At Digital Science, we recognize that the journey toward AI adoption is as unique as the organizations and individuals we support. From bench researchers to medical affairs professionals to research offices, our approach is grounded in collaboration and deep understanding.

Since 2013, we’ve been investing in advanced AI methodologies, expanding our technical and analytical capabilities, and assembling a global team of AI experts. To us,  AI isn’t a one-size-fits-all solution; it encapsulates a range of both new and existing capabilities and approaches that when thoughtfully applied, can significantly enhance capabilities and streamline workflows. Our commitment continues to be focused on working closely with our partners, deeply understanding their unique challenges and aspirations, to deliver innovative and responsible AI capabilities that enhance human intelligence, drive progress, and unlock the full potential of the research community.

Our Capabilities

For the last decade, we have focused around machine learning innovations with Dimensions.ai, investment in Writefull and development of different LLMs. Building on this AI lineage, 2024 will see a continuous flow of new releases, starting with Dimensions Research GPT Enterprise and Dimensions Research GPT.

Dimensions in ChatGPT

Available via OpenAI’s GPT Store, the new products aim to provide users looking to use ChatGPT for research-related questions with generative answers they can trust – grounded in scientific evidence from Digital Science’s Dimensions database.

Key features of Dimensions Research GPT Enterprise – available to Dimensions customers with a ChatGPT Enterprise licence – include: 

• Answers to research queries with publication data, clinical trials, patents and grant information
• Set up in the client’s private environment and only available to client’s end users
• Notifications each time content generated is based on Dimensions data, with references and citation details
• Possible for clients to have custom features (following prior discussion with Dimensions).

For Dimensions Research GPT, answers to research queries are linked to tens of millions Open Access publications, and access to the solution is free to anyone with a Plus or Enterprise subscription to OpenAI’s GPT Store.

Next-generation search experience

Dimensions has introduced a new summarization feature to support the user in their discovery process for publications, grants, patents and clinical trials. It has integrated AI-driven summarization capabilities into the Dimensions web application to enable all users to accelerate the identification of the most relevant content for their research questions. Short, concise summaries are now available for every record in a given search result list with a single click, providing users with AI-generated insights quickly. The Dimensions team has used feedback from members of the research community – including academic institutions, industry, publishers, government, and funders – to develop this summarization feature in the Dimensions web app.

Smarter searching in Dimensions

Other AI solutions will follow shortly from Digital Science, all of which seek to surface AI capabilities to support users with specific, relevant functionalities where AI in particular can offer improved results. Just as importantly, they have been developed with a grounding in reliability and responsibility so that users can trust them as they do with all our other products. 

Connecting your Data

The Dimensions Knowledge Graph, powered by metaphactory, aims at helping customers harness the synergy of global research knowledge and their internal data, and enable AI-powered applications and business decisions.

AI-Powered Writing Support

Writefull uses big data and Artificial Intelligence to boost academic writing. With language models trained on millions of journal articles, it provides the best automated language feedback to date leading the next generation of research writing help.

Deeper Understanding of Scholarly Papers

Available within ReadCube Enterprise Literature Management & Papers Reference Management, our beta AI Assistant is designed to enhance research efficiency by providing real-time, in-depth analysis, summarization, and contextual understanding of scholarly articles within a researcher’s literature library.

Our latest AI insights

An experienced partner in AI

The history of AI at Digital Science

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AI & Digital Science

How does Digital Science use AI? We ask ChatGPT

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The post Digital Science and Artificial Intelligence appeared first on Digital Science.

Featured Resource: New agile methods to extract deeper knowledge

Find out how we provide an open and iterative approach by connecting our tools with those of the research community. We develop special functions within a matter of days allowing you to implement a search strategy, connect with a diverse range of data sources and so much more.

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Providing valuable data on funding flows for market model development

Find out how Illumina uses Dimensions to understand trends in academic funding, KOL identification and to find leads for sales.

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“ It is really beneficial to be able to use the same keywords across the 3 different content types – grants, pubs and clinical trials – in one platform.”

Aruna Rajan, Staff Market Research Analyst, Illumina

Getting started with Dimensions L&C

In this video learn how to run semantic search in Dimensions L&C to get a list of precise and comprehensive results. We will also explore the ontological tree within Dimensions L&C, which contains over 40 million concepts related to life sciences and chemistry, and finally we will see a quick overview of the analytical and search tools Dimensions L&C has to offer.

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Generate insights quicker using Co-occurrence Analysis

In this video learn how to run a Co-occurrence Analysis in Dimensions L&C, a powerful tool which enables you to generate insights from millions of scientific documents within seconds. We will explore general and specific use cases for this function to enable you to get the most out of Dimensions L&C.

