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• Protein Structure Prediction
  crossref Qingfeng Chen 2024-04-25T11:01:47Z 置信度 0.70
• Protein–ligand structure prediction
  crossref Rita Strack 2024-04-12T16:02:51Z 置信度 0.70
• Protein Structure Prediction and Analysis
  crossref Luisa Di Paola 2024-06-03T21:01:36Z 置信度 0.70
• Accurate Prediction of Protein-Binding Residues in Protein Sequences Using SCRIBER
  Deciphering molecular-level mechanisms that govern protein-protein interactions (PPIs) relies in part on the accurate prediction of protein-binding partners and protein-binding residues. These predictions can be used to support a wide spectrum of applications …
  crossref Jian Zhang, Feng Zhou, Xingchen Liang, Lukasz Kurgan 2024-11-22T10:42:07Z 置信度 0.70
• Structural Bioinformatics and Protein Structure Prediction
  crossref Kavita Patel, Ashutosh Mani 2024-09-21T16:02:02Z 置信度 0.70
• Structure prediction of alternative protein conformations
  Abstract Proteins are dynamic molecules whose movements result in different conformations with different functions. Neural networks such as AlphaFold2 can predict the structure of single-chain proteins with conformations most likely to exist in the PDB. Howeve…
  crossref Patrick Bryant, Frank Noé 2024-08-26T06:02:23Z 置信度 0.70
• From Sequence to Structure to Function: De Novo Protein Design, the Role of AI and Structure Prediction Neural Networks
  Recent advancements in artificial intelligence (AI) and deep learning have revolutionized the field of protein engineering, particularly in the area of de novo protein design. This review article explores the impact of AI-driven approaches on protein design, w…
  crossref Marcelo Kauffman 2024-04-03T04:11:57Z 置信度 0.70
• Improved prediction of site‐rates from structure with averaging across homologs
  Abstract Variation in mutation rates at sites in proteins can largely be understood by the constraint that proteins must fold into stable structures. Models that calculate site‐specific rates based on protein structure and a thermodynamic stability model have …
  crossref Christoffer Norn, Fábio Oliveira, Ingemar André 2024-06-25T04:16:24Z 置信度 0.70
• Prediction and Evaluation of Protein Aggregation with Computational Methods
  Protein and peptide aggregation has recently become one of the most studied biomedical problems due to its central role in several neurodegenerative disorders and of biotechnological importance. Multiple in silico methods, databases, tools, and algorithms have…
  crossref Mubashir Hassan, Saba Shahzadi, Mai Suan Li, Andrzej Kloczkowski 2024-11-22T10:42:33Z 置信度 0.70
• DescribePROT Database of Residue-Level Protein Structure and Function Annotations
  DescribePROT is a freely available online database of structural and functional descriptors of proteins at the amino acid level. It provides access to 13 diverse descriptors that include sequence conservation, putative secondary structure, solvent accessibilit…
  crossref Bi Zhao, Sushmita Basu, Lukasz Kurgan 2024-11-22T05:44:18Z 置信度 0.70
• Prediction of Oligomeric Status of Quaternary Protein Structure by using Sequential Minimal Optimization for Support Vector Machine
  Abstract The prediction of quaternary structure and its function is important in pharmaceutical applications, drug design, medical or engineering applications. With the drastic rise in the number of protein sequences presented to the public database, acquiring…
  crossref Suresh Kumar 2024-08-17T19:20:13Z 置信度 0.70
• Creators of AI protein-structure prediction win chemistry Nobel
  The 2024 Nobel Prize for Chemistry has been awarded to David Baker, Demis Hassibis and John Jumper for their work on protein structure and design.
