Development and validation of an attention-based cGAS-specific deep learning scoring function for structure-based virtual screening

3Br-MGD: few-shot toxicity prediction with a three-branch deep encoder and meta-learning framework.

Predicting the toxicity of pharmaceutical compounds remains a major challenge in drug discovery. Early and accurate toxicity assessment is essential for eliminating harmful candidates before costly preclinical and clinical testing, thereby improving patient safety, reducing development costs, and accelerating the drug development process. Despite advances in computational toxicology, existing methods often struggle to capture complex molecular characteristics and maintain robust performance under limited-data conditions. To address these challenges, we propose 3Br-MGD, a novel three-branch framework that integrates deep learning and meta-learning for molecular toxicity prediction. The architecture combines complementary molecular representations: FingerprintMLP encodes Morgan fingerprint descriptors, Graph Convolutional Networks (GCNs) capture structural information from molecular graphs, and one-dimensional Deep Convolutional Neural Networks (1D-CNNs) extract sequential features from SMILES strings. These embeddings are integrated within a Prototypical Network-based few-shot learning framework, enabling rapid adaptation to new prediction tasks with limited labeled samples and improving generalization in low-resource settings. Experimental results on benchmark toxicity datasets demonstrate that 3Br-MGD consistently outperforms conventional baselines in predictive accuracy, robustness, and generalization. Furthermore, the integration of heterogeneous molecular encoders reduces dependence on large training datasets while enhancing interpretability through the exploitation of complementary chemical information from multiple molecular views.

Deep learning-assisted virtual screening of a large chemical library for selective GSK3β inhibitors.

Dual-Attention Multimodal Framework for Molecular Property Prediction

A novel Dual-Attention Multimodal framework for Graphs and Sequence-based representations, so-called DAM-GS, which provides a promising solution for molecular property prediction with broad applications in drug discovery and computational molecular science.

A synergistic deep learning and machine learning framework for screening heterocyclic compounds against ALDH1A1.

An integrated computer-aided drug design (CADD) and artificial intelligence (AI) framework to systematically identify selective ALDH1A1 inhibitors from a heterocyclic compound library is developed and suggests that LDN-27219 exhibits favorable binding characteristics and represents a promising lead candidate for subsequent experimental validation.

CLDN18.2 antibody design with protein language models: A deep learning optimization framework

CLDN18.2 is a promising tumor-specific antigen; however, the development of therapeutic antibodies against it is challenged by the need for simultaneous optimization of affinity and developability. To address this, we present cdrGPT, a deep learning framework based on GPT-2 for de novo generation of complementarity-determining region H3 (CDRH3) sequences. Our approach integrates pre-training on the Observed Antibody Space (OAS) database with structural templating derived from the known antibody zolbetuximab. Generated sequences were iteratively refined through rejection sampling and fine-tuned against a multi-parameter objective function encompassing predicted affinity and MHC class II binding risk. From an initial set of 50,000 sequences, this screening pipeline yielded 313 high-confidence candidates. Subsequent analysis using evolutionary scale modeling 2 (ESM2) embeddings, principal component analysis (PCA), and clustering revealed three structurally distinct clusters, with intra-cluster cosine similarities exceeding 0.99. Validation of seven representative sequences from the dominant cluster using AlphaFold3 confirmed high structural fidelity to the zolbetuximab template, demonstrating a root mean square deviation (RMSD) of 1.331 Å for the CDRH3 loop and positional deviations of less than 0.4 Å for key paratope residues. These results indicate that the designed variants preserve the core binding mode of the parent antibody. This study establishes a feasible pipeline for integrating AI-generated CDRH3 loops into functional antibody scaffolds, providing a foundation for the accelerated development of therapeutics targeting CLDN18.2 and other clinically relevant antigens.

A Scalable Structure-Aware Multimodal Architecture for Accurate Drug-Target Affinity Prediction.

Accurate prediction of drug-target binding affinity (DTA) is a key task in virtual screening. However, current computational methods face a key challenge: sequence-based approaches often fail to capture critical spatial information, while structure-based models rely on computationally expensive 3D coordinates, which restrict their scalability. To address this issue, we propose StructuraDTA, a novel multimodal framework that adopts an implicit structure modeling strategy. Instead of using static protein folding data, our method encodes drug molecular graphs via Graph Isomorphism Networks (GINs) to capture fine-grained topological features. Meanwhile, we optimize protein representations by integrating probabilistic structural priors into a pretrained language model, which effectively simulates thermodynamic conformational flexibility without relying on explicit 3D structural data. A bidirectional cross-attention mechanism is then used to dynamically align these heterogeneous feature modalities. Comprehensive evaluations on the Davis and KIBA benchmark datasets show that StructuraDTA stably outperforms state of-the-art comparison methods. Importantly, the model exhibits strong robustness in cold-start scenarios, and can accurately predict binding affinities for previously unseen drugs and targets. By retaining the predictive performance of structure based models while maintaining the high inference efficiency of sequence-based methods, we provide an accurate and scalable solution to accelerate genome-scale drug discovery research.