There are several related but distinct applications available through the ASKCOS website. These applications and their corresponding use cases are listed in the table below.
|Application||Brief Description||Use Case|
|One-Step Retrosynthesis||Single-step retrosynthesis||Identifying precursors (buyable or non-buyable) that can be used to synthesize a target compound via a single reaction|
|Tree-Builder||Multi-step retrosynthesis (automatic)||Identifying synthetic pathways that can be used to synthesize a target compound via a series of reactions (allows for creation of pathways from buyable chemicals to a target)|
|Context Recommendation||Reaction condition recommendations||Suggesting conditions (reagent, catalyst, solvents, temperature) under which a specified reaction can be run successfully|
|Forward Prediction||Product distribution||Predicting the products that are likely to be generated when two reactants are combined under specified conditions (reagent, catalyst, solvents, temperature)|
|Reaction Evaluation||Probability of reaction success||Predicting whether or not a target compound of interest will be generated when specified precursors react with one another, and simultaneously identifying conditions that are most likely to promote creation of the specified target compound|
|SCScore Evaluator||Estimate synthetic complexity||Predicting the synthetic complexity of a molecule [1-5 scale] using a trained neural network model|
|Buyable Look-Up||Database of commercially-available compounds||Determining whether or not a chemical is listed in our Buyables database and, if so, for what price (in USD per gram)|
|Drawing||Structures from SMILES/SMARTS||Converting molecule/reaction SMILES strings or template SMARTS strings to skeletal formulas|
Generally, the website takes target compound inputs as SMILES strings, although there are some compounds for which the common name (aspirin, ibuprofen, etc.) can be entered in the input window and converted to a SMILES string automatically. For more information about SMILES strings, click here. Compound SMILES strings can be obtained by first drawing the molecule of interest in ChemDraw, selecting the molecule with the selector tool, right-clicking, and choosing 'Molecule', 'Copy As', and finally, 'SMILES'. Alternatively, much of the website also supports drawing compounds of interest directly. This can be done by clicking the Draw links adjacent to input text fields, where applicable.
One-Step Retro is used to identify precursor molecules that can be combined to generate a target compound via a single reaction.
The one-step retrosynthesis tool generates a list of recommended precursor pairs. The pairs are rank-ordered based on the scoring function specified by the user. For each pair, the following information is displayed:
Adjacent to each pair of precursor SMILES strings is a link, "-->?”, that copies the strings over to the context recommender (see below) for easy acquisition of context recommendations.
To demonstrate the use of the one-step retrosynthesis tool, consider the potential target compound Atropine. To obtain a list of precursors that can be used to generate Atropine via a single reaction, click the “One-step Retro” link on the left-hand side of the ASKCOS homepage. The following screen will appear:
Enter Atropine (by name or SMILES string: CN1C2CCC1CC(C2)OC(=O)C(CO)c3ccccc3) into the “Target compound” field. The default algorithm options generally work well, and may be left as-is. Click Search. Below the Search button, the parsed molecule structure will appear, along with the rank-ordered list of precursor candidates. The first three candidates are shown in the image below. If a candidate is buyable, its price per gram will appear next to its SMILES string; if not, the phrase “cannot buy” is listed:
The SMILES strings of the proposed candidates are links that the user can click to run the one-step retrosynthesis on a candidate. This allows users to design full syntheses manually, in a step-by-step fashion. For example, clicking the SMILES string of the non-buyable precursor that is ranked second in the output above automatically runs the one-step expansion on that candidate:
Alternatively, the user can click the link designated by "-->?"" adjacent to the precursor SMILES strings in the one-step output. This automatically runs the context recommender. For example, clicking the context recommendation link for the first pair of precursors in the one-step retrosynthesis we generated for Atropine gives:
You can also click on the image of the reactants themselves to expand a list of the templates that were used to suggest that transformation. Multiple templates can lead to the same precursor, so there may be many with different degrees of specificity and different numbers of precedents.
Clicking through provides more details about the template, including its SMARTS string. It displays a selection of precedents from Reaxys sorted by decreasing yield.
The Tree Builder is used to identify precursor molecules that can be combined to generate a target compound via a series of reactions (a “tree”). This is a multi-step retrosynthetic planner.