  crossref Hamish Johnston 2024-12-10T11:20:30Z 置信度 0.70
• Computational Prediction of Linear Interacting Peptides
  Intrinsically disordered protein regions, IDRs, are observed in many eukaryotic proteins. They play critical roles in essentially all cellular processes because segments of these regions, known as linear interacting peptides (LIPs), are heavily involved in reg…
  crossref Nawar Malhis, Jörg Gsponer 2024-11-22T05:44:53Z 置信度 0.70
• Peer Review #3 of "Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential (v0.1)"
  Potential Natural Vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location if not impacted by human activities.PNV is useful for raising public awareness about land degradation and for estimating land potential…
  crossref 2018-08-27T02:31:45Z 置信度 0.70
• Peer Review #1 of "Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential (v0.1)"
  Potential Natural Vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location if not impacted by human activities.PNV is useful for raising public awareness about land degradation and for estimating land potential…
  crossref 2018-08-27T02:31:57Z 置信度 0.70
• Exploring the Potential of Machine Learning in Agriculture: A Review of its Applications and Results
  This review paper provides an overview of the applications of machine learning in the agriculture field. Machine learning, a subfield of artificial intelligence, has been successfully applied to various domains, and agriculture is no exception. The paper start…
  crossref Barkha Bhardwaj, Shivam Tiwari 2023-02-22T11:39:47Z 置信度 0.70
• Review for "Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals"
  crossref 2022-08-08T09:04:55Z 置信度 0.70
• Mapping Almond Stem Water Potential using Machine Learning
  Abstract Almonds are a major crop in California which produces 80% of all the world’s almonds. Widespread drought and strict groundwater regulations pose significant challenges to growers. Irrigation regimes based on observed crop water status can help to opti…
  crossref Peter Savchik, Mallika Nocco, Isaya Kisekka 2023-03-30T22:15:28Z 置信度 0.70
• Review for "Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals"
  crossref 2022-08-08T09:04:55Z 置信度 0.70
• Protein structure prediction in the era of AI: challenges and limitations when applying to <i>in-silico</i> force spectroscopy
  Mechanoactive proteins are essential for a myriad of physiological and pathological processes. Guided by the advances in single-molecule force spectroscopy (SMFS), we have reached a molecular-level understanding of how several mechanoactive proteins respond to…
  preprints 2022 置信度 0.74
• Protein Structure Prediction Using a Maximum Likelihood Formulation of a Recurrent Geometric Network
  Only ∼40% of the human proteome has structural coordinates available from experiment ( i . e ., X-ray crystallography, NMR spectroscopy, or cryo-EM) or homology modeling with quality templates ( i . e ., 30% sequence identity or greater), leaving most of the p…
  preprints 2021 置信度 0.74
• Construct a variable-length fragment library for <i>de novo</i> protein structure prediction
  Although remarkable achievements, such as AlphaFold2, have been made in end-to-end structure prediction, fragment libraries remain essential for de novo protein structure prediction, which can help explore and understand the protein-folding mechanism. In this …
  preprints 2022 置信度 0.74
• Decoding microbiome and protein family linkage to improve protein structure prediction
  Information extracted from microbiome sequences through deep-learning techniques can significantly improve protein structure and function modeling. However, the model training and metagenome search were largely blind with low efficiency. Built on 4.25 billion …
  preprints 2021 置信度 0.74
• Protein language model embeddings for fast, accurate, alignment-free protein structure prediction
  All state-of-the-art (SOTA) protein structure predictions rely on evolutionary information captured in multiple sequence alignments (MSAs), primarily on evolutionary couplings (co-evolution). Such information is not available for all proteins and is computatio…
  preprints 2021 置信度 0.74
• Single-sequence protein structure prediction using language models from deep learning
  ABSTRACT AlphaFold2 and related systems use deep learning to predict protein structure from co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite dramatic, recent increases in accuracy, three challenges remain: (i) prediction of…
  preprints 2021 置信度 0.74
• Course-based Undergraduate Research Module for Enzyme Discovery Using Protein Structure Prediction
  A major challenge for science educators is teaching foundational concepts while introducing their students to current research. Here we describe an active learning module developed to teach protein structure fundamentals while supporting ongoing research in en…
  preprints 2021 置信度 0.74
• A Sequential Niche Multimodal Conformation Sampling Algorithm for Protein Structure Prediction
  Motivation Massive local minima on the protein energy surface often causes traditional conformation sampling algorithms to be easily trapped in local basin regions, because they are difficult to stride over high-energy barriers. Also, the lowest energy conform…
  preprints 2020 置信度 0.74
