CAS BioFinder

CAS BioFinder® is a comprehensive drug discovery platform designed to accelerate every stage of the pharmaceutical research pipeline. Built for discovery biology and medicinal chemistry teams, it combines expert-curated life sciences data, AI-enhanced predictive technology, and deep cross-disciplinary insights to help researchers prioritize the targets and drug candidates most likely to succeed — more quickly and cost-effectively than traditional approaches.

Powered by standardized biological and chemical data anchored in CAS REGISTRY®, CAS BioFinder connects structure-activity relationships (SAR), sequences, toxicity profiles, biomarkers, pathways, and more sourced from thousands of the world's most prestigious journals and patents. The platform brings biology and chemistry data together through ontologies, enabling researchers to uncover meaningful relationships between ligands, proteins, diseases, and pathways in a single integrated environment.

Target Identification and Validation

• Conduct sequence searches to identify white space and potential safety risks early in the discovery process.
• View related biomarkers to explore disease pathogenesis and protein pathways, enabling teams to prioritize the most relevant targets.
• Compare modalities — including small molecules, biologics, ADCs, and peptides — using evidence-based filters and curated summaries to reduce uncertainty before committing resources.
• Access detailed information on ligands, diseases, and proteins to validate target proteins faster and with greater confidence.

Medicinal Chemistry Capabilities

• Connect target protein-disease combinations to known ligands and compare options based on druglikeness, commercial availability, and other key parameters.
• Explore heat maps to identify R-group modifications that impact ligand potency against a specific protein.
• Predict the bioactivity of novel molecules to estimate selectivity and identify potential safety risks before advancing candidates.
• Leverage a curated database of structure-activity relationships and ligand-target-disease combinations to identify ligand sets faster and with greater accuracy.

AI-Powered Research with CAS Newton℠

• Ask precise scientific questions in your preferred language using the CAS Newton deep research AI assistant, which is custom-built and trained on validated CAS BioFinder data.
• Receive detailed, well-cited responses and follow links directly to underlying data sources and analytical visualizations.
• Establish a landscape view of a research area to create a starting point for future investigations.
• Facilitate cross-functional collaboration by quickly building a shared baseline of knowledge across scientific disciplines.
• Test theories early through rapid literature searches and iterative follow-up questioning.

Pipeline Prioritization and Risk Reduction

• Determine target viability quickly by integrating sequence searches, disease association data, and pathway information in one platform.
• Identify high-potential leads faster using AI-driven predictions and curated structure-activity relationships.
• Reduce costly dead ends by uncovering off-target effects and safety risks earlier in the discovery process.
• Enable efficient, timely, and cost-effective drug discovery by combining comprehensive data with cutting-edge predictive tools.

Data Foundation

• Access life sciences data standardized by expert scientists and connected through ontologies for deep cross-field relationships.
• Search SAR data, biological sequences, toxicity profiles, biomarkers, and pathway information from thousands of leading journals and patents.
• Benefit from custom-built authority constructs for proteins, genes, and organisms anchored in CAS REGISTRY® to ensure data consistency and reliability.
• Work with data that is current, relevant, and deeply connected across biology and chemistry disciplines.

CAS BioFinder is available with a free trial and integrates with other CAS solutions to support the full drug discovery team workflow. The platform has been cited in peer-reviewed literature, including Angewandte Chemie International Edition, for its ability to enable rapid in silico prediction of pharmacological activity and bioactivity by modeling protein–ligand interactions based on data extracted from the scientific literature.