentage of substances tested are commercially successful and can be used by the pharmaceutical industry. For example, the probability of success (POS) for an orphan drug is 6.2%, and ranges from a minimum of 3.4% for oncology to a maximum of 33.4% for vaccines (infectious diseases)
Traditional PV – presently following paper and pen – manual extraction of data from various sources and analyzing.
Next Gen PV- end to end automation of repetitive manual work
Present scenario what industry experts say, what is the adoption rate
why- atrrition rate, cost, time , huge data, demand and expectations
a sample of 406,038 entries of clinical trial data for over 21,143 compounds from years 2000–2015, only a small percentage of substances tested are commercially successful and can be used by the pharmaceutical industry. For example, the probability of success (POS) for an orphan drug is 6.2%, and ranges from a minimum of 3.4% for oncology to a maximum of 33.4% for vaccines (infectious diseases)
A major reason to keep moving forward is the sheer number of signals that need to be analyzed. “Vaccines that were tested on 30,000 or 40,000 subjects in a clinical trial have now been administered to hundreds of millions of people in a matter of three or four months”, he noted. That requires safety organizations to accelerate their work as rapidly as clinical trial teams have done – and, like them, avoid returning to “the old way” of managing processes.
At the same time, the immense public awareness of clinical trials and testing surrounding COVID-19 vaccines brings another big change to PV. “Patients not only deserve to know their medicines are safe, they are now sharing data, either through self-reporting systems like the CDC’s VSafe app or via social media and other channels,” Palsulich said.
Increase in newer devices and drugs in markets increase volume of patients safety reports that are Structured and unstructured data from various sources , due to increased awareness (like social media, publications, personal devices) majority of ICSR received are unstructured content required more efforts frustration in to process 10000s of ICSR , More patient-centric data will be available from more disparate sources
Manpower alone cannot manage the increasing data volumes and complexities. This is due to increased volume of adverse events and pressure from businesses to lower their costs through automation.
Because of these reasons, traditional PV will unsustainable in near future. Therefore need new tech like for better management and better scale up drug safety activities through automated intake and
https://www.pharmexec.com/view/future-pharmacovigilance-and-regulation-management-depends-automation
More and more drug approval, public awareness, patient centric, social media
https://www.expresspharma.in/pharmacovigilance-during-a-pandemic/
bench to the bedside can be imagined given that it can aid rational drug design
better compounds going into clinical trials (related to the structure itself, but also including the right dosing/PK for suitable efficacy versus the safety/therapeutic index, in the desired target tissue);
better validated targets (to decrease the number of failures owing to efficacy, especially in clinical Phases II and III, which have a profound impact on overall project success and in which target validation is currently probably not yet where one would like it to be
better patient selection (e.g., using biomarkers
better conductance of trials (with respect to, e.g., patient recruitment and adherence)
In past- that is not required- but- bioassya- screen all drugs experiamerntally - Lock and key (differnt types of locks)
Proteins are the building blocks of life, responsible for most of what happens inside cells. How a protein works and what it does is determined by its 3D shape — ‘structure is function’ is an axiom of molecular biology. Proteins tend to adopt their shape without help, guided only by the laws of physics.
For decades, laboratory experiments have been the main way to get good protein structures. The first complete structures of proteins were determined, starting in the 1950s, using a technique in which X-ray beams are fired at crystallized proteins and the diffracted light translated into a protein’s atomic coordinates. X-ray crystallography has produced the lion’s share of protein structures. But, over the past decade, cryo-EM has become the favoured tool of many structural-biology labs.
mathematical modeling techniques that can be used to ,
It was shown by Lipinski,37 who introduced a rule of five which defines molecular properties essential for a drug's pharmacokinetics in the human body, that the chemical space might contain as many as 1060 compounds when taking into consideration only basic structural rules.
The biggest databases are GDP-13,45 containing approximately 970 million compounds, and GDP-17,46 containing 166 billion organic small molecules, both freely available for researchers
Fig. 1. The molecular property prediction flow chart. Orange dash arrows depict representations with information loss. Blue solid arrows represent the mathematical transformation without information loss. Yellow arrowsrepresent the learning process. Starting from the upper left, a molecule is composed of a group of atoms held together by chemical bonds in 3D space. Analytical chemistry techniques can be used to identify the composition of atoms and bonds in a molecule. A typical way of representing molecules is through a 2D molecular graph, aka, molecular structure. In addition, analytical measurements can also be used directly to calculate molecular descriptors. Most molecular representations start from the molecular structure that can be further converted or abstracted to SMILES, Graph Model, Fingerprint, and Descriptors. Inspired by representation learning, these molecular representations can be further converted into molecular embeddings through deep learning models. Once the molecules are converted to proper representations, machine learning models can be applied to build molecular property prediction models.
In machine learning methods, knowledge about drugs, targets and already confirmed DTIs are translated into features that are used to train a predictive model, which in turn is used to predict interactions between new drugs and/or new targets.
Patient cohort selection and recruiting mechanisms
https://www.sciencedirect.com/science/article/pii/S0165614719301300
Clinical trials are usually not designed to demonstrate the effectiveness of a treatment in a random sample of the general population, but instead aim to prospectively select a subset of the population in which the effect of the drug, if there is one, can more readily be demonstrated, a strategy referred to as 'clinical trial enrichmen.
Recruiting a high number of suitable patients does not guarantee success of a trial, but enrolling unsuitable patients increases the likelihood of its failure
AI models and methods can also be used to enhance patient cohort selection through one or more of the following means identified by the Food and Drug administration (FDA): (i) by reducing population heterogeneity, (ii) by choosing patients who are more likely to have a measurable clinical endpoint, also called 'prognostic enrichment', and (iii) by identifying a population more capable of responding to a treatment, also termed 'predictive enrichment
The Central Drugs Standards Control Organization (CDSCO) (Pharmacovigilance Gsr 287 € dated 8-03-2016, REGD.D.L.-33004/99) has made it mandatory for the MAHs to report ICSR of the marketed drug in India to National Coordination Center for Pharmacovigilance Programme of India (NCC-PvPI) as well as to them. Currently, 64,441 ICSR has been collected and submitted to NCC-PvPI, Ghaziabad. Finally, these reports will be sent to WHO-UMC, Sweden, through VigiFlow software
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6984023/#__ffn_sectitle
Various factors contribute to the ADR- dose, enviromnet -
most recent Oracle Health Sciences Connect event, April 2021
science fiction for some time. How_x0002_ever, the continued rapid growth in computer_x0002_processing power over the past two decades, the availability of large data sets and the devel_x0002_opment of advanced algorithms have driven major improvements in machine learning
The first problem is that this approach is plausible only in the case of monocausal diseases. Such cases cer- tainly do exist: for example, in the case of viral infections in which a certain proteaseis requiredfor replication or a receptor is required for cell entry. Also, target-based drug discovery has led to a significant number of approved drugs, and in particular to follow-up com- pounds when a system tends to be better understood. This approach has shown real impact [29]. However, only a minority of more- complex diseases fall into this category, leading to frequent failures in the clinic, in particular as a result of poor efficacy
The second problem is that achieving activity in a model system, such as against isolated proteins, neglects the question of whether the compound reaches its intended target site
artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare.
quality of decisions regarding which compound to take forward (and how to conduct clinical trials) are more important than speed or cost
current proxy measures and available data cannot fully utilize the potential of AI in drug discovery, in particular when it comes to drug efficacy and safety in vivo
machine learning and artificial intelligence (AI). Although the terminology differs, what matters at the core is (i) which data are being analysed and (ii) which methods are used for this purpose.