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Chemistry Data for Systems Thinkers

Systems thinking has become an essential part of modern medicinal chemistry and new drug development (1). Dealing with the increasing data volumes, information silos and low interoperability has become one of the biggest challenges to medicinal chemists when trying to take a holistic approach to identify the interactions and hidden connections within the organelles, cells, tissues, organisms. Often, we wonder:

“Am I seeing the big picture without losing insight of the details?”

In this webinar, we will discuss how to take a system approach to create new chemistry knowledge, to translate knowledge into useful applications and finally to be ready to face the unfolding world crises (2). The topics include
- Systematic integration of biological and chemical data;
- AI-ready data for synthesis route design and prediction;
- Three practical examples using system thinking examples, including
1. digitalization of chemistry knowledge in pharmaceutical industry,
2. responding to COVID-19 pandemic using conscientious data excerption from literature,
3. data readiness in green chemistry to support sustainability.

Change management and education are inevitably critical to pursuing systems thinking approach, therefore we will talk about some best practices for pharmaceutical industry and educational system based on our learning's from the collaborative projects.

(1) Systems Thinking for Medicinal Chemists, Jacobs Journal of Medicinal Chemistry, 2015, I (1),004
(2) One-world chemistry and systems thinking, Nature Chemistry, 2016, 8, 393–398

Paul Dockerty, PhD, is a Customer Engagement Manager in the Professional Services group at Elsevier, now responsible for supporting pharmaceutical customers in their digitalization journey. He is passionate about using data as a leverage to fight the natural resistance to change in digital transformations.
Recorded May 26 2020 57 mins
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Presentation preview: Chemistry Data for Systems Thinkers

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  • Pharma data as an asset: how to become information centric Jul 8 2020 2:00 pm UTC 60 mins
    Dr Martin Romacker
    Life Sciences and pharma are moving to a more data driven approach. Data is seen as an asset, yet not treated as such, with levels of investment not matching the value that reusable data will bring across the R&D value chain. Effective AI, ML and predictive outcomes are dependent on access to clean, reusable data and getting that clean data is often hard, time consuming and challenging.

    FAIR Data principles provide you with the perfect blueprint to overhaul your data processes.

    In this webinar, Dr Martin Romacker – Principal Scientist, Data and Information Architecture, Roche will walk us through how to access clean reusable data that will drive value across the R&D value chain and will outline areas where Roche is taking action to enable better and more effective data reuse.
  • Computer Programs for Semi-Automation of Evidence Synthesis Recorded: Jun 10 2020 60 mins
    Dr. Farhad Shokraneh, Research Fellow School of Medicine, University of Nottingham
    While all types of literature review are becoming reasonably more attractive for students, researchers, practitioners and policy makers the workload involved in all types of evidence synthesis should not be underestimated. Apart from standardization of procedures and methods, many organizations and collaborations started using computers to reduce the workload and save time in processing all types of reviews. Systematic reviews and meta-analyses, scoping reviews, rapid reviews, overviews, and realist reviews are only some members of review family that can benefit from using computer programs. The research, innovations, discussions, and skepticism around and surrounding the automation became so important that some of automation pioneers started International Collaboration for Automation of Systematic Reviews (ICASR) https://icasr.github.io/. The current webinar will also benefit the outcome of ICASR annual meetings.

    Despite emergence and listing of hundreds of tools in Systematic Review Toolbox (http://systematicreviewtools.com/), these software programs are underused. This webinar will introduce some of these programs alongside the evidence supporting their use and will provide a guide on how to choose the program, when to use them, what are their advantages and disadvantages, and why we should use them. There are automation tools for searching, screening, extracting data, analysis, and report writing. The presentation will also discuss the reasons for underusing problem and its solutions and will justify the fact that automation of evidence synthesis is still an idealist dream and why semi-automation of evidence synthesis is more realistic horizon for in the next decades.

