As the primary messenger RNA (mRNA) delivery vehicles, ionizable lipid nanoparticles (LNPs) exhibit excellent safety, high transfection efficiency, and strong immune response induction. However, the screening process for LNPs is time-consuming and costly. To expedite the identification of high-transfection-efficiency mRNA drug delivery systems, we propose an explainable LNPs transfection efficiency prediction model, called TransMA. TransMA employs a multimodal molecular structure fusion architecture, wherein the fine-grained atomic spatial relationship extractor named molecule 3D Transformer captures three-dimensional spatial features of the molecule, and the coarse-grained atomic sequence extractor named molecule Mamba captures one-dimensional molecular features. We design the mol-attention mechanism block, enabling it to align coarse and fine-grained atomic features and capture relationships between atomic spatial and sequential structures. TransMA achieves state-of-the-art performance in predicting transfection efficiency using the scaffold and cliff data splitting methods on the current largest LNPs dataset, including Hela and RAW cell lines. Moreover, we find that TransMA captures the relationship between subtle structural changes and significant transfection efficiency variations, providing valuable insights for LNPs design. Additionally, TransMA's predictions on external transfection efficiency data maintain a consistent order with actual transfection efficiencies, demonstrating its robust generalization capability. We hope that high-accuracy transfection prediction models in the future can aid in LNPs design and initial screening, thereby assisting in accelerating the mRNA design process.

MOTIVATION: Deep generative methods based on language models have the capability to generate new data that resemble a given distribution and have begun to gain traction in ligand design. However, existing models face significant challenges when it comes to generating ligands for unseen targets, a scenario known as zero-shot learning. The ability to effectively generate ligands for novel targets is crucial for accelerating drug discovery and expanding the applicability of ligand design. Therefore, there is a pressing need to develop robust deep generative frameworks that can operate efficiently in zero-shot scenarios. RESULTS: In this study, we introduce ZeroGEN, a novel zero-shot deep generative framework based on protein sequences. ZeroGEN analyzes extensive data on protein-ligand inter-relationships and incorporates contrastive learning to align known protein-ligand features, thereby enhancing the model's understanding of potential interactions between proteins and ligands. Additionally, ZeroGEN employs self-distillation to filter the initially generated data, retaining only the ligands deemed reliable by the model. It also implements data augmentation techniques to aid the model in identifying ligands that match unseen targets. Experimental results demonstrate that ZeroGEN successfully generates ligands for unseen targets with strong affinity and desirable drug-like properties. Furthermore, visualizations of molecular docking and attention matrices reveal that ZeroGEN can autonomously focus on key residues of proteins, underscoring its capability to understand and generate effective ligands for novel targets. AVAILABILITY AND IMPLEMENTATION: The source code and data of this work is freely available in the https://github.com/viko-3/ZeroGEN.

Cyclic peptides, known for their unique ring-like structures, show considerable promise in therapeutic applications. Experimentally determining their permeability is time-consuming and labor-intensive. Hence, an efficient and rapid membrane permeability prediction model would greatly expedite the early-stage screening of cyclic peptide drugs. To meet this end, we proposed a novel deep learning model to predict membrane permeability of cyclic peptides, dubbed as CyclePermea. Remarkably, CyclePermea predicts membrane permeability using only the 1D sequence information of cyclic peptides, unlike previous works based on complex spatial descriptors and various physicochemical properties. It incorporates a peptide encoder based on a pre-trained BERT architecture. We also introduced two auxiliary loss functions designed to enhance the model’s comprehension of cyclic peptides’ distinctive characteristics. The first, termed ’Constraint Contrastive Learning Loss’, aims to mitigate the challenge of feature clustering. The second, ’Cyclization Site Prediction Loss’, is proposed to facilitate the model’s recognition of the unique spatial structure inherent in cyclic peptides. Through extensive experiments, CyclePermea demonstrated superior performance over baseline models in the benchmark dataset, both in in-distribution settings and simulated out-of-distribution settings. We hope that CyclePermea would contribute in accelerating the early screening of cyclic peptide drugs in the future.

Generating realistic images from text descriptions is a challenging problem in computer vision. Although previous works have shown remarkable progress, guaranteeing semantic consistency between text descriptions and images remains challenging. To generate semantically consistent images, we propose two semantics-enhanced modules and a novel Textual-Visual Bidirectional Generative Adversarial Network (TVBi-GAN). Specifically, this paper proposes a semanticsenhanced attention module and a semantics-enhanced batch normalization module. These modules improve consistency of synthesized images by involving precisely semantic features. What's more, an encoder network is proposed to extract semantic features from images. During the adversarial process, the encoder could guide our generator to explore corresponding features behind descriptions. With extensive experiments on CUB and COCO datasets, we demonstrate that our TVBi-GAN outperforms state-of-the-art methods.