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Andrew TaylorJuly 13, 2023

Causaly Raises $60 Million in Series B Funding to Catalyze AI-powered Preclinical Discovery

The financing, led by ICONIQ Growth, enables Causaly to accelerate innovation and drive increased adoption of its category-leading AI platform to modernize scientific research.

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

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.

Elizabeth Bolitho

February 29, 2024

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The Official 2024 Launch of Causaly Copilot

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

Yiannis Kiachopoulos

February 27, 2024

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Aiding Drug Repurposing Investigations with AI

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.

Elizabeth Bolitho

February 23, 2024

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Target Selection

Elizabeth Bolitho • February 29, 2024

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

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.

Elizabeth Bolitho • February 13, 2024

Leveraging AI for Scientific Knowledge Extraction

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.

Elizabeth Bolitho • January 30, 2024

AI-Powered Drug Discovery: Identifying Safety Red Flags

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.  

Elizabeth Bolitho • January 26, 2024

Search by Target Class: Enzyme Targets for Celiac Disease

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.  

Elizabeth Bolitho • January 19, 2024

Navigating the Biomedical Literature: Insights vs. Papers

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.

Elizabeth Bolitho • January 9, 2024

Eliminating Bias with Human-Centric AI

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.

Elizabeth Bolitho • December 19, 2023

Accelerate Target Discovery with the Help of AI

Drug discovery is complex, with a high level of uncertainty full of critical decisions with significant time and cost implications. With 90% of drugs failing in clinical trials, there is an urgent need to accelerate the pathway from disease biology to candidate selection. Causaly’s human-centric AI empowers users to make better research decisions by accelerating […]

Elizabeth Bolitho • December 15, 2023

Exploring Biochemical Pathways with Human-Centric AI

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).

Elizabeth Bolitho • December 12, 2023

Target Identification and Prioritization: Ovarian Cancer

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.

Elizabeth Bolitho • November 27, 2023

Target Identification in Prostate Cancer

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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Elizabeth Bolitho • February 29, 2024

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

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.

Yiannis Kiachopoulos • February 27, 2024

The Official 2024 Launch of Causaly Copilot

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

Elizabeth Bolitho • February 23, 2024

Aiding Drug Repurposing Investigations with AI

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.

Elizabeth Bolitho • February 13, 2024

Leveraging AI for Scientific Knowledge Extraction

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.

Elizabeth Bolitho • February 9, 2024

Comparison of Safety Biomarkers for Chemotherapeutics

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.

Elizabeth Bolitho • February 6, 2024

Exploring the Disease Pathophysiology of Rheumatoid Arthritis

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.

Elizabeth Bolitho • January 30, 2024

AI-Powered Drug Discovery: Identifying Safety Red Flags

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.  

Elizabeth Bolitho • January 26, 2024

Search by Target Class: Enzyme Targets for Celiac Disease

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.  

Elizabeth Bolitho • January 24, 2024

Challenging the Status Quo: A Biomarker Use Case

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.

Elizabeth Bolitho • January 19, 2024

Navigating the Biomedical Literature: Insights vs. Papers

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.

Elizabeth Bolitho • January 12, 2024

Uncovering the Mechanism of Action of Evolocumab against PAD

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.

Elizabeth Bolitho • January 9, 2024

Eliminating Bias with Human-Centric AI

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.

Elizabeth Bolitho • January 5, 2024

Target-Based Drug Discovery Begins with Understanding Disease Pathophysiology

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.

Elizabeth Bolitho • December 19, 2023

Accelerate Target Discovery with the Help of AI

Drug discovery is complex, with a high level of uncertainty full of critical decisions with significant time and cost implications. With 90% of drugs failing in clinical trials, there is an urgent need to accelerate the pathway from disease biology to candidate selection. Causaly’s human-centric AI empowers users to make better research decisions by accelerating […]

Elizabeth Bolitho • December 15, 2023

Exploring Biochemical Pathways with Human-Centric AI

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).

Elizabeth Bolitho • December 12, 2023

Target Identification and Prioritization: Ovarian Cancer

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.

Elizabeth Bolitho • December 7, 2023

Biomarkers of Treatment Response in Liver Cancer

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. Here, we used Causaly to identify and prioritize biomarkers of sorafenib […]

Elizabeth Bolitho • December 4, 2023

Expedite Biomarker Discovery with Human-Centric AI

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.

Elizabeth Bolitho • November 27, 2023

Target Identification in Prostate Cancer

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.

Elizabeth Bolitho • November 24, 2023

Biomarker Identification and Prioritization: GERD

Biomarker-based strategies have shown to improve drug program success. However, biomarker discovery is marked by the intricate challenge of extracting relevant molecular signatures from extensive biological data. In this use case, we demonstrate how Causaly can be used to understand the biomarker landscape in GERD, a common digestive disorder characterized by chronic acid reflux.