Life Science Database

At NUVISAN, we have a unique 3 million compound library (REF 1, 2) and more than a billion associated experimental data points that cover biological activity on a wide range of target classes, physchem, ADMET end points, and more. By leveraging the legacy data, we provide informed decisions through data mining and custom predictive tools for various stages of the drug discovery pipeline.

During all stages of a drug discovery process, using the information in the LSDB, we can provide the following:

Hit Identification Stage

• Complement high-throughput screening programs by evaluating hits with respect to favorable properties—experimentally measured or predicted based on the NUVISAN machine learning models—and by assessing historical bioactivity profiles.
• Perform data mining and conduct virtual screening programs to identify hits and profile them against specific pharmacological targets.

Lead Optimization Stage

• Data-driven SAR and scaffold hopping for optimization of efficacy, selectivity, and pharmacokinetic properties.

Preclinical Development Stage

• Our prediction models trained on historical data are used for predicting ADME end points. For more details, see cheminformatics.

In addition, we offer custom services to curate and annotate your project data, ensuring a smooth transition of your project to NUVISAN. We use state-of-the-art tools and platforms for handling chemical structures and associated biological activity data to get maximum insight from them. NUVISAN offers data services with an aim of reducing the cost and time needed in each stage of drug development. Get in touch to learn more about our data services and how we can help your company accelerate the drug development process.

References

1. Kogej, T., et al., Big pharma screening collections: more of the same or unique libraries? The AstraZeneca–Bayer Pharma AG case. Drug Discov Today (2013)
2. Follmann, M., et al., An approach towards enhancement of a screening library: The Next Generation Library Initiative (NGLI) at Bayer—against all odds? Drug Discov Today (2019)