    About the speaker: Farhad is an expert in systematic review (SR) methodology and automation, and manages the largest database (over 340 SRs) of schizophrenia trials. He uses Embase to provide search and consultancy to academicians, industry, clinicians and policy making teams around the world.
  • Reaxys Training Webinar 2-Structure Search_日本語版 Recorded: Jun 9 2020 33 mins
    Dr Marta da Pian
    Reaxysトレーニングビデオ:構造検索:Marvin JS/ ChemDrawエディタから反応検索まで
    ※日本語字幕付

    - MarvinJS/ChemDraw editor (drawing tools and checkbox panel)
    - Reaction search (product/reagent/catalyst, Atom mapping, Reacting center)
    - Diving into the Result panel
  • Chemistry Data for Systems Thinkers Recorded: May 26 2020 57 mins
    Paul Dockerty
    Systems thinking has become an essential part of modern medicinal chemistry and new drug development (1). Dealing with the increasing data volumes, information silos and low interoperability has become one of the biggest challenges to medicinal chemists when trying to take a holistic approach to identify the interactions and hidden connections within the organelles, cells, tissues, organisms. Often, we wonder:

    “Am I seeing the big picture without losing insight of the details?”

    In this webinar, we will discuss how to take a system approach to create new chemistry knowledge, to translate knowledge into useful applications and finally to be ready to face the unfolding world crises (2). The topics include
    - Systematic integration of biological and chemical data;
    - AI-ready data for synthesis route design and prediction;
    - Three practical examples using system thinking examples, including
    1. digitalization of chemistry knowledge in pharmaceutical industry,
    2. responding to COVID-19 pandemic using conscientious data excerption from literature,
    3. data readiness in green chemistry to support sustainability.

    Change management and education are inevitably critical to pursuing systems thinking approach, therefore we will talk about some best practices for pharmaceutical industry and educational system based on our learning's from the collaborative projects.

    (1) Systems Thinking for Medicinal Chemists, Jacobs Journal of Medicinal Chemistry, 2015, I (1),004
    (2) One-world chemistry and systems thinking, Nature Chemistry, 2016, 8, 393–398

    Paul Dockerty, PhD, is a Customer Engagement Manager in the Professional Services group at Elsevier, now responsible for supporting pharmaceutical customers in their digitalization journey. He is passionate about using data as a leverage to fight the natural resistance to change in digital transformations.
  • Addressing Questions & Unmet Needs in Melanoma Research and Treatment Recorded: May 19 2020 60 mins
    Marc Hurlbert; Tom Williams
    The landscape for melanoma research and treatment has rapidly changed over the last decade. Since 2011, the FDA has approved 12 new melanoma treatment regimens – including new classes of drugs that are molecularly targeted therapies (BRAF/MEK inhibitors), immune checkpoint inhibitors (anti CTLA-4, PD-1/PD-L1) and other immunotherapies (e.g. T-Vec). Scientists have also unraveled many of the genomic mutations found in the most common form, cutaneous melanoma, melanoma that arises primarily on sun-exposed areas of the skin. With these advances in research and treatment, the key unanswered questions have changed rapidly and existing preclinical models may not be sufficient to answer such questions surrounding immune checkpoint inhibition; resistance development, comparing to cuaneous melanoma, and how to improve early detection.

    Importantly, there are no models that accurately predict the patient journey. New models and additional research is needed to more fully represent all melanoma subtypes, stages, or treatment responses.

    About the speakers:
    Marc Hurlbert, Ph.D. Chief Science Officer, Melanoma Research Alliance. Marc is currently responsible for guiding MRA’s scientific strategy, overseeing the peer-reviewed grant-making program, and forging scientific collaborations. He has more than 18 years of nonprofit and grant-making experience focused on advancing medical research. Past work has included treatment and prevention strategies for breast cancer, lymphoma and multiple myeloma, as well as juvenile diabetes.

    Tom Williams, PhD, Life Sciences Professional Services Project Manager, Elsevier. Tom is a Life Sciences Knowledge Manager and Research Scientist. with extensive experience as an academic researcher in neurodegeneration and Alzheimer’s disease. He is also in skilled biophysical chemistry, dementia disorders, and biochemistry; and the author of many publications in the field of Alzheimer’s disease.
  • Optimizing clinical trial design with extracted efficacy data Recorded: May 7 2020 46 mins
    Marnix Wieffer
    Around 90% of the small molecule drugs that enter clinical trials do not make it to the market. Therefore, optimizing clinical trial design and reducing late stage failures are key priority for drug developers.

