Predict reagents, catalysts, solvents and temperature for a desired transformation using a neural network model. (ACS Cent. Sci., 2018, 4, 1465-1476)
New in 2021.01: Quantitative condition predictions now available using neural network v2 model. Select in settings menu.
Graph models require atom-mapped reactions. If atom-mapping is not available, please try the fingerprint models instead.
Predict most likely outcomes of a chemical reaction using a template-free WLN model for predicting likely bond changes. (Chem. Sci., 2019, 10, 370-377)
Predict likely impurities for a chemical reaction. Considers minor products, over-reaction, dimerization, solvent adducts, and subsets of reactants.
Predict selectivity of regio-selective reactions. The QM-GNN model combines a WLN graph encoding with predicted quantum descriptors as input to a multitask neural network. (ChemRxiv 2020)
Predict site selectivity of aromatic C-H functionalization reactions with a multitask neural network that uses a WLN graph encoding. (React. Chem. Eng., 2020, 5, 896-902)
# | Rank | Reactants (Amount) | Reagents (Amount) | Temperature | Predict with conditions |
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%% index+1 %% | ✔ (rank: %% result.evaluation %%) (rank: N/A) |
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%% Math.round(result.temperature) %% °C |
# | Rank | Reagents | Catalyst | Solvents | Solvent Score | Temperature | Predict with conditions |
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%% index+1 %% | ✔ (rank: %% result.evaluation %%) (rank: N/A) |
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%% result.solvent_score %% |
%% Math.round(result.temperature) %% °C |
Rank | Product | Probability | Max. Score | Molecular Weight | Predict impurities | Predict regio-selectivities |
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%% res.rank %% |
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%% (Math.round((res.prob + Number.EPSILON) * 10000) / 10000).toFixed(4) %% | %% (Math.round((res.score + Number.EPSILON) * 1000) / 1000).toFixed(3) %% | %% (Math.round((res.mol_wt + Number.EPSILON) * 10) / 10).toFixed(1) %% |
Rank | Product | Probability |
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%% res.rank %% |
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%% (Math.round((res.prob + Number.EPSILON) * 10000) / 10000).toFixed(4) %% |
Reactant | Sites |
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