Case Studies
Case Study 1: Unlocking Legacy Data for AI-Driven Insights
Client: Top 10 Large Pharma Company
Challenge: Legacy unstructured data from ELNs and CRO reports were inaccessible for ML/AI applications.
Solution: ChemAI utilized Large Language Models (LLMs) combined with Cheminformatics tools to extract and structure chemical data.
Outcome:
– Enabled AI applications to automate chemistry processes.
– Extracted key entities such as chemicals, mixtures, equipment, and reaction steps.
– Improved data accessibility, supporting faster decision-making in R&D.
Case Study 2: AI-Driven Process Chemistry Optimization
Client: Mid-Sized Pharma
Challenge: The company aimed to accelerate process chemistry results while reducing the number of experiments required.
Solution: ChemAI implemented AI-driven methodologies across 11 campaigns over 12 months.
Outcome:
– Prevented wasted resources in over 15% of cases.
– Achieved superior results in 6 campaigns.
– Reduced experiments in two campaigns from 50 to just 15.
– Matched traditional DOE performance in three campaigns.
– Identified flawed data early, preventing unnecessary experiments.
Case Study 3: Predictive Foundation Model for Amide Couplings
Client: Leading Contract Development and Manufacturing Organization (CDMO)
Challenge: The company needed a highly predictive model for amide coupling reactions with minimal experimental data.
Solution: ChemAI developed an AI-powered foundation model using active learning and automated synthesis workflows.
Outcome:
– Outperformed existing literature and ELN data.
– Automated first-plate reaction selection and synthesis.
– Integrated UV and MS peak assessments for improved model performance.
Case Study 4: AI-Optimized Formulation for High-Value Skincare
Client: Top 30 Cosmetics Company by Market Capitalization
Challenge: The company needed to maximize the hydrating power of a new skin cream formulation from an unknown starting point while ensuring environmental sustainability.
Solution: ChemAI’s SmartChemistry AI was used to analyze and optimize the formulation with active molecules.
Outcome:
– AI-assisted selection of ingredient proportions.
– Created a high-performance formulation with minimal trial and error.
– Enabled rapid product development and market readiness.
“Very difficult to design this formulation without SmartChemistry AI” – Data Scientist.
Case Study 5: Rapid Robotic Reaction Optimization
Client: Subsidiary of the #1 Cosmetics Brand
Challenge: The company sought self-optimizing chemical reactions to enhance active ingredient synthesis efficiency.
Solution: ChemAI partnered with three organizations to implement a continuous closed-loop system for reaction prediction, synthesis, and analysis.
Outcome:
– Optimized seven reaction parameters, including catalyst choice, solvent selection, and temperature.
– Increased efficiency in active ingredient synthesis.
– Enabled real-time reaction optimization through AI-driven predictions.