Tasks

• Provide expert services in pharmaceutical R&D for developing deep learning models for drug discovery, including predictions of organic compound solubility and interactions with proteins and DNA.

Challenges

Solutions

• Specialized service combining ML expertise with chemistry and drug discovery.
• Development and deployment of deep learning models to predict organic compound solubility and interactions with proteins/DNA.
• Utilization of extensive datasets and advanced algorithms for accurate insights.
• Seamless integration with existing R&D workflows to ensure minimal disruption.
• Continuous support and model updates for adapting to new data and research needs.

Outcomes

• Achieve a 15-20% reduction in research time and costs, accelerating the drug discovery process and improving the success rate of potential drug candidates.

Technologies Used

Tasks

• Develop an AI-driven ecosystem to enhance HPLC capabilities in pharmaceutical R&D, enabling peak prediction, automated integration, and compliance with regulatory standards and GMP.

Challenges

Solutions

• Develop an AI-powered ecosystem to enhance HPLC capabilities.
• Predict peak retention times and integrate peaks automatically per GMP and regulatory standards.
• Utilize ML models trained on historical chromatographic data for real-time predictions and integrations.
• Include a user-friendly interface for parameter monitoring and compliance.
• Seamlessly integrate with existing LIMS for streamlined data management and record-keeping.

Outcomes

• Achieve a 20-25% increase in operational efficiency, reducing manual workload and enhancing the accuracy of chromatographic analyses.

Technologies Used

Tasks

• Implement AI/ML solutions to forecast material consumption and sales, optimizing procurement, supply chain, and production quantities in the Pharma industry.

Challenges

Solutions

• Implement ARIMA and LSTM models for time series forecasting of material usage and sales.
• Integrate models with ERP and CRM systems for seamless data flow.
• Establish a robust data governance framework.
• Enable continuous model retraining to adapt to market changes.
• Enhance procurement planning and production schedules to reduce material waste.

Outcomes

• Achieve a 10-15% reduction in material waste, improving overall efficiency and reducing costs.

Technologies Used

Tasks

• Audit current IT architecture.
• Analyze reporting processes across departments.
• Recommend solutions for analytic function optimization.
• Gather requirements for reporting and analytics.
• Define master data systems for each category.
• Propose new IT architecture for system integration.
• Outline development plan for the new system.

Challenges

The main challenge was to develop a comprehensive strategy to automate data collection and reporting processes, integrate various data systems, and improve data-driven decision-making capabilities for Servier Ukraine.

Solutions

• Established unified reporting standards for all data categories.
• Created a single Master Data system per category.
• Enabled automated data exchange across systems to reduce manual tasks.
• Built an automated reporting system for recurring reports.
• Recommended MS Power BI as the primary reporting tool.

Outcomes

• Automated data collection and reporting.
• Enhanced data integration and minimized silos.
• Strengthened decision-making with predictive analytics.
• Reduced manual tasks, boosting data efficiency.
• Improved user experience with a modern interface.

Technologies Used