The tree-builder produces a rank-ordered list of recommended synthetic routes. Compounds that are buyable are outlined in green; those that are not are outlined in orange. Between each target compound and its retrospective precursor(s) is a number that specifies the number of examples of reactions in Reaxys that support the template(s) that was (were) applied to make the proposed disconnection.
You can store the results of an expansion and review them later. To do this after an expansion is complete, scroll to the top of the website and click the "My Results" tab in the black banner. In the list that appears, click the “Save this page” link to save your results. From this list, you can also access results that you have saved previously (to do this, click "See saved results" instead of "Save this page").
Additionally, once a retrosynthetic expansion is performed, the user has the option to blacklist a chemical or reaction that appears in the tree if it is, e.g., known to be patented. Then, the user may re-run the expansion to obtain recommendations that exclude the chemical(s) or reaction(s) that were deemed undesirable. The list of compounds and reactions that you have blacklisted can be accessed by clicking the "My Banlist" tab in the black banner at the top of the website. For more information, see the Example below.
A set of proposed synthetic routes to the compound Fluconazole is easily created using the default tree-builder settings. To get started, click the Tree Builder module option (accessible from the Modules drop-down at the top of the ASKCOS website). This will bring up a field where you can enter the SMILES string of your target (in this case, Fluconazole):
When you enter Fluconazole’s name or SMILES string in the "Target compound" field, the various algorithm settings appear, along with the parsed structure (which you should confirm is correct):
Click Start. Once the expansion is complete, a summary of the results of the expansion appears, followed by a rank-ordered list of potential synthetic routes. The summary of the expansion results shows the number of chemicals (or, at half-depths, reactions) that were found by the algorithm during the expansion.
The first recommendation in the list of synthetic routes is Fluconazole itself, because it can be purchased for less than the maximum chemical price that was specified as part of the stop criteria. Since Fluconazole is commercially available, it is outlined in green:
The second option is a linear synthesis consisting of two reactions. The epoxide intermediate is outlined in orange because it is not buyable:
Hovering over each of the compounds pulls up a pale-yellow window with additional information, including the compound’s SMILES string, frequency of appearance as a reactant or product in Reaxys, and the options to “blacklist” or “hide all” (see Tree Builder - Output). For compounds that are commercially available, the price per gram is listed (non-purchasable compounds show the words “not buyable”). For example, for the non-purchasable epoxide intermediate:
As described in Tree Builder - Output, compounds and reactions may be blacklisted, if desired. To do this, for example, for the epoxide intermediate in Option 2, mouse over the compound, and click the blacklist link in the pale-yellow window that appears. Another pop-up will appear with a text field where you can specify the reason for blacklisting the chemical (for your records only). After you click OK, another window will appear, confirming that the compound with the specified SMILES string was blacklisted. Click OK again.
To review the compounds and reactions that you’ve blacklisted, scroll to the top of the webpage and click the "My Banlist" tab. A list of options will appear, including the option to “View banned chemicals”:
If you click the “View banned chemicals” link, you will be taken to a new webpage that shows a list of the chemicals you’ve blacklisted. For now, only the epoxide intermediate appears. You have the option to temporarily disable compounds on your blacklist by clicking the True link under the Active column on the left; you may also permanently delete compounds on your blacklist by clicking the X under the Delete column on the right:
Now that we’ve blacklisted this chemical, it is possible to return to the Tree Builder tab and rerun the Fluconazole expansion. This time, none of the trees that the algorithm recommends will contain the blacklisted chemical.
The context recommender is useful for cases when you know the chemical transformation you want to make, and you’d like recommendations for the reaction conditions. This might be the case, for example, after you’ve run the Tree Builder on a target compound and you have a series of transformations in-hand: each reaction in the series can then be passed through the context recommender to obtain suggested reaction conditions. At this time, the context recommender is capable of recommending a catalyst, reagent, solvent (up to 2), and temperature.
Two options for the context recommender are available: a neural network and a nearest neighbor search. The nearest neighbor approach is an early version of the context recommender that relies on computationally expensive similarity calculations. It is slower and less accurate than the neural network.