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Understand disease mechanisms

In this video learn how to use “co-occurrence analysis” in Dimensions L&C to get a better understanding of disease mechanisms. We will generate insights from hundreds of millions of scientific documents by correlating a disease with semantic concepts – such as signalling pathways, genes & proteins and pathophysiological processes – to quickly generate hypotheses on disease mechanisms, which is an important first step in drug target identification and the drug development process in general.

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Understand drug candidate mechanisms of action

In this video learn how to use “chemistry search” to discover chemical compounds similar to your drug candidate or containing a specific chemical substructure of your interest. We will run a comprehensive search to identify documents, such as patents, publications, grants and clinical trials, that mention relevant compounds and apply a powerful semantic concepts extraction analysis to generate insights on the compounds’ mechanisms of action and therapeutic applications.

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Clarify the mechanism of action

To select the best therapeutic application for repositioning a known drug or avoid undesirable adverse events for novel compounds, it is critical to have a clear understanding how the drug or compound works.  

To uncover these insights, you need to find and analyze all information available on a drug/compound or those similar to it. The powerful semantic search of Dimensions L&C allows you to search through 40 million concepts, including new compounds, approved drugs, genes, and proteins alongside chemistry search and co-occurrence analysis.  This enables you to identify more relevant documents and gain deeper insights about drug and compound connections to proteins, disease, signalling pathways, pathophysiology, and toxicities.

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Analyze disease mechanisms

Understanding target and disease biology is crucial for success when bringing a new drug to market. However, the data supporting this understanding is spread across hundreds of thousands and millions of scientific documents, including publications, patents, grants, and clinical trials.  Dimensions L&C provides a powerful semantic search with up to 40 million concepts that incorporate genes,  proteins and diseases, derived from hundreds of millions of scientific documents, together with co-occurrence analysis.

This enables you to quickly discover more relevant documents and gain deeper insights regarding disease mechanisms and the connections between diseases, proteins, signalling pathways, and biological processes in a real-time.

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Identify drug targets

Identification of a “right” drug target is a critical step in drug development that helps to avoid later stage failures of drug candidates in clinical trials. To make an evidence-based decision on drug targets, a compilation and analysis of multiple scientific documents are required.

Dimensions L&C provides a powerful semantic search of up to 40 million concepts, including genes & proteins, diseases and corporate information, together with co-occurrence analysis. This enables you to rapidly discover more relevant documents and gain deeper insights as to potential drug targets and their connections to diseases, drugs, biological and pathological processes, and commercial potential in a real time.

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Driving Innovation & Collaboration Across Boehringer Ingelheim

In this case study, Dr. Karlheinz Spenny and his team at the SIC share what triggered their internal innovation campaign to move from their legacy literature management solution to the Enterprise edition of ReadCube and their experiences along the way.

What’s inside:

• Factors driving the switch from BI’s legacy literature management/citation tool
• Why BI chose Papers Enterprise
• Preparing for & managing change through transition
• The rollout & reactions
• Standout features
• Reflections post-deployment

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“What we have achieved within just 7 months after the roll-out of ReadCube can only be considered a jump to the next level of literature management for our research scientists and has positioned BI as front-runners within our industry.”

Dr. Karlheinz Spenny, Boehringer Ingelheim

Navigating Networks Of Oncology Biomarkers 

Using large-scale analytics of published literature, biomarkers across six cancer types were successfully characterized in terms of their emergence in the published literature and the context in which they are described.

This novel approach could help identify biomarkers and biomarker panels, which may be otherwise missed through traditional search methods, for expert review and exploration in a clinical setting.

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Landscape analysis 

Research is continually evolving and dynamics within and between fields change constantly. Landscape assessments ensure you understand the lay of the land and are prepared to adapt to change. Our analysts use Dimensions to help you:

• Identify major global research areas.
• Understand how scientific fields are changing. 
• Assess emerging trends within a specific field of study. 
• Recognize the experts in these fields.

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Enhancing your Gap Analysis with Machine Enhanced Literature Retrieval

Dimensions data can be used to enhance your literature gap analysis. Dimensions is particularly suitable for performing literature gap analyses on a large scale, combining standard abstract searches with high precision full-text queries and subsequent aggregation. This allows creating heat maps on numbers of publications for combinations of terms, e.g., drugs versus adverse events, with the option to directly view the set of publications for a particular combination. Such an approach takes minutes to run and can be repeated to track changes over time.

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Real word databases identification 

While real-world databases are of increasing interest to researchers, there is no comprehensive dataset listing all real-world databases. We have created an algorithm which finds publications presenting real-world databases:

• Several search methods were applied to select articles which present research using real world databases
• Publications were identified which were frequently cited by the above articles, to arrive at the initial publications announcing real world databases
• With this approach the number of known real world databases could be doubled

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How to Run Your Biotech R&D Virtually

Hire more people, take your groundbreaking data and hit milestones in just 3 steps! We provide a suite of research solutions to hire, store and share data across your companies to make your research efficient and cost-effective. Anyone working in a lab will benefit from this session.