    With Phase II efficacy-related failure rates as high as 57%, many companies are seeking ways to improve their outcomes and reduce the climbing $2.6 billion costs to get one drug to market. As clinical trials become increasingly more complex and costly, is even more critical to mitigate the risk of failed clinical trials or arms due to suboptimal study design or poor efficacy

    Join us for this 45-minute webinar where Customer Consultant drug safety Jean-Dominique Pierret and Drug Safety Marketing Manager Dr. Marnix Wieffer will discuss how using PharmaPendium we can uncover critical information to make better more informed clinical development decisions.

    This will include in-depth information and demonstrations of how to leverage the comparative data in PharmaPendium to reduce the risk of late-stage failures. With a focus on efficacy, we will discuss how PharmaPendium enables you to:

    •Find efficacy weaknesses early
    •Identify the most appropriate preclinical models,
    •Improve success rates of Phase I and II clinical trial designs by optimising selection of sample size
    •Primary/secondary endpoint and study design and
    •Prepare for more effective regulatory reviews
  • Clinical and biochemical data-driven drug re-purposing for anti-infective drugs Recorded: Apr 30 2020 79 mins
    Andrey Khudoshin
    Drug repurposing has been shown to be advantageous for treating rare and common diseases. A data-driven drug repurposing approach may not only accelerate the time to reach the market but also helps in reducing costs and the clinical steps required, with the pre-existing knowledge of potential side-effects, special situations like age, gender or pregnancy, possibility to use combination with other drugs for more effective treatment, etc.

    In the webinar, five data-driven strategies for antiviral and antibiotic drug research and development will be discussed:
    • Search leveraging clinical data on drugs and biomolecules for treatment of related viral and bacterial disorders
    • Search for substances reported to be active against related viruses and microbes in patents and articles
    • Search for substances that interact with viral and bacterial proteins
    • Investigation of compounds affecting human proteins, involved in the viral life cycle
    • Assessment of the safety of drug candidate
    Above approaches will be demonstrated, utilizing the clinical data from Embase and experimental biochemical data from Reaxys Medicinal Chemistry

    About the speaker:
    Dr. Andrey Khudoshin holds a PhD in Chemistry from Lomonosov Moscow State University. Before starting a corporate career in the field of chemistry, biology and drug R&D in various international companies, he also completed his postdoctoral research on transformation of natural compounds to valuable substances, like raw materials for green chemistry and potential bioactive compounds. He joined Elsevier in 2015 and since then he is involved in the implementation of Elsevier's Life Science solutions, supporting increase the effectiveness of drug R&D.
  • Accelerate drug discovery by building and turning data into actionable insights Recorded: Apr 28 2020 57 mins
    Dr. Min Lu, Dr. Rosalind Sankey
    The prioritization of hits from large compound lists for further follow-up is a challenging task for medicinal chemists. During this step of drug discovery, multiple parameters such as synthetic accessibility, target specificity, physicochemical properties, and potential toxicities, in addition to desired biological activity, must be considered simultaneously. Increasing amounts of biological data are accumulating in the pharmaceutical industry and published literature (including journals and patents).

    However, data does not equal actionable information, and guidelines for appropriate data capture, harmonization, integration, mining, and visualization need to be established to fully harness its potential. Here, we describe ongoing efforts at Merck & Co. to structure data in the area of discovery chemistry. We are integrating complementary data from both internal and external data sources (Reaxys) into one, and will demonstrate how this well-curated database facilitates compound set design, tool compound selection, target deconvolution in phenotypic screening, and predictive model building (e.g. target prediction).

    Early in the discovery process, chemists select a subset of compounds for further research, often from many viable candidates. These decisions determine the success of a discovery campaign, and ultimately what kind of drugs are developed and marketed to the public. We present our findings in the context of complex problem solving and decision theory, and discuss the implications on drug discovery.

