RetroSynth
AI-driven retrosynthesis at scale
Breaking the synthesis bottleneck with AI and physics-based modeling
Modern drug discovery is often bottlenecked by manual synthesis planning, which limits the exploration of chemical space and often leads to project failures. RetroSynth is the AI-driven synthesis planning platform engineered to break this constraint. By harnessing advanced deep learning algorithms, we can rapidly predict optimal, highly accurate, and cost-efficient synthetic pathways that bypass the rigidity of traditional workflows, accelerating your lead optimization at a fraction of the time and cost. Coupled with accurate physics-based methods (e.g. FEP+) and accurate ML/AI models, this provides a complete solution to effectively explore vast chemical space and rapidly and accurately select the best molecules that are also synthetically tractable.
See your designs move from bench to synthesis, faster and cheaper
Predict and identify actionable routes
Generate and score thousands of optimal, plausible routes, based on real-time availability of internal and commercial building blocks, to deliver the most optimal synthetic routes directly to the bench
Save up to 20X on synthesis costs per compound
Dramatically reduce project spend by identifying efficient, reliable, and short synthesis routes that minimize the need for expensive, multi-stage custom synthesis.
Maximize the ROI of each DMTA cycle
Accelerate your path to go/no-go decisions by using AI-powered forward reaction verification to ensure you only invest resources in compounds with the highest probability of successful synthesis.
Reduce synthesis turnaround time by up to 3X
Eliminate the bottleneck of manual route planning with a cloud-native architecture that evaluates 100,000+ ideas in 24 hours, identifying the fastest path from design to tested compound.
Key Innovations
Unrivaled Scientific Accuracy
By utilizing Message Passing Neural Networks (MPNN) for forward reaction verification alongside custom scoring developed with medicinal chemists, RetroSynth ensures that every suggested pathway is plausible and bench-ready, saving you time on unpromising compounds.
Real-Time Building Block Intelligence
A proprietary bloom filter implementation allows the system to query billions of building blocks in real-time, delivering actionable routes based on the immediate availability of internal and commercial precursors.
Integrated Workflow ROI
Tight integration with cutting edge computational tools such as FEP+ and De Novo Design through LiveDesign allows you to seamlessly prioritize top-scoring compounds within your existing workflow, potentially saving up to $9M per project by avoiding the synthesis of molecules that will not advance.
Massive Scale & Performance
Our distributed cloud-native MCTS architecture and Kubernetes infrastructure allow you to complete 100,000+ ideas in 24 hours at a low compute cost, enabling the rapid exploration of ultra-large chemical spaces without sacrificing accuracy.
Featured Webinar
Reach out to see the AI advantage in action
Training & Resources
Online certification courses
Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.
Tutorials
Learn how to deploy the technology and best practices of Schrödinger software for your project success. Find training resources, tutorials, quick start guides, videos, and more.
Other Resources