Case Study

BenchSci is a leading biomedical AI platform that accelerates preclinical research by transforming the way pharmaceutical and life sciences organizations access and apply scientific knowledge. At the heart of its offering is ASCEND, a machine learning-powered platform that decodes millions of scientific papers to surface actionable, evidence-based insights—helping R&D teams reduce experimental inefficiencies and make smarter decisions, faster.

BenchSci primarily supports mid-sized to large pharmaceutical companies focused on minimizing trial-and-error during target validation, reagent selection, and early-stage experimental design. With ASCEND, researchers can eliminate time-consuming manual reviews of literature and improve the success rate of preclinical programs.

Challenges

Pharmaceutical R&D teams faced several roadblocks that hindered efficiency and success:

• Excessive time spent manually reviewing thousands of articles to select antibodies, reagents, and biomarkers

• Experimental failures caused by poor reagent reproducibility or inappropriate selections

• Lack of visibility into the latest peer-reviewed studies and competitive research trends

• Slow, fragmented workflows in target validation and exploratory research

Top Remotely worked with BenchSci clients to streamline discovery processes and embed AI-driven insights into daily research operations.

Objectives

The key objectives of implementing BenchSci’s ASCEND platform included:

• Drastically reduce reagent search time using AI-filtered, evidence-backed recommendations

• Minimize failure rates in preclinical experiments by improving reagent reliability

• Accelerate early go/no-go decisions through contextual insights on targets and models

• Foster transparent, cross-functional collaboration within scientific teams

• Integrate internal and external scientific knowledge for improved traceability

Solutions

Top Remotely guided the deployment and adoption of BenchSci ASCEND, unlocking key platform capabilities:

• AI-Powered Search: Surfacing evidence-backed insights from over 15 million peer-reviewed papers, including reagent usage in similar biological contexts.
  
• Ontology-Driven Categorization: Structuring biological terms, experimental conditions, and outcomes to allow precise filtering and comparison.
  
• Custom Data Enrichment: Integrating internal experimental data with public datasets for better decision-making.
  
• Cross-Team Collaboration Tools: Allowing scientists across departments to share annotations, reagent performance data, and experiment metadata.
  
• Competitive Landscape Scanning: Identifying related studies, models, and techniques used by peers and competitors.

Deployment Process

Top Remotely facilitated the end-to-end implementation in a scalable, secure, and scientifically aligned manner:

• Integration with Internal Systems: Connected to customers’ internal knowledge bases, ELNs (Electronic Lab Notebooks), and LIMS platforms.
  
• Data Harmonization: Mapped diverse biological data sources into a unified research knowledge graph.
  
• User Onboarding: Provided training sessions and scientific success teams to guide usage across therapeutic areas.
  
• Feedback Loops: Continuous algorithm improvement based on user interaction and outcome tracking.

Results

The adoption of BenchSci’s ASCEND platform brought measurable improvements in research speed, accuracy, and collaboration:

• Over 90% reduction in time spent searching for reagents, saving thousands of research hours per year

• 40% decrease in failed preclinical experiments due to reagent mismatches or poor reproducibility

• 2.5x acceleration in target validation decision-making, reducing delays in drug development pipelines

• Improved cross-department transparency, with shared annotations and reagent libraries improving consistency across scientific teams

The platform not only enhanced R&D workflows but also helped companies avoid costly, late-stage experimental failures.

Technology Stack

• AI/ML Frameworks: TensorFlow, BERT, spaCy for natural language understanding
  
• Backend: Python (Django), GraphQL
  
• Frontend: Vue.js
  
• Data Infrastructure: AWS (RDS, S3, Lambda), Neo4j (knowledge graph database)
  
• Security & Compliance: SOC 2 Type II certified, HIPAA-compliant architecture
  
• Integrations: APIs for ELN, LIMS, and internal data warehouses

BenchSci exemplifies how AI can radically transform pharmaceutical research by automating complex information discovery, reducing experimental risks, and enabling faster scientific breakthroughs. Through ASCEND, preclinical teams gain unprecedented visibility into both published and internal research, enabling informed, data-backed decisions from day one.

With Top Remotely’s support, life sciences organizations deployed BenchSci effectively—bridging fragmented data silos, empowering researchers, and turning information overload into a competitive advantage. For pharma teams seeking faster innovation without compromising scientific integrity, BenchSci offers the future of intelligent preclinical research.