    Speakers
    Min Lu, Ph.D. Merck Research Laboratories, Boston, USA
    Rosalind Sankey, Ph.D. Elsevier Chemistry Solutions, Frankfurt, Germany
  • Finding novel lead compounds in pesticide discovery inspired by pharma research Recorded: Apr 8 2020 49 mins
    Dr Maria Shkrob, Dr Frederik van den Broek
    The use of high throughput (HTP) methodologies for supporting discovery and development of new agrochemical products opens up new opportunities to test many new compounds potentially acting on biological targets in various organisms. Finding new lead compounds which might act as a new pesticide can sometimes be a lengthy process; we present a method which can provide lead compounds by using the breath of information available from pharmaceutical research.

    This webinar will give an overview of the chemical and biological informatics methods and data used to map compounds active against biological targets in parasites in humans to fungal targets. The webinar will then also explore how to arrive at insights in structure activity relationships and freedom to operate in the chemical space for these candidate compounds. Thereby demonstrating how findings from pharmaceutical research can be transferred to fungal research.
  • Applications of Biomedical Knowledge Graph for AI and Machine Learning Recorded: Mar 11 2020 60 mins
    Anton Yuryev
    Biomedical knowledge graphs (BMKGs) link biomedical entities (such as diseases, proteins, and drugs) through certain defined relationships. They are important tools to computationally analyze the comprehensive body of biomedical knowledge.

    In this webinar, Anton Yuryev, Biology Director at Elsevier will talk about the approaches and use of knowledge graph together with artificial Intelligence algorithms for various biomedical applications such as drug repurposing, personalized drug therapy, and personalized immunotherapy.

    The talk will cover
    - comparing different graph embedding techniques
    - introducing a new graph embedding technique that uses patient OMICs data to calculate node activity using sub-network enrichment analysis
    - how to use node activity to find likely disease mechanism in a patient and use this mechanism to predict personalized treatment or neoantigen vaccine design, or new target discovery

    About speaker:
    Dr. Anton Yuryev has PhD in Genetics from Johns Hopkins University where he discovered proteins physically linking gene transcription with mRNA processing in eukaryotic cells. He worked over 30 years in bioinformatics as Senior Scientist at InforMax, as Senior Bioinformatics Analyst at Orchid Cellmark, and as Senior Director of Application Science at Ariadne Genomics. Dr. Yuryev published over 50 scientific articles, edited four scientific books, authored algorithms for primer design and pathway analysis. He currently serves as Professional Services Director at Elsevier and responsible for development of targeted bioinformatics solutions using Elsevier proprietary software, knowledgebases and artificial intelligence in the areas of drug development, personalized precision medicine, agro- and synthetic biology.
  • Drug repurposing for rare diseases: an integrated data driven approach Recorded: Feb 27 2020 53 mins
    Jabe Wilson; Megan Golden
    Pharma R&D for rare diseases is, itself, rare, because patient populations are perceived as insufficient to deliver ROI. Drug repurposing eliminates the expensive process of discovering a completely new compound, shortens the time that is needed to reach the patients, and ensures a higher success rate.

    Elsevier and Pistoia Alliance organized a drug-repurposing datathon, with Cures Within Reach and Mission: Cure being the consulting organizations. The objective was to identify repurposable drug candidates for chronic pancreatitis – a rare disease that affects about 1 million people globally, and currently doesn’t have an approved treatment. As a result, this datathon identified 4 drug candidates in 30-60 days. They were reviewed and approved by the expert panel, pending further clinical trials by Mission:Cure.

    This webinar will talk about this unique non-profit and private collaborative datathon, using Entellect, an AI-powered technology platform for identifying repurposable drug candidates for chronic pancreatitis.