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Pinpointing Global Expertise In Non-Alcoholic Fatty Liver Disease

Disease types associated with NAFLD present a tremendous research and development challenge for life sciences organizations and also a great opportunity for them to create value and innovation. Find out how Digital Science offers the data capabilities to help you uncover insights and expertise in disease-related topics and key indication areas.

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Identifying Global Expertise in CAR-T-time data

Digital Science offers data capabilities and the connected data to help life science organizations understand the Car-T research and development landscape. Find out how Digital Science offers the data capabilities to help you uncover insights and expertise in disease-related topics and key indication areas.

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Measuring the value of medical affairs

As a medical affairs professional or publications planner in the pharmaceutical industry, we understand that you need to demonstrate the impact of the work you do. Download our whitepaper today to find out how altmetrics can help you.

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Get better insights for medical affairs

Spend less time searching for the right information and more time acting on it. Dimensions is a comprehensive discovery and analytics platform with millions of data points, and an ideal information source for medical affairs professionals. 

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Finding the right experts just got easier

Since finding KOLs is a frequently performed and important activity, the Dimensions team created a new tool that makes it even easier. It features a simple and intuitive user interface that accesses all of the data Dimensions has to offer. 

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The post Resources for Industry appeared first on Digital Science.

Generating data is resource-intensive, and increasing efforts are made in industry and academia to support analysis of data compendia as well as reuse of existing datasets for purposes different from those for which they were originally generated. These efforts are exemplified by the FAIR principles which provide guidelines for making data “FAIR”: Findable, Accessible, Interoperable, and Reusable. Retrospective data stewardship to FAIRify existing data is extremely time- and cost-consuming, especially for large, long-established pharmaceutical companies that have accumulated considerable amounts of legacy data, often fragmented across the organization. Over time, important information is lost due to organizational changes and employee turnover, either because the data are not well documented or because they are stored in nonstandard or nonmachine-readable formats. Thus, it is crucial to have in place FAIR play processes from the point of data generation, including clear data and metadata management strategies. Figshare can help manage this process.

Organizations must set expectations and provide incentives for scientists generating data to include rich and harmonized metadata, and the process to do so should be simple and straightforward. Tools for metadata capture need to be intuitive, flexible, and configurable enough to handle new data types in real-time, while adhering to established controlled vocabularies and ontologies. Metadata registration and curation tools should capture identifying, descriptive data and relationships in a single source of truth system and seamlessly propagate information to downstream applications and services. User-friendly study design tools should be integrated with both data production and analysis, providing a platform for experimental scientists and data scientists to collaboratively specify, iterate, and agree upon experimental parameters, analysis methods, and statistical power before running studies.

Importantly, clear data access rules need to be established as part of this FAIRification process. The goal of these rules should be to democratize the data, in which they are no longer accessible by a select few but by the whole organization. This requires a cultural shift away from a “my data” and toward an “our data” mindset. These access policies should clearly establish which data can be accessed by whom and when, with a drive towards reducing bureaucracy and speeding up scientific insights. This is especially valuable in drug discovery and development, during which early and broad access to both preclinical and clinical data may enable the generation of new hypotheses or steer existing programs in new directions. Overall, a FAIR data ecosystem is the foundation from which data scientists select and integrate subsets of data necessary to perform meta-analyses across different datasets.

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Why FAIR is important to Figshare

One of Figshare’s early goals was to encourage the sharing of negative results and to provide a platform to disseminate those results to reduce duplication of work. Our ethos has not changed and even before the formalisation of the FAIR data principles we believed that data should be findable, accessible, interoperable and reusable. As an extension of those principles we also believe in tracking public research outputs via Altmetrics and Dimensions to further encourage making data openly available. We continue to support academic publishers, preprint platforms, researchers, conferences and labs

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Figshare does the heavy lifting

Founder and CEO Mark Hahnel, outlines the FAIR principles and talks in detail about how Figshare does the heavy lifting so you can easily meet the FAIR guiding principles and practices.

The post Are Your DRUG Discovery Teams Playing ‘FAIR’LY appeared first on Digital Science.

More about Digital Science

We’re an innovative technology company. Our vision is of a future where a trusted and collaborative research ecosystem drives progress for all.  Contact us to find out how our products & services can help fuel opportunity, innovation and discovery

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The post How data science can drive competitive advantage in drug discovery research appeared first on Digital Science.

