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.

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.

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.  

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.

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.

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.

With almost half a million papers on obesity in PubMed, and 2 papers added per minute, identifying promising therapeutic targets is time-consuming, subject to bias and is not always comprehensive. Causaly extracts insights rather than papers, uncovering over 5,000 targets for obesity in seconds.

The rising prevalence of T1D demands new treatments. The identification and understanding of targets for T1D for developing effective drugs and alleviating patient burdens. AI can streamline this process by dramatically reducing reading time, minimizing bias and uncovering hidden target-disease insights, enabling the exploration of more promising avenues.

No cure exists for hemophilia, necessitating frequent, tailored interventions. The imperative for improved treatments hinges on identifying relevant biological targets. Utilizing Causaly, we explored potential targets for hemophilia investigated in animal models.

Early-stage triple-negative breast cancer (TNBC) primarily relies on surgery and chemotherapy, yet its response is often suboptimal compared to other breast cancer subtypes. The development of targeted therapies is crucial to improve prognosis, enhance treatment efficacy and mitigate drug resistance. Identifying novel therapeutic pathways holds the potential to transform TNBC treatment.

Psoriatic arthritis significantly impairs joint health and overall patient well-being. The intricacy of this disease is underscored by its diverse molecular mechanisms, which add layers of complexity to understanding and managing psoriatic arthritis. Utilizing Causaly, we assessed the potential of enzymes targets for psoriatic arthritis.

Epilepsy is a neurological condition marked by spontaneous seizures. Challenges in translating experimental epilepsy studies to clinical use include model validation, efficacy metrics, and long-term gene therapy safety concerns.¹ Nevertheless, identifying targets using genomic data offers crucial insights into epilepsy’s genetic foundation.

Myocardial infarction treatments primarily focus on restoring blood flow and mitigating immediate threat, but they often fail to address the irreversible cell damage caused during a heart attack. Understanding cell death mechanisms, particularly ferroptosis, offers a promising avenue for developing more comprehensive treatments. Here, miR331 and SIRT1 were identified as targets affecting ferroptosis in myocardial […]

Despite available treatments for blood cancer, achieving sufficient dosage at the tumor site for therapeutic efficacy is difficult, underscoring the crucial need for improved targeted therapies. Here we used Causaly to identify and prioritize potential targets for Diffuse Large B Cell Lymphoma (DLBCL) – the most prevalent Non-Hodgkin Lymphoma.     

Amyotrophic Lateral Sclerosis (ALS) is a neuromuscular disease, affecting 12 individuals per 100,000 in the U. S. ALS is incurable, thus, the discovery of novel targets is paramount for improving treatment options. Using Causaly, we identified and assessed the potential of RIPK1 as a target for ALS.