Catalysis Modeling
AI/ML in conjunction with QM to decrease avoidable experiment expenditure
Context
Multiple rounds of HTE to optimize reaction conditions delays drug development
Traditional High-Throughput Experimentation (HTE) for reaction optimization is resource-intensive and iterative, leading to escalating costs and prolonged timelines. AI-driven molecular catalysis is transforming this landscape by enabling predictive and prescriptive modeling capabilities.
At Aganitha, we leverage state-of-the-art AI/ML techniques to extract insights from HTE data, and entity-yield relationships, enabling data-driven reaction optimization. Our models integrate QM driven features, delivering reliable reaction yield predictions.
Our Solution
Customized model development to predict reaction yields using HTE
With our Analytics & AI/ML platform, we can help you identify the right combination of base, ligand, and solvent in the very first iteration of the 96-well plate experiments. Additionally, we can help you with:
🔹 Reaction Specific Modeling – AI-driven models for cross-coupling reactions, asymmetric hydrogenations, and C–H activations, built on physics-informed descriptors and high-dimensional data analysis for precise yield predictions.
🔹 Seamless Integration – Effortlessly integrates with chemoinformatics and computational chemistry pipelines, ensuring smooth adoption without workflow disruptions.
🔹 Chemist-Centric UI & Advanced Analytics – An intuitive interface empowering chemists with real-time insights, identifying low-performing substrates and high-performing reagent combinations for efficient wet-lab validation.
