SYSTEMS AND METHODS FOR TEMPLATE-FREE REACTION PREDICTIONS

1 . A computerized method for determining a set of reactions to produce a target product, the method comprising:

receiving the target product;

executing a graph traversal thread;

requesting, via the graph traversal thread, a first set of reactant predictions for the target product;

executing a molecule expansion thread;

determining, via the molecule expansion thread and a reactant prediction model, the first set of reactant predictions; and

storing the first set of reactant predictions as at least part of the set of reactions.

2 . The method of claim 1 , further comprising:

requesting, via the graph traversal thread, a second set of reactant predictions for a reactant prediction from the first set of reactant predictions;

executing a second molecule expansion thread; and

determining, via the second molecule expansion thread and the reactant prediction model, the second set of reactant predictions.

3 . The method of claim 2 , further comprising storing the second set of reactant predictions with the first set of reactant predictions as at least part of the set of reactions.

4 . The method of claim 1 , further comprising:

accessing a set of training reactions; and

training the reactant prediction model using the set of training reactions.

5 . The method of claim 4 , wherein training the reactant prediction model using the set of training reactions comprises incrementally augmenting the set of training reactions during training.

6 . The method of claim 5 , wherein incrementally augmenting the set of training reactions comprises:

augmenting a first portion of the set of training reactions; and

training the reactant prediction model using the augmented first portion of the set of training reactions, comprising using, for each training reaction in the augmented first portion:

a product of the training reaction as an input; and

a set of reactions of the training reaction as an output.

7 . The method of claim 6 , wherein incrementally augmenting the set of training reactions comprises:

augmenting a second portion of the set of training reactions; and

training the reactant prediction model using the augmented second portion of the set of training reactions, comprising using, for each training reaction in the augmented second portion:

a product of the training reaction as the input; and

a set of reactions of the training reaction as the output.

8 . The method of claim 5 , wherein incrementally augmenting the set of training reactions comprises:

augmenting a first portion of the set of training reactions; and

training the reactant prediction model using the augmented first portion of the set of training reactions, comprising using, for each training reaction in the augmented first portion:

a set of reactions of the training reaction as an input; and

a product of the training reaction as an output.

9 . The method of claim 8 , wherein incrementally augmenting the set of training reactions comprises:

augmenting a second portion of the set of training reactions; and

training the reactant prediction model using the augmented second portion of the set of training reactions, comprising using, for each training reaction in the augmented second portion:

a set of reactions of the training reaction as the input; and

a product of the training reaction as the output.

10 . The method of claim 1 , further comprising executing an orchestrator thread, wherein the orchestrator thread:

executes the graph traversal thread;

receives, via the graph traversal thread, the request for the first set of reactant predictions for the target product; and

executes the molecule expansion thread to determine the first set of reactant predictions.

11 . The method of claim 10 , wherein the orchestrator thread transmits the determined first set of reactant predictions to the graph traversal thread.

12 . The method of claim 10 , wherein the orchestrator thread stores the first set of reactant predictions to maintain a retrosynthesis graph.

13 . The method of claim 12 , further comprising executing a tree search on the retrosynthesis graph to identify a set of possible routes through the retrosynthesis graph, wherein each route of the set of possible routes represents an associated way to build the target product.

14 . The method of claim 13 , further comprising updating, for each route identified in the set of possible routes, a blacklist of reactant-product pairs.

15 . The method of claim 14 , further comprising omitting one or more additional routes from the set of possible routes by determining, during the tree search, that the one or more additional routes containing a reaction in a reaction-product pair in the blacklist.

16 . The method of claim 1 , wherein the reactant prediction model is a trained single-step retrosynthesis model that determines the first set of reactant predictions based on the target product.

17 . The method of claim 16 , wherein the single-step retrosynthesis model comprises:

a trained forward prediction model configured to generate a product prediction based on a set of input reactants; and

a trained reverse prediction model configured to generate a set of reactant predictions based on an input product.

18 . The method of claim 17 , wherein the set of input reactants, the set of reactant predictions, or both, comprise one or more of:

one or more reagents;

one or more catalysts; and

one or more solvents.

19 . The method of claim 17 , wherein determining, via the reactant prediction model, the first set of reactant predictions comprises:

predicting, by running the trained reverse prediction model on the target product, the first set of reactant predictions;

predicting, by running the trained forward prediction model on the first set of reactant predictions, a product; and

comparing the target product with the predicted product to determine whether to store the first set of reactant predictions.

20 . A non-transitory computer-readable media comprising instructions that, when executed by one or more processors on a computing device, are operable to cause the one or more processors to determine a set of reactions to produce a target product by performing:

receiving the target product;

executing a graph traversal thread;

requesting, via the graph traversal thread, a first set of reactant predictions for the target product;

executing a molecule expansion thread;

determining, via the molecule expansion thread and a reactant prediction model, the first set of reactant predictions; and

storing the first set of reactant predictions as at least part of the set of reactions.

21 . A system comprising a memory storing instructions, and at least one processor configured to execute the instructions to determine a set of reactions to produce a target product by performing:

receiving the target product;

executing a graph traversal thread;

requesting, via the graph traversal thread, a first set of reactant predictions for the target product;

executing a molecule expansion thread;

determining, via the molecule expansion thread and a reactant prediction model, the first set of reactant predictions; and

storing the first set of reactant predictions as at least part of the set of reactions.