What Is Computational Synthesis Planning?

Advances in computer hardware and computer technology are aimed at accelerating and improving all aspects of the classic design, synthesis, testing, and analysis cycle of medicinal chemistry. The use of data-driven synthetic prediction tools to reduce failures in the synthesis of new molecular entities has attracted more and more attention. Combining with artificial intelligence (AI) design synthetic routes, scientists use existing chemical reactions in the database to train AI algorithms to propose synthetic routes for a given molecule, including reaction conditions, and evaluate which route is best based on the number of steps and yield predictions. 

Scope of Applications

• Speed up and improve the drug discovery cycle.
• Accelerate the discovery of new molecular entities and reduce failure rates.
• Guide scale-up and process optimization.

Retrosynthesis

The bottleneck of manual synthetic evaluation can be solved by generating a hypothetical synthetic route, which is used to quickly determine the priority of compounds through a easy synthesis process. 

The general steps of extracting templates from the reaction data set through algorithms are:
1) Identify the reaction center.
2) Identify the atoms adjacent to the reaction center.
3) Add the functional groups involved in the reaction.

Condition recommendation

Models based-machine learning has the potential to provide better prediction of performance and certainty of chemical reaction, thereby speeding up searches.

Forward reaction prediction

Compared with the retrosynthesis model, it's easier to use the forward reaction model to evaluate quantitatively. One of the goals of computational synthesis planning is to ensure that the results obtained through algorithm design are reliable and feasible by performing accurate prediction.