arXiv AI
Jun 17
Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3
arXiv:2606. 17127v1 Announce Type: cross Abstract: Antimicrobial resistance causes to over a million deaths annually.
OmegAMP: Targeted AMP Discovery via Biologically Informed Generation
arXiv:2504. 17247v3 Announce Type: replace Abstract: Deep learning-based antimicrobial peptide (AMP) discovery faces critical challenges such as limited controllability, lack of representations that efficiently model antimicrobial properties, and low experimental hit rates.
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Hugging Face Trending Papers
Aug 31
Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling
Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than binary antimicrobial classification. The ESCAPE...
arXiv Machine Learning
Sep 1
Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling
The study demonstrates that a simple, sequence-only approach using 330 interpretable descriptors and the TabPFN tabular foundation model can outperform complex multimodal deep learning methods for multi-label antimicrobial peptide activity prediction. On the ESCAPE benchmark (82,359 peptides, five labels), a label‑powerset TabPFN model achieved a mean average precision of 77.8%, surpassing the previous best of 72.1%. The approach also shows that predicted structure is unnecessary, that a small set of global physicochemical scalars can recover most performance, and that modeling label dependence benefits rare activities and informs assay prioritization.
arXiv AI
2d ago
Pepti-drift: Scalable Safe-Active Peptide Generation Without Inference-Time Guidance
arXiv:2606.27824v3 Announce Type: replace-cross Abstract: Therapeutic peptides are a promising drug modality, but their generation must satisfy multiple therapeutic constraints. We introduce BindSafe...
arXiv Machine Learning
Sep 3
Freeze, Diffuse, Decode: Task-Aware Adaptation of Transformer Embeddings for Antimicrobial Peptide Design
Freeze, Diffuse, Decode (FDD) is a diffusion-based framework that adapts pre‑trained transformer embeddings to downstream tasks while preserving their geometric structure. By propagating supervised signals along the intrinsic manifold of frozen embeddings, FDD produces low‑dimensional, predictive, and interpretable representations. In antimicrobial peptide design, these representations support property prediction, retrieval, and latent‑space interpolation.
arXiv Machine Learning
Sep 2
Freeze, Diffuse, Decode: Geometry-Aware Adaptation of Pretrained Transformer Embeddings for Antimicrobial Peptide Design
arXiv:2511.23120v2 Announce Type: replace Abstract: Pretrained transformers provide rich, general-purpose embeddings, which are transferred to downstream tasks. However, current transfer strategies:...
arXiv AI
Aug 18
RetroMPA: A Molecular Property-Aware Auxiliary Framework for Enhancing Retrosynthesis Prediction
arXiv:2608. 16111v1 Announce Type: cross Abstract: Retrosynthesis is a cornerstone of drug discovery and organic synthesis.
arXiv AI
Sep 17
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
BOOM is a new benchmark for evaluating out‑of‑distribution (OOD) molecular property predictions in machine learning. It provides chemically‑informed tests across common property prediction tasks and assesses over 150 model‑task combinations. The study shows that current models, including chemical foundation models, struggle to generalize OOD, with the best model still exhibiting three times higher error than in‑distribution predictions.
arXiv AI
Jul 24
Leveraging Biokinetic Knowledge Priors for Data-Scarce Bioprocess Modeling
arXiv:2607. 20539v1 Announce Type: cross Abstract: While deep learning has accelerated drug discovery, its impact on biomanufacturing has been considerably more limited.
arXiv AI
Jun 2
Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling
arXiv:2606. 01220v1 Announce Type: cross Abstract: Generating molecules that simultaneously satisfy drug-like properties and conform to the 3D structure of a target protein is a core challenge in structure-based drug design (SBDD).
arXiv Machine Learning
Aug 4
Refine Drugs, Don't Complete Them: Uniform-Source Discrete Flows for Fragment-Based Drug Discovery
arXiv:2509. 26405v2 Announce Type: replace Abstract: We introduce InVirtuoGen, a discrete flow generative model for fragmented SMILES for de novo and fragment-constrained generation, and target-property/lead optimization of small molecules.
arXiv AI
Sep 2
MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery
arXiv:2603.03517v2 Announce Type: replace-cross Abstract: General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and perfor...