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Discover the latest reports, data sheets, articles, infographics, and videos on biomedical R&D and AI.

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ProQR Uses Causaly to Support Target Identification For Axiomer™ Platform

January 28, 2025

ProQR faced challenges with bandwidth, aggressive targets, and the overwhelming growth of biomedical literature. Causaly's AI platform accelerated ProQR’s R&D by enabling faster review of publications and allowing their team to make more informed decisions. Read the case study below to learn how ProQR met their 2024 target ID goal by Q3 and achieved 5x productivity compared to using PubMed.

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Unraveling Mechanisms of Disease Pathogenesis with AI

Anna Tzani
March 6, 2024

AI-supported investigations into disease mechanisms offer a transformative approach to understand complex pathologies. By leveraging AI to unlock disease understanding, researchers can identify novel biomarkers and therapeutic targets with unprecedented precision. This not only accelerates the journey from lab to market but also enhances the efficacy and safety of new treatments.

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The Official 2024 Launch of Causaly Copilot

Yiannis Kiachopoulos
February 27, 2024

How we successfully went from experimentation to scale to build and launch a production-grade GenAI Copilot made for scientists

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AI-Powered Drug Discovery: Identifying Safety Red Flags

Anna Tzani
January 30, 2024

Unmanageable toxicity accounts for 30% of clinical drug development failures and can cause severe side effects and potential harm to patients. Download our report to see how AI-powered drug discovery can help mitigate late-stage clinical failures and market withdrawals.

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Browse topics of interest

Disease Pathophysiology
Target selection
Safety
Biomarkers
causaly

Unraveling Mechanisms of Disease Pathogenesis with AI

Anna Tzani
March 6, 2024

AI-supported investigations into disease mechanisms offer a transformative approach to understand complex pathologies. By leveraging AI to unlock disease understanding, researchers can identify novel biomarkers and therapeutic targets with unprecedented precision. This not only accelerates the journey from lab to market but also enhances the efficacy and safety of new treatments.

Read more

Leveraging AI for Scientific Knowledge Extraction

Anna Tzani
February 13, 2024

AI is transforming the analysis of extensive biomedical data, allowing pharma companies to expedite R&D processes, and cut costs. By reaching conclusions quicker, the inclusion of AI in drug development pipelines can inform decision-making, enabling the prioritization of more promising research avenues.

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Exploring the Disease Pathophysiology of Rheumatoid Arthritis

Anna Tzani
February 6, 2024

Understanding the pathophysiology of a disease is pivotal in comprehending its cause and progression and facilitating the identification of novel targets for therapeutic intervention. Data-driven strategies are essential in navigating this complexity, facilitating a deeper understanding of disease pathophysiology, which can be leveraged to develop more effective treatments.

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Search by Target Class: Enzyme Targets for Celiac Disease

Anna Tzani
January 26, 2024

The strategic prioritization of drug targets by target class can be used to streamline discovery, enabling efficient resource allocation and time-savings in early drug development, as well as a competitive edge given the variable success rates of different target classes. Prioritization of specific target classes may therefore enable investment optimization in preclinical research.

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Navigating the Biomedical Literature: Insights vs. Papers

Anna Tzani
January 19, 2024

With 2 publications added to PubMed every minute, identifying therapeutic targets with traditional keyword searching is time-consuming, causes reading fatigue and is subject to bias. By machine-reading the literature, Causaly manages this data overload, extracting scientific insights rather than papers, to enable the exploration of more novel avenues.

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Uncovering the Mechanism of Action of Evolocumab against PAD

Anna Tzani
January 12, 2024

Deciphering a drug’s MoA is crucial for making informed decisions in drug development, paving the way for the development of more targeted and effective therapeutic solutions. AI can revolutionize this process by facilitating knowledge discovery without bias, unveiling hidden drug-disease interactions.

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ProQR Uses Causaly to Support Target Identification For Axiomer™ Platform

January 28, 2025

ProQR faced challenges with bandwidth, aggressive targets, and the overwhelming growth of biomedical literature. Causaly's AI platform accelerated ProQR’s R&D by enabling faster review of publications and allowing their team to make more informed decisions. Read the case study below to learn how ProQR met their 2024 target ID goal by Q3 and achieved 5x productivity compared to using PubMed.

Read more

Enhance Your Search Sensitivity with AI: Off-Target Effects of BRAF

Anna Tzani
February 29, 2024

In contrast to conventional keyword searching techniques, Causaly’s AI can sift through and remove irrelevant data from biomedical searches, offering a more thorough and accurate understanding of entire research landscapes. This not only streamlines knowledge acquisition but ensures accuracy and precision in navigating the biomedical literature.