• Pseudocontact shift NMR data obtained from a non-canonical amino acid-linked lanthanide tag improves integral membrane protein structure prediction
  A single experimental method alone often fails to provide the resolution, accuracy, and coverage needed to model integral membrane proteins (IMPs). Integrating computation with experimental data is a powerful approach to supplement missing structural informati…
  preprints 2022 置信度 0.74
• Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction
  Among a wide variety of mass spectrometry (MS) methodologies available for structural characterizations of proteins, ion mobility (IM) provides structural information about protein shape and size in the form of an orientationally averaged collision cross-secti…
  preprints 2021 置信度 0.74
• De novo protein structure prediction by incremental inter-residue geometries prediction and model quality assessment using deep learning
  Motivation The successful application of deep learning has promoted progress in protein model quality assessment. How to use model quality assessment to further improve the accuracy of protein structure prediction, especially not reliant on the existing templa…
  preprints 2022 置信度 0.74
• ROPIUS0: A deep learning-based protocol for protein structure prediction and model selection and its performance in CASP14
  Protein structure prediction has recently been revolutionized when AlphaFold2 [1] predicted protein structures with near-experimental accuracy in the latest CASP14 season of critical assessment of methods of protein structure prediction (CASP). Among numerous …
  preprints 2021 置信度 0.74
• MULTICOM2: an open-source protein structure prediction system powered by deep learning and distance prediction
  Protein structure prediction is an important problem in bioinformatics and has been studied for decades. However, there are still few open-source comprehensive protein structure prediction packages publicly available in the field. In this paper, we present our…
  preprints 2021 置信度 0.74
• MMpred: a distance-assisted multimodal conformation sampling for de novo protein structure prediction
  Motivation The mathematically optimal solution in computational protein folding simulations does not always correspond to the native structure, due to the imperfection of the energy force fields. There is therefore a need to search for more diverse suboptimal …
  preprints 2021 置信度 0.74
• Improved protein structure prediction by deep learning irrespective of co-evolution information
  We describe our latest study of the deep convolutional residual neural networks (ResNet) for protein structure prediction, including deeper and wider ResNets, the efficacy of different input features, and improved 3D model building methods. Our ResNet can pred…
  preprints 2020 置信度 0.74
• Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterised proteins
  Deep learning-based prediction of protein structure usually begins by constructing a multiple sequence alignment (MSA) containing homologues of the target protein. The most successful approaches combine large feature sets derived from MSAs, and considerable co…
  preprints 2020 置信度 0.74
• ProALIGN: Directly learning alignments for protein structure prediction via exploiting context-specific alignment motifs
  Template-based modeling (TBM), including homology modeling and protein threading, is one of the most reliable techniques for protein structure prediction. It predicts protein structure by building an alignment between the query sequence under prediction and th…
  preprints 2020 置信度 0.74
• Improved protein structure prediction using predicted inter-residue orientations
  The prediction of inter-residue contacts and distances from co-evolutionary data using deep learning has considerably advanced protein structure prediction. Here we build on these advances by developing a deep residual network for predicting inter-residue orie…
  preprints 2019 置信度 0.74
• CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction
  Protein functions are largely determined by the final details of their tertiary structures, and the structures could be accurately reconstructed based on inter-residue distances. Residue co-evolution has become the primary principle for estimating inter-residu…
  preprints 2020 置信度 0.74
• Protein structure prediction and design in a biologically-realistic implicit membrane
  ABSTRACT Protein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. While soluble protein design has advanced, membrane protein design remains challenging due to difficulties in modeling the …
  preprints 2019 置信度 0.74
• A <i>de novo</i> protein structure prediction by iterative partition sampling, topology adjustment, and residue-level distance deviation optimization
  Motivation With the great progress of deep learning-based inter-residue contact/distance prediction, the discrete space formed by fragment assembly cannot satisfy the distance constraint well. Thus, the optimal solution of the continuous space may not be achie…
  preprints 2021 置信度 0.74
• Analysis of distance-based protein structure prediction by deep learning in CASP13
  This paper reports the CASP13 results of distance-based contact prediction, threading and folding methods implemented in three RaptorX servers, which are built upon the powerful deep convolutional residual neural network (ResNet) method initiated by us for con…