Today’s chapter of our NLP blog series is written by Andrii Buvailo. Andrii is a co-founder and director at BPT Analytics, and also Editor at BiopharmaTrend.com, responsible for all content, analytics, and product development in the project. He has been writing about research and business trends in the pharmaceutical industry for over four years, mainly focusing on the digital transformation of drug discovery. Before moving to the pharma space, Andrii held a number of executive positions in various hi-tech companies. Prior to his industrial career, he spent years as a practising scientist, having participated in numerous research projects in Belgium, Germany, the United States, and Ukraine. Andrii holds a BSc and an MSc in Inorganic Chemistry, and a PhD in Physical Chemistry from Kyiv National Taras Shevchenko University. Outside his professional career, Andrii is a big fan of travel, chess, and digital drawing.

See more posts in the NLP blog series

Building an initial knowledge base about the pharmaceutical industry

BPT Analytics project started back in 2016 with a simple drug discovery market research blog at BiopharmaTrend.com, where we posted our own regular observations about innovations and technology trends in the pharmaceutical industry, focusing on what companies did to advance the field. At that time we started a systematic effort of collecting data about as many drug discovery and biotech companies and startups as we possibly could. The idea was to create a large enough database of properly labelled companies to see if we would be able to later train machine learning models on it. In 2019 we were awarded a Catalyst Grant to advance our efforts.

Today, we already have a database of more than 7,000 pharma/biotech companies and over 3,000 investors active in the area. The list of companies is matched with numerous other databases and information resources, including clinical trials, marketed drugs, research papers and patents, funding rounds, R&D partnerships, and other aspects important to understand each company’s role and position in the pharmaceutical landscape. We gather data from numerous sources, including our web-parsing engine, collections with external APIs, data supplied by users, and data collected manually.

Importantly, we have built the infrastructure to manually curate the inflowing data by our freelancers. This process allowed us to accelerate and scale up our manual data curation effort. In order for this data to become useful for the pharmaceutical professional and other decision-makers, we are building a subscription-based web-interface BPT Analytics where users can conduct their own market research using our data, with advanced filters and powerful visualization tools. The interface is currently in private beta testing mode for basic functionality.

On the horizon: using NLP to automate ontology construction 

While well-organised manual data curation is one way to build a useful market intelligence service for the pharma industry, it is certainly a limited value proposition. For example, our search is limited to exact keyword-based indexing, without any semantic search options. It means that we can only find information using exact terms and parameters. If a document contains a slight variation of the same term, our search will not be able to find that document.

Another limitation is that all labelling has to be done manually for each entity, and all entities have to be manually associated in the database, which is extremely resource-demanding and inefficient.  In order to provide a new level of data mining capabilities for our future customers, we are now exploring ways to apply natural language processing (NLP) technologies in our project. Some of the key tasks that we are hoping to solve by implementing NLP models is to be able to automate domain-specific entity recognition – identifying biotech companies, drugs, diseases, therapeutic modalities etc. – out of vast amounts of mostly unstructured data, and grouping them by a number of requirements.

In time this means we will be able to extract relations between the entities and build knowledge graphs; a key component in being able to understand the pharmaceutical R&D market and derive macro- and micro-trends and business insights for the user.

Challenges to overcome

Integrating NLP models into the existing project is a tricky endeavour, and we will need to expand our expertise substantially to achieve this goal. We have unique domain-specific expertise in the life sciences industry and biotech market, a large corpus of quality data to train models. We are now exploring our potential customers’ needs to formulate use cases, and pipeline requirements for the NLP system and its output.

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The post Using NLP to Build a Market Intelligence Platform for the Biotech Industry appeared first on Digital Science.

Disease types associated with NAFLD present a tremendous research and development challenge for life sciences organizations and also a great opportunity for them to create value and innovation. The NASH market alone was valued at $1,179 million in 2017, and is expected to reach $21,478 million by 2025, growing at a CAGR of 58.4% from 2021 to 2025.

The graphic shows some examples of insights in non-alcoholic fatty liver disease (NAFLD) and its common disease type NASH (non-alcoholic steatohepatitis) extracted from our Dimensions platform. Digital Science offers the data capabilities to help information services leaders, discovery scientists, CI analysts, knowledge management, partnering and business development professionals uncover insights and expertise in disease-related topics and key indication areas.

To find out more, or to arrange a demo, get in touch.

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The post Pinpointing Global Expertise in Non-Alcoholic Fatty Liver Disease (NAFLD) appeared first on Digital Science.

Our report finds new evidence that highlights differences in the primary audiences engaging with malaria and Alzheimer’s disease research, respectively.

The study, which was conducted by the consultancy team at Digital Science, concluded that policymakers make up the primary community engaging with Malaria research, whilst practitioners and mainstream news outlets were most prominent for Alzheimer’s disease.

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The post Unraveling the Engagement and Impact of Academic Research appeared first on Digital Science.