Blog

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

Featured blogs

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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

Target-Based Drug Discovery Begins with Understanding Disease Pathophysiology

Anna Tzani
January 5, 2024

Target-based drug discovery begins with understanding the physiological basis of the disease, and the subsequent abnormal or deviant pathways and targets responsible for the disease phenotype.¹ A foundational understanding of disease pathophysiology therefore serves as a roadmap for drug development success.

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Exploring Biochemical Pathways with Human-Centric AI

Anna Tzani
December 15, 2023

In drug development, understanding biochemical pathways is essential for elucidating molecular mechanisms in cells and tissues, thereby informing targeted therapeutic strategies. In this example, we used Causaly to identify and explore biochemical pathways affected by targets for acute kidney injury (AKI).

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Exploring Biochemical Pathways with Human-Centric AI

Anna Tzani
December 15, 2023

In drug development, understanding biochemical pathways is essential for elucidating molecular mechanisms in cells and tissues, thereby informing targeted therapeutic strategies. In this example, we used Causaly to identify and explore biochemical pathways affected by targets for acute kidney injury (AKI).

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Target Identification and Prioritization: Ovarian Cancer

Anna Tzani
December 12, 2023

Human-centric AI can be used to accelerate target identification and prioritization, extracting evidence for the most relevant target-disease relationships. Importantly, our AI provides a view of all scientific evidence, ensuring 100% traceability or original sources for researchers to apply their own judgement.

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Target Identification in Prostate Cancer

Anna Tzani
November 27, 2023

Limited therapeutic efficacy and drug resistance in advanced-stage prostate cancer contributes to poor outcomes, highlighting the need for targeted therapies with alternative mechanisms of action. Here, we leveraged human-centric AI to accelerate target identification for prostrate adenocarcinoma.

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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.

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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.

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.

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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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