A method for the early estimation of anaerobic degradability of organic substrates, starting from initial data acquired from tests measuring BMP (Biochemical Methane Potential). The method consists of: i) calculating the two parameters B.sub.0 and k; ii) comparing the fit of the decreasing trend of B.sub.0,est as Δt varies with a homographic function in the first quadrant; iii) evaluating the goodness of fit between a homographic function and the trend of B.sub.0,est as Δt varies, checking whether the adjusted coefficient of determination R.sup.2.sub.adj≥R.sup.2.sub.adj,min; iv) selecting the value of B.sub.0,est corresponding to a slope of less than 0.1% that occurs for three consecutive Δt; if no, acquire additional BMP measurements and repeat the previous steps.

Provided are a method and device for assessing a feasibility of one or more biochemical reactions in an organism. The method includes receiving an input representing the organism and input representing one or more biochemical reactions that are to be assessed; computing a reaction feasibility score for each of the one or more input biochemical reactions a knowledgebase; and selecting the biochemical reaction that is likely to occur in the organism.

Provided are a method and device for assessing a feasibility of one or more biochemical reactions in an organism. The method includes receiving an input representing the organism and input representing one or more biochemical reactions that are to be assessed; computing a reaction feasibility score for each of the one or more input biochemical reactions a knowledgebase; and selecting the biochemical reaction that is likely to occur in the organism.

The efficiency of polymer synthesis is increased by reducing the number of monomer addition cycles needed to create a set of polymer strands. The number of cycles depends on the sequences of the polymer strands and the order in which each type of monomer is made available for addition to the growing strands. Efficiencies are created by grouping the polymer strands into batches such that all the strands in a batch require a similar number of cycles to synthesize. Efficiencies are also created by selecting an order in which the monomers are made available for addition to the growing polymer strands in a batch. Both techniques can be used together. With these techniques, the number of cycles of monomer addition and commensurate reagent use may be reduced by over 10% as compared to naïve synthesis techniques.

Provided are a method and device for assessing a feasibility of one or more biochemical reactions in an organism. The method includes receiving an input representing the organism and input representing one or more biochemical reactions that are to be assessed; computing a reaction feasibility score for each of the one or more input biochemical reactions a knowledgebase; and selecting the biochemical reaction that is likely to occur in the organism.

A single-step retrosynthesis method and system based on a multi-semantic network are provided. The method includes the following steps: inputting an ECFP4 feature and a SMILES word one-hot feature of a target product molecule during the single-step retrosynthesis prediction, and outputting the first k reactions which may occur on the target product molecule in a reaction template through the multi-semantic network. The SMILES string of a reactant corresponding to the target product molecule is calculated by applying the output reaction template into SMILES string of the target product molecule. The present disclosure is the first method for performing single-step retrosynthesis prediction by using a multi-semantic fusion network in the field of single-step retrosynthesis. It is a template-based single-step retrosynthesis method, and the prediction result has relatively high interpretability. The network learns fused semantic features, ECFP4 semantic features and SMILES word one-hot semantic features.

A single-step retrosynthesis method and system based on a multi-semantic network are provided. The method includes the following steps: inputting an ECFP4 feature and a SMILES word one-hot feature of a target product molecule during the single-step retrosynthesis prediction, and outputting the first k reactions which may occur on the target product molecule in a reaction template through the multi-semantic network. The SMILES string of a reactant corresponding to the target product molecule is calculated by applying the output reaction template into SMILES string of the target product molecule. The present disclosure is the first method for performing single-step retrosynthesis prediction by using a multi-semantic fusion network in the field of single-step retrosynthesis. It is a template-based single-step retrosynthesis method, and the prediction result has relatively high interpretability. The network learns fused semantic features, ECFP4 semantic features and SMILES word one-hot semantic features.

Provided is a method and apparatus for determining a molecular conformation. The method of determining a molecular conformation includes generating candidate conformations based on one or more artificial neural network (ANN)-based conformation generative model that is based on an artificial neural network (ANN), comparing energy values between the candidate conformations by inputting the candidate conformations to an ANN-based conformation selecting model, and determining a final conformation based on a result of the comparing.

Provided is a method and apparatus for determining a molecular conformation. The method of determining a molecular conformation includes generating candidate conformations based on one or more artificial neural network (ANN)-based conformation generative model that is based on an artificial neural network (ANN), comparing energy values between the candidate conformations by inputting the candidate conformations to an ANN-based conformation selecting model, and determining a final conformation based on a result of the comparing.

This disclosure relates to an analysis method for analyzing a chemical reaction from one or more starting materials, comprising a preparation step S1 of preparing a reaction network diagram indicating the one or more starting materials, at least part of one or more intermediate products and one or more final products generated from the chemical reaction, and reaction pathways of the chemical reaction, and a prediction step S2 of predicting the reaction rate in each reaction pathway using an artificial intelligence algorithm.