Deep learning is improving and changing the process of de novo molecular design at a rapid pace. In recent years, great progress has been made in drug discovery and development by using deep generative models for de novo molecular design. However, most of the existing methods are string-based or graph-based and are limited by the lack of some very important properties, such as the three-dimensional information of molecules. We propose DNMG, a deep generative adversarial network (GAN) combined with transfer learning. Specifically, we use a Wasserstein-variant GAN based network architecture that considers the 3D grid spatial information of the ligand with atomic physicochemical properties to generate a representation of the molecule, which is then parsed into SMILES strings using an improved captioning network. Comprehensive in experiments demonstrate the ability of DNMG to generate valid and novel drug-like ligands. The DNMG model is used to design inhibitors for three targets, MK14, FNTA, and CDK2. The computational results show that the molecules generated by DNMG have better binding ability to the target proteins and better physicochemical properties. Overall, our deep generative model has excellent potential to generate molecules with high binding affinity for targets and explore the space of drug-like chemistry.

The enhancer is a DNA sequence that can increase the activity of promoters and thus speed up the frequency of gene transcription. The enhancer plays an essential role in activating gene expression. Currently, gene sequencing technology has been developed for 30 years from the first generation to the third generation, and a variety of biological sequence data have increased significantly every year. Due to the importance of enhancer functions, it is very expensive to identify enhancers through biochemical experiments. Therefore, we need to study new methods for the identification and classification of enhancers. Based on the K-mer principle this study proposed a feature extraction method that others have not used in convolutional neural networks. Then, we combined it with one-hot encoding to build an efficient one-dimensional convolutional neural network ensemble model for predicting enhancers and their strengths. Finally, we used five commonly used classification problem evaluation indicators to compare with the models proposed by other researchers. The model proposed in this paper has a better performance by using the same independent test dataset as other models.

The Shwachman-Diamond syndrome (SDS) is a rare inherited ribosomopathy that is predominantly caused by mutations in the Shwachman-Bodian-Diamond Syndrome gene (SBDS). SBDS is a ribosomal maturation factor that is essential for the release of eukaryotic translation initiation factor 6 (eIF6) from the 60S ribosomal subunits during the late stages of 60S maturation. Release of eIF6 is critical to permit inter-subunit interactions between the 60S and 40S subunits and to form translationally competent 80S monosomes. SBDS has three key domains that are highly flexible and adopt varied conformations in solution. To better understand the domain dynamics of SBDS upon binding to 60S and to assess the effects of SDS-disease specific mutations, we aimed to site-specifically label individual domains of SBDS. Here we detail the generation of a fluorescently labeled SBDS to monitor the dynamics of select domains upon binding to 60S. We describe the incorporation of 4-azido-l-phenylalanine (4AZP), a noncanonical amino acid in human SBDS. Site-specific labeling of SBDS using fluorophore and assessment of 60S binding activity are also described. Such labeling approaches to capture the interactions of individual domains of SBDS with 60S is also applicable to study the dynamics of other multi-domain proteins that interact with the ribosomal subunits.