    The topics will include:
    - Introduction to datathon
    - Predictive analytics for drug repurposing - needs and challenges
    - Established strategy and workflow
    - Outcome and impact
    - Update to the clinical trial progress

    About speaker:
    Jabe Wilson, Global Commercial Director, Data and Analytics, Elsevier
    Megan Golden, co-founder and co-director, Mission: Cure
  • Predicting drug-drug interactions to reduce adverse event risk Recorded: Feb 26 2020 49 mins
    Marnix Wieffer
    Adverse drug reactions (ADRs) are a serious problem worldwide. One reason for the increase in ADRs is the growth in prescription use—especially among aging populations where drug–drug interactions (DDIs) are more likely. Currently, 9 percent of Americans over age 55 take 10 or more prescription drugs, which greatly increases the likelihood of DDIs and ADRs.

    Identifying potential drug-drug interaction risk is a key priority for pharmaceutical manufacturers and regulatory authorities. To minimize health risks to clinical trial subjects and patients, assessments should be performed as early as possible in development.
    Join solution marketing manager Dr Marnix Wieffer for this webinar where he will discuss outstanding issues with predicting Drug-drug interaction risk and possible solutions. We run through a couple of live examples that show how PharmaPendium is supporting Drug-drug interaction risk prediction.
    Using PharmaPendium we will investigate
    •What enzymes and transporters act on my drug of interest?
    •What is the DDI risk for drugs that are substrates CYP2D6?
    •What is the DDI risk of my drug under development with Antiarrhythmic drugs?
  • Using machine learning to identify adverse events from scientific literature Recorded: Feb 19 2020 61 mins
    Umesh Nandal
    Information found in the biomedical literature is a significant source for tracking and reporting adverse drug reactions (ADR). The EMA and FDA have both mandated that market authorization holders maintain active screening of literature for any mentions of ADRs related to their drugs or other medicinal products. Given the increasing amount of literature, manual screening, reviewing and monitoring literature costs more time, money and creates an additional compliance risk. Using the advanced technologies in Artificial intelligence (AI), Machine learning (ML) and Natural language processing (NLP), we have developed models to identify Adverse events (AE) in the literature, which can save considerable time and effort in large-scale analysis and in integrating data from multiple diverse information sources.

    This webinar will discuss:
    - the challenges of literature mining using AI
    - the Biomedical Named Entity Recognition (BNER) and its advantages for information extraction tasks
    - how to create a quality training set for machine learning
    - the experiment outcome and further applications

    About speaker:

    Umesh Nandal, PhD, is the Principal Machine Learning & NLP scientist in Content Transformation (CT) department at Elsevier. With a background in Chemistry and computational biology, Umesh is applying state-of-the-art methods in ML and NLP to improve or build new life science products of Elsevier that can help researchers in getting correct answers to their questions quickly. Prior to joining Elsevier, he used various ML and computational approaches to analyse molecular data generated from high-throughput technologies to understand biological processes in healthy and diseased organisms. During his PhD, he intensively worked on the comparison of mouse models with humans by building a network based integration method that can compare their biological networks.
  • Practical data management for greater collaboration in pharma R&D Recorded: Jan 14 2020 44 mins
    Ted Slater, Sr. Director of Product Management PaaS, Elsevier
    The Life sciences industry has always relied on empirical data in order to identify patterns, test theories and understand the efficacy of treatments. The increase in data - in volume, variety and velocity necessitates a digital transformation; moving to greater collaboration and more effective partnerships. This would require more clear and practical guidance on how data is captured and managed.

    In this webinar, Ted Slater – Sr. Director of Product Management PaaS will discuss
    •What are the FAIR data principles?
    •What are the benefits of creating and using FAIR data for drug discovery and development?
    •Elsevier’s initiatives in leverage the FAIR guiding principles, and
    •How to build around FAIR data principles to drive research programs

