BenchSci Extends Impact on Drug Discovery With Addition of AI-Assisted Animal Model Selection

BenchSci’s suite of AI-powered applications that provide scientists with insights to plan, procure for, and execute experiments more effectively is now applicable to more preclinical R&D experiments with the addition of animal models. 
Using animal models to validate studies is an essential step in preclinical research, as they enable scientists to experiment under controlled situations that mimic the biological conditions of human diseases. BenchSci enables scientists to make more informed and precise animal model selections by providing evidence of successful utilization in experiments similar to their own.
“We’re proud to be introducing animal models to the BenchSci platform as we continue to focus on helping scientists accelerate preclinical research,” says Casandra Mangroo, BenchSci’s SVP of Product and Science. “Relevant insights from the published literature are surfaced in one place to help scientists understand how other scientists have used animal models and designed successful experiments. Scientists will have the information they need to select the most appropriate animal model to advance their experiments.” 
For scientists, finding and selecting appropriate animal models can be a time-consuming process. The published literature may contain inconsistent abbreviations and aliases, and vendor catalogs do not provide enough information on the experimental context for specific diseases or systems.
A scientist will often consult and manually read multiple pieces of literature to find an animal model that their peers in the scientific community have previously utilized in an experiment similar to their own and understand how to reproduce successful results. This can be time-consuming and costly. 
BenchSci’s proprietary machine learning models extract animal usage data from the published literature, connecting it to product data. Advanced bioinformatics and ontologies link animal models to the experiments they have been used in across the published literature.
Scientists can apply their experimental context to filter BenchSci’s animal models database to review only relevant experimental insights. This saves scientists hours of precious research time while enabling them to make more informed product selection decisions and increase their likelihood of experimental success. 
BenchSci’s animal model database includes:
The addition of animal models to the platform is one more way that BenchSci is becoming a complete resource for all aspects of decision-making in experimental design.  
For more BenchSci updates, visit our news page. 
About BenchSci
BenchSci’s vision is to bring medicine to patients 50% faster by 2025. We’re doing this by empowering scientists with the world’s most advanced biomedical artificial intelligence to run more successful experiments. Backed by F-Prime, Gradient Ventures (Google’s AI fund), and Inovia Capital, our platform accelerates science at 15 top-20 pharmaceutical companies and over 4,300 leading research centers worldwide. We’re a CIX Top 10 Growth company, certified Great Place to Work®, and top-ranked company on Glassdoor. Learn more at www.benchsci.com.
For more information, please contact Marie Cook at mcook@benchsci.com.
Source: BenchSci