  preprints 2019 置信度 0.74
• Protein structure prediction using sparse NOE and RDC restraints with Rosetta in CASP13
  Computational methods that produce accurate protein structure models from limited experimental data, e.g. from nuclear magnetic resonance (NMR) spectroscopy, hold great potential for biomedical research. The NMR-assisted modeling challenge in CASP13 provided a…
  preprints 2019 置信度 0.74
• Sequence alignment using machine learning for accurate template-based protein structure prediction
  Motivation Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful if good templates can be found. Although modern homology detection methods can find remote homologs with high se…
  preprints 2019 置信度 0.74
• Prot-LAMBDA: Explicit Distance Learning Enhances Structural Reasoning in Protein Language Models
  Protein language models (PLMs) learn evolutionary information from large-scale sequence data, but three-dimensional relationships are encoded only implicitly. Here, we introduce Prot-LAMBDA (Protein LAnguage Model Boosted with Distance Awareness), a PLM that e…
  preprints 2026 置信度 0.74
• WaterFlow: Prediction of Ordered Water Molecule Positions on Protein Structures
  Ordered water molecules mediate many protein functions including stability, ligand binding, and catalysis. Predicting their positions with sub-angstrom accuracy would support protein design, binding affinity prediction, and automated model building in X-ray cr…
  preprints 2026 置信度 0.74
• Large-scale structure prediction of DUF-containing protein-protein interactions
  Motivation Continuing advances in genome and metagenome sequencing expand the number of identified conserved protein families that remain functionally uncharacterized and contain domains of unknown function (DUFs). Functional-association resources such as STRI…
  preprints 2026 置信度 0.74
• HDOCK-Multimer: integrating docking and combinatorial assembly for structure prediction of large protein complexes
  Deep learning methods, such as AlphaFold and RosettaFold, achieve high accuracy in protein structure prediction. However, predicting the structure of large protein complexes remains challenging due to their large size and intricate multi-chain interactions. Do…
  preprints 2026 置信度 0.74
• Mavchen 1: A Conformational Ensemble Platform for Protein Ligand Pose Prediction That Substantially Outperforms Static Structure Prediction in a Category-Stratified Benchmark
  Deep learning structure predictors, most prominently AlphaFold2 (the field-standard tool benchmarked against throughout this study), have substantially expanded access to protein structural information, yet characteristically return a single static conformatio…
  preprints 2026 置信度 0.74
• AlphaConformers: Structure-guided sampling enables prediction of multiple protein conformations
  Proteins are dynamic molecules capable of adopting multiple conformations. However, AlphaFold2 predominantly generates models around a single conformation, usually representing a ligand-bound state. To address this limitation, we developed AlphaConformers, a s…
  preprints 2026 置信度 0.74
• AntiSite: Modality Dropout Enables Antibody Paratope Prediction With or Without Structure From a Single Model
  Summary Reliable paratope identification is central to understanding antibody–antigen recognition and advancing therapeutic antibody discovery. AntiSite is a unified antibody paratope prediction framework that combines protein-language-model sequence embedding…
  preprints 2026 置信度 0.74
• ProtBLIP2-SST: Protein Function Prediction via BLIP2 with Sequence, Structure, and Text
  Protein function prediction traditionally relies on structured gene ontology (GO) labels or multi-label classifiers. However, these labels or classifiers cannot flexibly describe molecular function, biological process, cellular component, and free-text functio…
  preprints 2026 置信度 0.74
• Beyond Chemical Similarity: Structure-Agnostic Drug–Drug Interaction Prediction with MeSH Semantics and a Drug–Target–Protein Knowledge Graph
  Background Adverse drug–drug interactions (DDIs) cause preventable hospitalizations, but exhaustive experimental screening of all drug pairs is infeasible. Many computational predictors rely on SMILES or other molecular representations, limiting their direct a…
  preprints 2026 置信度 0.74
• A discrete protein subset drives structure prediction discordance in orphan proteins
  Structure and disorder predictors are increasingly used as decision-grade tools in protein engineering and in the analysis of newly emerged proteins, yet how the current state-of-the-art behaves on sequences outside the well-charted evolutionary space remains …
  preprints 2026 置信度 0.74
• ppigFinder: an integrated desktop application for bacterial genome annotation and AlphaFold 3-based protein–protein interaction screening
  Motivation AlphaFold-based structure prediction has transformed structural biology by enabling accurate protein modelling and providing a powerful framework for inferring protein-protein interactions (PPIs). However, discovering candidate PPIs directly from ge…
  preprints 2026 置信度 0.74
• De <i>novo</i> protein structure prediction using ultra-fast molecular dynamics simulation