    About the speaker
    Ted Slater is Senior Director for Product Management PaaS at Elsevier. He is one of the authors on the seminal 2016 FAIR guiding principles paper and has held senior roles in large pharmaceutical and computing technology companies, content providers, and biotechs. He holds master’s degrees in Molecular Biology from the University of California at Riverside, and in Computer Science from New Mexico State University.
  • Relevance of index databases in navigating data and information in Chemistry Recorded: Dec 10 2019 58 mins
    Jürgen Swienty-Busch, Elsevier and Ye Li, PhD, MLIS Chemistry and Chemical Engineering Librarian, MIT
    This webinar will examine how index databases can help navigate chemical information efficiently, and how the structured chemical data may enable innovative discoveries. Attendees will be able to:
    •Understand the intellectual value of chemical data and literature indexing
    •Explore functionalities based on indexing and taxonomy in databases
    •Recognize innovative and computational trends of using structured chemical data
  • Leverage PK data in PharmaPendium to inform drug development strategies Recorded: Dec 5 2019 49 mins
    Marnix Wieffer
    Pharmacokinetic information from FDA and EMA regulatory documents informs translational and clinical development decisions and may lead to more successful drug development and regulatory approval strategies.

    Pharmacokinetic information from FDA and EMA regulatory documents informs translational and clinical development decisions and may lead to more successful drug development and regulatory approval strategies.
    Join solution marketing manager Dr. Marnix Wieffer for this webinar, where he will discuss how to leverage extracted pharmacokinetic data from literature and FDA/EMA Drug Approval documents to make better-informed decisions on which drugs have the most potential to succeed in clinical development.

    Using PharmaPendium we will
    •Retrieve detailed information on PK parameters on approved drugs
    •Retrieve all data on PK parameter of interest (for example Cmax) on drug acting on the same target I am working on
    •Investigate what is the best translational model to assess Serum protein binding
    •And much more
  • Effective and Compliant Literature screening for Pharmacovigilance Recorded: Dec 3 2019 47 mins
    Jean Dominique Pierret
    Biomedical literature is an important source of adverse event reporting. However, due to the high volume of literature, sub-optimal search strategies and regulatory pressure, pharmaceutical companies and CROs are facing significant challenges around costs, efficiencies, oversight and regulations.

    During this webinar, Dr Jean Dominique Pierret, Customer Consultant Pharmacovigilance at Elsevier, will discuss how, through query design, prioritization technology and outsourcing we can significantly improve efficiency and compliance of literature screening.
    We will explore:
    1.The optimization of queries in literature databases
    2.The benefits if literature workflow management
    3.Efficiency gains through prioritization
    4.How outsourcing can support efficiency gains
  • Introduction into PharmaPendium: informed decision making during development Recorded: Oct 3 2019 43 mins
    Marnix Wieffer
    Optimal decisions making during pharmaceutical development critically relies on high quality data. PharmaPendium is the go-to database to access extracted data from regulatory documents and literature on approved drugs
    Join PharmaPendium Marketing Manager Dr. Marnix Wieffer for this introduction webinar where we will discuss why we need high quality extracted data on marketed drugs during drug development and provide an introduction in:
    -PharmaPendium’s unique document and data content
    -The expert taxonomies that help to unlock critical data
    -The benefits of both searching in full text through regulatory documents and accessing extracted data
    -Example use cases showing how we can work with PharmaPendium to uncover important reviewer insights by full text searching in regulatory documents
    -Gain translational inside by recovering extracted drug safety data
  • New Quosa Recorded: Sep 17 2019 51 mins
    Ken Karapetyan
    We will give updates on the status of the new Quosa, how it will be different from the existing Quosa. We will share the development roadmap and will discuss the migration plan.
  • PharmaPendium August release: Unlocking and sharing critical safety insights Recorded: Sep 5 2019 47 mins
    Marnix Wieffer
    Optimal decisions making during pharmaceutical development critically relies on high quality data. PharmaPendium is the go-to database to access extracted data from regulatory documents and literature on approved drugs

    Join PharmaPendium Marketing Manager Marnix Wieffer to discover the new features of the upcoming PharmaPendium release (week of 19 August) that will support uncovering critical safety insights and effective information sharing. We will discuss:
    1.Gain translational insights through improved Boolean operator drug safety searching
    2.Streamline information exchange through new sharing searches function
    3.Highlight critical post-market data through custom colouring of the FAERS heatmap

    The webinar includes demonstrations of all the new features and a Q&A session for attendees to get further information about PharmaPendium’s drug safety and data sharing workflows
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  • Live at: May 26 2020 2:00 pm
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