Small-molecule drug design aims to generate compounds that target specific proteins, playing a crucial role in the early stages of drug discovery. Recently, research has emerged that utilizes the GPT model, which has achieved significant success in various fields to generate molecular compounds. However, due to the persistent challenge of small datasets in the pharmaceutical field, there has been some degradation in the performance of generating target-specific compounds. To address this issue, we propose an enhanced target-specific drug generation model, Adapt-cMolGPT, which modifies molecular representation and optimizes the fine-tuning process. In particular, we introduce a new fine-tuning method that incorporates an adapter module into a pre-trained base model and alternates weight updates by sections. We evaluated the proposed model through multiple experiments and demonstrated performance improvements compared to previous models. In the experimental results, Adapt-cMolGPT generated a greater number of novel and valid compounds compared to other models, with these generated compounds exhibiting properties similar to those of real molecular data. These results indicate that our proposed method is highly effective in designing drugs targeting specific proteins.

We propose a novel power control technique called PC-ECGNN, which uses edge convolution to optimize power allocation in wireless IoT networks. PC-ECGNN leverages interference link distances as edge features and desired link channel gains as initial vertex features, iteratively updating vertex features based on neighbors and edge features. PC-ECGNN is the first technique to incorporate edge convolution into power control and has been customized for the considered scenario, optimizing the neural network structure to provide fast convergence and high performance simultaneously. Experimental results show that PC-ECGNN outperformed the state-of-the-art PC-MPGNN, achieving a 4% increase in average spectral efficiency and a 4dBm reduction in average transmit power compared to PC-MPGNN. Furthermore, our technique demonstrates advantages over existing methods in dynamic environmental changes. The proposed model, trained in a fixed environment, showed minimal performance degradation across various test environments different from the training setting, outperforming traditional models trained in individual environments. When applying meta-learning, the proposed model achieved better performance in each test environment after additional fine-tuning with only 1% of the pre-training epochs, compared to models trained with the full number of epochs in each individual test environment.