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Leveraging AI for Scientific Knowledge Extraction

Anna Tzani
February 13, 2024

AI is transforming the analysis of extensive biomedical data, allowing pharma companies to expedite R&D processes, and cut costs. By reaching conclusions quicker, the inclusion of AI in drug development pipelines can inform decision-making, enabling the prioritization of more promising research avenues.

Read more

Search by Target Class: Enzyme Targets for Celiac Disease

Anna Tzani
January 26, 2024

The strategic prioritization of drug targets by target class can be used to streamline discovery, enabling efficient resource allocation and time-savings in early drug development, as well as a competitive edge given the variable success rates of different target classes. Prioritization of specific target classes may therefore enable investment optimization in preclinical research.

Read more

Navigating the Biomedical Literature: Insights vs. Papers

Anna Tzani
January 19, 2024

With 2 publications added to PubMed every minute, identifying therapeutic targets with traditional keyword searching is time-consuming, causes reading fatigue and is subject to bias. By machine-reading the literature, Causaly manages this data overload, extracting scientific insights rather than papers, to enable the exploration of more novel avenues.

Read more

Eliminating Bias with Human-Centric AI

Anna Tzani
January 9, 2024

Traditional keyword searching can miss crucial information, skewing representations of research landscapes before reading even begins. Instead, Causaly’s human-centric AI can extract all targets for a given disease without bias, uncovering scientific insights, rather than just papers.

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Aiding Drug Repurposing Investigations with AI

Anna Tzani
February 23, 2024

Drug repurposing offers a cost-effective and efficient pathway to discovery new therapeutic uses for existing treatments. AI can advance this process by rapidly analyzing large-scale biomedical data and scientific texts to identify drug-disease relationships, opening up avenues for treatments in unexplored indications.

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Comparison of Safety Biomarkers for Chemotherapeutics

Anna Tzani
February 9, 2024

The identification and utilization of safety biomarkers plays a key role in mitigating toxicity risks and reducing costs in drug development, thereby accelerating the delivery of safe and effective drugs to patients. AI can streamline the identification of relevant biomarkers from the ever-growing biomedical literature, offering insights into drug resistance and toxicity.

Read more

AI-Powered Drug Discovery: Identifying Safety Red Flags

Anna Tzani
January 30, 2024

Unmanageable toxicity accounts for 30% of clinical drug development failures and can cause severe side effects and potential harm to patients. Download our report to see how AI-powered drug discovery can help mitigate late-stage clinical failures and market withdrawals.

Read more

Comparison of Safety Biomarkers for Chemotherapeutics

Anna Tzani
February 9, 2024

The identification and utilization of safety biomarkers plays a key role in mitigating toxicity risks and reducing costs in drug development, thereby accelerating the delivery of safe and effective drugs to patients. AI can streamline the identification of relevant biomarkers from the ever-growing biomedical literature, offering insights into drug resistance and toxicity.

Read more

Challenging the Status Quo: A Biomarker Use Case

Anna Tzani
January 24, 2024

Traditional keyword searching is highly inefficient, subject to bias and is not always comprehensive, providing limited potential for knowledge discovery and hypothesis generation. This selective approach introduces a bias towards familiar areas of expertise, which can lead to missed opportunities for novel insights and innovations. This is where AI comes in.

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Biomarkers of Treatment Response in Liver Cancer

Anna Tzani
December 7, 2023

Biomarkers serve as objective measures of treatment response to guide patients towards the most appropriate therapies. Yet, in the era of big data, pinpointing promising biomarkers remains a challenging endeavor. AI is revolutionizing translational medicine by improving the efficiency and accuracy of biomarker identification.

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Expedite Biomarker Discovery with Human-Centric AI

Anna Tzani
December 4, 2023

Biomarkers are pivotal throughout drug development, from discovery to market, playing key roles in unravelling drug mechanisms, providing prognostic insights and assessing treatment efficacy. Despite the clinical promise, biomarker development is challenging. There are substantial obstacles, from disease heterogeneity and rigorous validation requirements to the inability to extract meaningful biomarker insights from extensive biological data.

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How Gen AI is Improving Productivity Across Life Sciences R&D Teams

Yiannis Kiachopoulos
October 29, 2024

The life sciences industry faces the challenge of managing vast amounts of data and coordinating diverse teams to efficiently translate scientific insights into tangible outcomes. Traditional research methods are time-consuming and limit productivity. Generative AI offers a transformative solution by accelerating hypothesis generation, enhancing data analysis, improving decision-making, and facilitating collaboration.

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The Official 2024 Launch of Causaly Copilot

Yiannis Kiachopoulos
February 27, 2024

How we successfully went from experimentation to scale to build and launch a production-grade GenAI Copilot made for scientists

Read more
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