Poor target selection is the leading reason for clinical trial failure, with approximately 50% of trials falling short due to lack of efficacy. Most efficacy related failures are attributable to poor disease-target linkage. To navigate this, drug discovery teams need to have an extensive understanding of the underlying disease biology, target safety profile, availability of biomarkers, and more.
Balancing these considerations is far from simple. The exponential growth of biomedical data makes target prioritization and selection deeply complex. Modern drug discovery scientists urgently need effective tools that help them manage the data overload, reduce bias and separate signal from noise.
Build foundational understanding of disease biology to design targeted therapies
- Explore molecular and cellular processes associated with the disease to establish the critical points where interventions can be targeted.
- Analyze pathways and signalling networks to focus on targets that play a pathogenic role in the disease process.
Identify & prioritize novel and validated targets to focus resources
- Causaly reads the scientific data within seconds to identify a list of potential targets. Deeply explore the supporting evidence for each target-disease association, including directional effects and contradictory findings.
- Easily identify novel targets by focusing on emerging findings, considering targets with little validation, or exploring targets yet to be directly associated with the disease of interest.
- With multiple criteria for prioritization, rapidly filter for validated targets in Causaly by assessing the strength of disease-target linkage, genetic evidence, validation in animal models, human relevance, druggability, and more. Tailor your selection process to match your specific needs.
Stay up to date with the latest and most comprehensive data
- Stay on top of your research area with customizable alerts that notify you the moment new evidence appears.
- Have confidence that you are not missing any relevant data with our comprehensive target ontology. Dive deep into specific target classes such as enzymes, receptors, membrane transporters to uncover new possibilities to focus your discovery programs.
Build foundational understanding of disease biology to design targeted therapies
- Explore molecular and cellular processes associated with the disease to establish the critical points where interventions can be targeted.
- Analyze pathways and signalling networks to focus on targets that play a pathogenic role in the disease process.
Accelerate Your Discovery Programs
Gain up to 90% in research productivity and focus resources on validated targets or explore more novel directions.
Terminate Non-Promising Projects Early
Establish the strength of the underlying disease biology, identify safety red-flags, and look for competitive signals at the inception of discovery projects.
Make More and Bigger Scientific Bets
Increase the number of innovative bets through open-ended, exploratory questions that allow you to consider adjacencies and increase chances of novel discoveries – without running a single experiment.
What our clients say
Target Selection Resources
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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.
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.
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.
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.
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 […]
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).