The context recommender produces a list of recommendations for reaction conditions. Each recommendation contains a subset of the following features: reagent, catalyst, solvent (1 or 2), and temperature. We define reagents as compounds that do not contribute heavy atoms to the product. In some cases, the distinction between a reagent and a catalyst in Reaxys is ambiguous, such that the distinction between these categories in the recommendations may be ambiguous as well, although data cleaning has been performed to help mitigate this. Note that the context recommender will not produce an error if the proposed chemical transformation is not achievable via a single reaction.
Consider the FMOC deprotection in the scheme below. A weak base would effectively remove the FMOC group without altering the other functionality in the molecule. This example will show that the context recommender is sensitive to this type of requirement.
To begin, click the Context Recommendation option on the left-hand side of the ASKCOS homepage. This will bring up a screen where you can enter the SMILES strings of the reactant(s) and product of the reaction of interest (or, alternatively, you can click the Draw link adjacent to the text fields).
In the “Reactants” text field, enter the SMILES strings of the reactant in the FMOC deprotection: Reactant SMILES: O=C([C@H](CO[C@@H]1O[C@@H]([C@@H]([C@@H]([C@H]1NC(C)=O)OC(C)=O)OC(C)=O)COC(C)=O)NC(OCC2C3=C(C4=C2C=CC=C4)C=CC=C3)=O)NCCCCCCCCCCCCCC For reactions that include multiple reactants, distinct reactant SMILES strings should be separated with a period. In the “Products” text field, enter the SMILES string of the product of the FMOC deprotection: Product SMILES: O=C([C@H](CO[C@@H]1O[C@@H]([C@@H]([C@@H]([C@H]1NC(C)=O)OC(C)=O)OC(C)=O)COC(C)=O)N)NCCCCCCCCCCCCCC Once you enter the strings, the parsed structures will appear:
For the context recommender, choose the neural network. Click Get Context Recommendations. Below the Get Context Recommendations button, a list of ten recommendations will appear:
The results, which include weak bases and low temperatures, reflect the need to selectively deprotect the Fmoc group.
The forward predictor can be used to predict the outcome of a reaction between any number of compounds.
Prediction approach – two options exist:
Maximum number of products
The forward predictor produces a list of candidate reaction outcomes that are rank-ordered by a Probability score that reflects the model’s confidence that each outcome will appear. The probabilities for a set of candidates should not be interpreted together to comprise an expected product distribution, nor should an individual probability be interpreted as an expected yield; rather, a probability for a given outcome purely reflects the model’s confidence regarding whether or not that compound will appear at all (the model is not trained on specific yield-related data).
Consider the p-toluenesulfonic acid-promoted etherification of benzhydrol by dimethylaminoethanol that can be used to synthesize diphenhydramine. To predict the outcome of a reaction between benzhydrol and DMAE in the presence of p-toluenesulfonic acid, start by entering the SMILES strings of the two reactants, separated by a period, in the "Reactants" field (or, alternatively, draw the structures in the window that appears after clicking the Draw link adjacent to the Reactants field).
SMILES string of DMAE: CN(C)CCO SMILES string of benzhydrol: OC(c1ccccc1)c1ccccc1 The parsed structures will appear so the user can confirm that their input was interpreted correctly.
Next, choose the Prediction approach. Since we have a reagent that we would like to specify, we can use either approach, although the template-based approach does not make any explicit consideration of the fact that p-toluenesulfonic acid is expected to act as a reagent and not a reactant.
Choosing the template-free expansion method gives the predicted outcomes according to the Template-free forward predictor:
The reaction evaluator allows you to specify a pair of precursor compounds and a potential target compound, and reports the model’s confidence (probability) that the target compound will be generated via a reaction between the specified precursors.
Prediction approach – two options exist:
Context recommender – two options exist when applicable (see Context Recommendation - Options for more information):
The primary output of the reaction evaluator is the plausibility score, which indicates the model’s confidence that the compound specified by the user in the “Product” field will be generated when the specified precursors react with one another.
Consider the simplest one-step retrosynthesis for diphenhydramine. We can enter the reactant and product SMILES strings into the Evaluator:
Using the Template-free method, we can virtually screen 10 different reaction conditions to find which one seems to be most promising, i.e., which one leads to the intended product with the highest confidence.Alternately, we can ask if this reaction is possible under any set of conditions, which is the question that the Fast filter is designed to answer. Choosing this option gives a very confident prediction that this reaction is possible.