From silos to synthesis: Fostering collaborative AI through platform integration with LiveDesign ML

Successfully leveraging AI investments demands a platform that delivers unparalleled predictive accuracy and seamless operationalization. Many organizations struggle with fragmented ML infrastructure and models built on inconsistent data, leading to low adoption and high MLOps friction.

LiveDesign ML transforms AI into a strategic asset by providing a centralized, integrated ML platform engineered for scale and collaboration. This platform breaks down data silos by unifying disparate data sources and computational tools into a single, cohesive workflow. This integration enables the real-time sharing of models, features, and experimental results, allowing domain experts and data scientists to collaboratively build and iterate on ML solutions. LiveDesign ML leverages best-in-class training data from Schrödinger’s gold-standard modeling tools for superior model quality and prospective confidence. Furthermore, it fully automates the entire MLOps lifecycle – from training and validation to deployment – guaranteeing high-performance models are available in real-time.

In this session, we will demonstrate how to:

• Maximize AI ROI: Eliminate model deployment friction and minimize manual MLOps with our automated platform.

• Achieve Gold-Standard Accuracy: Leverage models trained on data from validated, physics-informed simulation tools.

• Scale and Integrate: See current features like Retrosynth and Chemical Property Predictions in action, and explore the strategic roadmap to GenerativeML, Co folding, and LD Assistant

• Live demo: See how LiveDesign ML leads to accelerated discovery cycles, enhanced model fidelity, and a higher return on ML investment from our product expert

Who should attend: 

This webinar is tailored for leaders and practitioners focused on driving efficiency and accuracy in drug discovery using advanced computation and AI.

• Heads/VPs of Computational Chemistry, AI/ML, and R&D

• Cheminformatics and MLOps Leads

• Computational Chemists and Biologists

• Informatics and Data Science Strategists