Under the development of modern pharmaceutical and computer technologies, using artificial intelligence technology to accelerate drug development progress has become a research hotspot. And efficient prediction of organic reaction products is a key issue in drug retrosynthesis path planning. Concerning the problem of uneven distribution of chemical reaction types in the sample dataset, an Active Sampling-training Gated Graph Convolutional Neural-network (ASGGCN) model was proposed. Firstly, the SMILES (Simplified Molecular Input Line Entry Specification) codes of the chemical reactants were input into the model, and the location of the reaction center was predicted through Gated Graph Convolutional Neural-network (GGCN) and attention mechanism. Then, according to chemical constraint conditions and the candidate reaction centers, the possible chemical bond combinations were enumerated to generate candidate reaction products. After that, the gated graph convolutional difference network was used to rank the candidate products and obtain the final reaction product. Compared with the traditional graph convolutional network, the gated graph convolutional network has three weight parameter matrices and fuse the information through gating, so it can obtain more abundant atom hidden feature information. At the same time, the gated graph convolutional network is trained by active sampling, which can take into account both the analysis abilities of poor samples and ordinary samples. Experimental results show that the Top-1 prediction accuracy of the reaction product of the proposed model reaches 87.2%, which is increased by 1.6 percentage points compared to the accuracy of WLDN (Weisfeiler-Lehman Difference Network) model, illustrating that the organic reaction products can be predicted more accurately by the proposed model.

To solve the issue that, in wireless resource competition, the environment information which gamers get in power game is asymmetric, a power game mechanism based on hidden Markov prediction was proposed. By establishing a Hidden Markov Prediction Model (HMPM), the proposed mechanism estimated whether competitors would take part in the game to improve the information accuracy of the game. Then, the predicted information was used to calculate the best transmission power via the cost function. The simulation results show that, compared with MAP (Maximum A Posteriori) method and NP (No Predicting) method, the power game model based on hidden Markov prediction can not only meet the target capacity, but also improve the power efficiency of the unauthorized users.

To solve the problem of transmission in wireless networks, a transmission and scheduling scheme based on W-learning algorithm in wireless networks was proposed in this paper. Building the system model based on Markov Decision Progress (MDP), with the help of W-learning algorithm, the goal of using this scheme was to transmit intelligently, namely, the package loss under the premise of energy saving by choosing which one to transmit and the transmit mode legitimately was reduced. The curse of dimensionality was overcome by state aggregate method, and the number of actions was reduced by action set reduction scheme. The storage space compression ratio of successive approximation was 41%; the storage space compression ratio of W-learning algorithm was 43%. Finally, the simulation results were given to evaluate the performances of the scheme, which showed that the proposed scheme can transport data as much as possible on the basis of energy saving, the state aggregation method and the action set reduction scheme can simplify the calculation with little influence on the performance of algorithms.