In recent years, artificial intelligence (AI) has re-emerged due to the continuous advancement of computing abilities, big data and algorithms. With the development of chemoinformatics, AI technology has shown great potential in the field of chemistry. AI brought new opportunities for the development of the prediction of synthetic products and reaction path prediction and has been widely applied in drug design. Alfa Chemistry has applied advanced AI technique to provide accurate prediction of synthetic products and study the path formation.
Figure 1. Synthetic Products Prediction and Path Formation. (Wu, G.; et al. 2016)
Machine learning builds a hypothesis space in which the program learns common patterns in the data, and builds models and tries to predict the true value of the benchmark. The closer the predicted value is to the baseline true value, the more accurate the model. Through such a process of continuous learning and optimization, the program tries to obtain the learning and problem-solving capabilities of the human brain.
We use algorithms in de novo molecular design methods to virtually design and evaluate a series of molecules that meet specific properties for the prediction of synthesized product molecules.
At Alfa Chemistry, we have applied advanced synthetic programs combined with complex algorithm and descriptors to provide reliable results of synthetic products prediction and path formation. Our process is as follows:
The program we designed will output synthesis suggestions for a specific reaction.
Our synthetic planners will give a complete synthetic route (even reaction conditions). This process requires a module to resolve the target compound, and these modules usually apply rule-based or irregular methods to propose possible transformations.
The data set can be obtained from SciFinder, Reaxys, patents, published chemical literature or proprietary databases. We construct a data set to build the model that include data points representing the range of possible parameter values to avoid overfitting or introducing the training set deviation.
Descriptors can be divided into physical-based descriptors or information-based descriptors. When building a reaction prediction model, one must consider how many dimensions or reaction variables need to be modeled and how to most effectively characterize them. Variables commonly used in reaction prediction include substrates, solvents, temperature, additives, bases, and ligands.
The machine learning algorithms used in synthetic chemistry can be divided into linear and nonlinear categories. At Alfa Chemistry, random forest, k nearest neighbors, support vector machine and neural network methods are available.
The synthetic products prediction and path formation are obtained.
Our synthetic products prediction and path formation services remarkably reduce the cost, promote further experiments, and accelerate the process of drug design for customers worldwide. Our personalized and all-around services will satisfy your innovative study demands. If you are interested in our services, please don't hesitate to contact us. We are glad to cooperate with you and witness your success!