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.

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.

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

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.

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.

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.

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.

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.  

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.  

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.

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.

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.

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.

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.

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 […]

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).

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.

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 […]

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.