  ABSTRACT Modern genomics sequencing techniques have provided a massive amount of protein sequences, but experimental endeavor in determining protein structures is largely lagging far behind the vast and unexplored sequences. Apparently, computational biology i…
  preprints 2018 置信度 0.74
• EpiTune: An Accurate Epitope Prediction Model with Mechanistic Insights
  Summary The accurate prediction of b-cell epitopes facilitates vaccine development by identifying known antibodies for an antigen. Multiple epitope prediction models use protein language models to enable more accurate predictions with modest results. Here, we …
  preprints 2026 置信度 0.74
• Structure-verified graph attention networks for residue-level prediction of antibody-contact escape mutations in the SARS-CoV-2 spike protein
  Abstract Predicting which SARS-CoV-2 spike mutations enable antibody escape requires training labels whose biological relevance can be independently verified. We built a two-stage pipeline linking 905 Indian SARS-CoV-2 genome sequences to solved antibody-spike…
  preprints 2026 置信度 0.74
• INDELVAR: structure-informed prediction of in-frame indel pathogenicity with calibrated PP3/BP4 thresholds
  In-frame insertions and deletions are difficult to interpret because their effects depend on both the sequence change and its protein context. We developed INDELVAR, a random forest model for in-frame insertions and deletions of 1-10 amino acids that integrate…
  preprints 2026 置信度 0.74
• Deep learning-guided identification of bacteriophage receptor-binding protein candidates for foodborne pathogen detection
  Foodborne pathogens including Salmonella spp., Escherichia coli and Listeria monocytogenes cause an estimated 600 million illnesses annually. Yet conventional detection methods remain slow, costly, or insufficiently specific for routine food safety surveillanc…
  preprints 2026 置信度 0.74
• Pi-Ensemble: Sequence-guided generation of interpolated protein conformational ensembles
  Proteins are critical biomolecular machines that populate ensembles of interconverting conformations. Many biological processes depend on transitions between metastable states. Although molecular dynamics (MD) simulations provide a physically grounded route to…
  preprints 2026 置信度 0.74
• PathFold: Predicting the Entire Protein Folding Pathway from Protein Sequence Alone
  Recent advances in protein structure prediction, exemplified by AlphaFold, have largely addressed the determination of static structures, one aspect of the protein folding problem. However, predicting folding pathways, by which proteins reach their native stat…
  preprints 2026 置信度 0.74
• Accurate <i> ΔT _m </i> Prediction Without Protein Structure Inputs for Biomolecular Stability
  Predicting protein stability, like changes in melting temperature ( ΔT m ) caused by mutations, is a critical task in therapeutic protein engineering and drug discovery. This is reflected by a growing solution space, including both AI-based sequence and struct…
  preprints 2026 置信度 0.74
• A generalisable framework to inject distance information into Alphafold-like structure predictors
  Structure prediction methods are now highly successful at predicting three-dimensional structures from sequence. However, it is still often desirable to supplement these methods with additional external priors on pairwise distances in the structures. We presen…
  preprints 2026 置信度 0.74
• A novel benchmark dataset for enzyme function prediction reveals the limitations of state-of-the-art models
  Accurate computational prediction of enzyme function, standardized by Enzyme Commission (EC) numbers, is essential for large-scale genome annotation and generative enzyme design. However, it remains unclear whether state-of-the-art predictors learn the intrins…
  preprints 2026 置信度 0.74
• A Glycan-Aware Diffusion Model for Carbohydrate and Glycoprotein Structure Prediction
  Biomolecular diffusion models can now predict proteins and heterogeneous complexes, but glycans remain difficult because their branched topology, conformational flexibility, and strict stereochemical rules must be captured simultaneously. We developed SweetFol…
  preprints 2026 置信度 0.74
• ConfDock: Atom-specific Uncertainty Quantification for Molecular Docking via Conformal Prediction
  Molecular docking is widely used in structure-based drug discovery, yet most approaches provide point estimates without rigorous uncertainty quantification. This limitation makes it difficult to assess when a predicted pose should be trusted, especially when d…
  preprints 2026 置信度 0.74
• Modelling antibody structures at the speed of language
  Protein structure prediction is currently substantially slower than obtaining sequence representations of proteins. This leads to most property prediction methods relying solely on trivial or learned sequence embeddings. However, contemporary structure predict…
  preprints 2026 置信度 0.74
• Prot2Prop: Structure-informed multitask protein property prediction
  Protein engineering often relies on separate models for related developability properties, limiting efficiency and transfer across tasks. We present Prot2Prop, a multitask framework based on a frozen ProstT5 encoder with shared and task-specific adapters for j…
