In silico synthesis planning

AI powered reaction modeling enables reduction in material and energy costs through reaction predictions, retro-synthesis iterations, yield prediction models


Forward synthesis

AI powered reaction modelling can help predict possible bond changes, products based on functional groups and atomic properties such as charge, hybridization etc.

Such models can be integrated with Electronic Laboratory Notebooks (ELNs) to help chemists make informed decisions regarding the reactions they plan to carry out.

Retrosynthesis path predictions

The resulting leads of a De-Novo Drug design may not be well known or commercially available molecules, leading to the necessity of designing a reaction path for manufacturing.

AI based retrosynthesis models can help chemists run scenario analysis to identify potential reaction paths, evaluate safety and costs involved in various paths before using the wet lab for chemical experiments.

Yield optimization models

AI driven yield prediction models trained on various reaction mechanism templates help reduce material and energy costs by optimizing for reaction conditions such as solvent, temperature, ligand and catalyst.

Building discrete models for individual reaction classes enables higher accuracies and flexibilities for drug chemists to choose relevant models for a given set of reactants.