Resources

ProQR Uses Causaly to Support Target Identification For Axiomer™ Platform

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Introducing Pipeline Graph: Bring Competitive Intelligence to Research Scientists

Agentic AI: Unlocking deeper insights and accelerating scientific discovery

An explosion in biomedical data has the potential to empower life sciences R&D teams. But only if they’re able to make sense of that data, rather than being overwhelmed by it. Which is where Agentic AI comes in — a new technology that can independently conduct research, suggest testable hypotheses, and dramatically reduce time-to-discovery.

How AI is helping life sciences R&D move faster and with more certainty

How can R&D teams make the drug discovery process more efficient and effective? Traditional research methods are time consuming and error-prone, which is too risky given the current pace of innovation. Now with AI, researchers can uncover complex biological relationships, generate new hypotheses, and find the right answer that leads to a breakthrough.

Deep Research: Causaly's Agentic AI is Unlocking a New Dimension of Scientific Discovery

As research continues to evolve, scientists need more. That’s why we’re excited about the upcoming release of Causaly Deep Research—the only agentic AI on the market built to help life sciences organizations achieve unmatched precision and tackle the deepest challenges in biomedical research.

Causaly Discover: Transforming Scientific Research with Agentic AI

Today, we're excited to introduce the latest version of Discover, not just as another literature search tool, but as a revolutionary AI application redefining how life sciences companies approach scientific discovery.

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

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.