Aug 2026· Journal of King Saud University: Computer and Information Sciences· Vol 38· 0 citations· 47 references
Computer Science
TL;DR
Overall, DrugPLMFormer provides a reproducible, leakage-aware framework for retrospective sequence-based druggability screening and target prioritization, while prospective validation and experimental confirmation remain necessary before operational deployment.
Abstract
Sequence-based protein druggability classification can support early target triage when structural information is unavailable, uncertain, or inconsistently linked to druggability labels. We present DrugPLMFormer, a sequence-first retrospective screening framework that combines frozen protein language model embeddings with self-attentive BiLSTM encoding, Transformer-based long-range modeling, optional physicochemical feature fusion, and compute-budgeted BO–CTCM model selection. Hyperparameters were selected through multi-fidelity screening within an approximately 200-evaluation budget, using a validation objective that combined AUPRC and MCC to balance threshold-free discrimination with operating-point stability, rather than to imply unrestricted generalization. On ProTar-II, using a 50% sequence-identity homology-aware split, DrugPLMFormer achieved 95.98% accuracy, 96.01% F1-score, 96.42% sensitivity, 95.61% specificity, and 0.981 ROC-AUC. Without using external data for training, tuning, threshold selection, or early stopping, the selected model showed favorable held-out mean performance on ProTar-II-Ind (96.62% accuracy, 0.9688 ROC-AUC) and DPI_CDF (96.20% accuracy, 0.9696 ROC-AUC). Paired external analyses indicated that accuracy and F1-score differences were numerically favorable but not statistically significant, whereas the ROC-AUC improvement on DPI_CDF was statistically supported. Train-to-external homology analysis showed that most external proteins had less than 50% sequence identity to the training set, although residual dataset shift and label heterogeneity may still affect generalization. With cached PLM embeddings, downstream CPU inference required approximately 1.0–1.2 ms per sequence, excluding tokenization and ESM-2 embedding generation. Overall, DrugPLMFormer provides a reproducible, leakage-aware framework for retrospective sequence-based druggability screening and target prioritization, while prospective validation and experimental confirmation remain necessary before operational deployment.
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.
Tao Qu, Lingyan Yuan, Wei-Ran Cui et al.· PLoS Computational Biology· 0 citations
Binary protein classification supports diverse tasks in computational biology, including pathway-membership inference and sequence-based candidate prioritization. Protein language models generate information-rich residue-level representations, but downstream classifiers commonly compress them using fixed pooling operations that may discard task-relevant sequence context. We present ETAP-CLF, a compact framework that combines pretrained per-residue ESM3 embeddings with lightweight transformer contextualization and learned attention pooling to classify variable-length proteins and generate residue-level attention scores. The ESM3 parameters remained frozen, and the same ETAP-CLF architecture and hyperparameter configuration were used across ferroptosis-, senescence-, and pyroptosis-associated protein prediction. ETAP-CLF achieved AUROCs of 0.98, 0.95 and 0.91 for these tasks, respectively. In the ferroptosis benchmark, ETAP-CLF outperformed the evaluated published models. These results demonstrate that a common downstream design can adapt to multiple process-associated classification tasks without fine-tuning the task-specific model architecture. ETAP-CLF provides a generalizable approach for sequence-based protein prioritization and a basis for broader evaluation across binary protein-classification problems.
Jianyu Ren, Hanli Jiang, Puchangxin Li et al.· bioRxiv· 0 citations
Findings show that performance estimates in protein function prediction should be interpreted as outcomes of complete data-centric workflows rather than isolated properties of predictive models.
This study evaluates four encoding schemes—one-hot, k-mer (substring-based encoding), embeddings, and Position-Specific Scoring Matrix (PSSM) using Convolutional Neural Networks (CNNs) and Long Short-Term Memories (LSTMs) and shows that k-mer encoding achieved the highest accuracy.
T. Kurniawan, Deshinta Arova Dewi, Randy Joy Magno Ventayen· Journal of Advances in Infor...· 0 citations
This paper proposes TextDTI, a multimodal framework that simultaneously exploits sequential and structural representations and enhances feature alignment through adversarial learning and contrastive loss, resulting in robust and high-performance DTI prediction.
Jiaqi Deng, Senyu Tang, Ji-Jun Tang et al.· Journal of Chemical Informat...· 0 citations
Experiments show that GraESM-FuseDTA achieves competitive overall performance and consistent advantages in ranking-oriented and variance-explanation metrics across warm start, drug cold start, target cold start, and strict pair cold start settings.