  preprints 2026 置信度 0.74
• HInt: interaction-based homology discovery through accelerated genome-scale AlphaFold screening
  ABSTRACT Identifying homologous proteins across deep evolutionary distances remains a major challenge because sequence and structural similarity progressively become undetectable over time. Although protein-protein interactions (PPIs) are often constrained by …
  preprints 2026 置信度 0.74
• Multi-Peptide Prompting Enables In-Context Learning in Protein Language Models
  A bstract Protein language models (PLMs) are trained primarily on individual protein sequences, yet many peptide-discovery problems require inference from only a small number of labeled examples. Here, we show that single-sequence PLMs can perform in-context p…
  preprints 2026 置信度 0.74
• PINPOINT: Protease INhibitor PredictiOn at the plant–pathogen INTerface using protein language models and structural modeling
  Summary Cysteine and serine proteases act as an immune hub in the plant apoplast to provide robust extracellular immunity during microbial colonisation. Microbial pathogens counteract these immune proteases by inhibiting their activity using small secreted pro…
  preprints 2026 置信度 0.74
• ProtPen combines sequence- and structure-based approaches to facilitate protein function predictions on a proteome-wide scale
  Proteins of unknown function represent a significant gap in our understanding of biological processes, encompassing large portions of the proteomes of many organisms, especially prokaryotes. Addressing this gap is critical to understanding the biology and path…
  preprints 2026 置信度 0.74
• MHChron: diversity-balanced dataset design for robust peptide–MHC binding prediction across MHC class I and II
  Accurate prediction of peptide–MHC (pMHC) binding is central to immunogenicity assessment, yet many existing predictors are trained and evaluated on narrow allele sets and restricted peptide-lengths. Here, we present MHChron, a unified pMHC binding prediction …
  preprints 2026 置信度 0.74
• ProtEnrich: Residual Multimodal Enrichment of Protein Sequence Embeddings
  Protein language models effectively capture evolutionary and functional signals from sequence data but lack explicit representation of the biophysical properties that govern protein structure and dynamics. Existing multimodal approaches attempt to integrate su…
  preprints 2026 置信度 0.74
• OmniScore: Universal Scoring of Diverse Biomolecular Complexes via Equivariant Geometry-Aware Discrete Representation Learning
  A bstract Scoring biomolecular complexes is central to structure assessment and drug discovery, yet the complexes themselves vary widely in pose, size, and molecular composition. A scoring function tuned for one interaction type rarely carries over to another,…
  preprints 2026 置信度 0.74
• EpiESM-GA: Resource-Efficient Protein Foundation Model Features for Equitable B-Cell Epitope Prediction
  Prediction of B-cell epitopes can assist in reducing costly wet-lab screening in vaccine design, diagnostics, and antibody discovery. However, current predictors often suffer from noisy labels, weak generalization, and structure-dependent workflows. Here we pr…
  preprints 2026 置信度 0.74
• Evaluating Graph Neural Network Architectures for Multi-Omics Cancer Subtyping using Methylation and Gene Expression Profiles
  Motivation Graph Neural Networks (GNNs) have gained increasing interest in the biomedical domain, as the integration of prior knowledge and deep neural networks has the potential to enhance insights into molecular processes and disease mechanisms. However, a c…
  preprints 2026 置信度 0.74
• Benchmarking confidence estimation and rescoring for cyclic peptide–protein complex predictions
  Cyclic peptides are a rapidly expanding class of therapeutics, but the reliability of deep-learning structure prediction for cyclic peptide–protein complexes has not been systematically evaluated. We assembled a curated benchmark of 111 non-redundant complexes…
  preprints 2026 置信度 0.74
• LLM-H2G: biomedical semantic-enhanced hypergraph contrastive learning for herb--disease association prediction
  Abstract Background : Herb–disease association prediction is central to computational traditional medicine, but existing graph and hypergraph methods mainly rely on observed topology and underuse biomedical textual semantics, especially in heterogeneous or spa…
  preprints 2026 置信度 0.74
• Benchmarking Deep Learning Predictions of Mutation-Induced Fold Switching
  Many proteins are known to adopt multiple distinct folded states which are often associated with key functional behavior. A predictive understanding of the properties of such fold-switching or metamorphic proteins can provide insights into protein dynamics and…
  preprints 2026 置信度 0.74
• SQUARNA: stem maximization for accurate de novo RNA secondary structure prediction
  ABSTRACT Non-coding RNAs play diverse roles in a wide range of cellular processes, with their spatial structure being pivotal to their function. RNA secondary structure is a key determinant of its overall fold. Given the scarcity of experimentally determined R…
  preprints 2026 置信度 0.74
• XL-MS–Guided Structure Prediction of Disordered <i>Encephalitozoon hellem</i> Proteins
  Microsporidia such as Encephalitozoon hellem are obligate intracellular human parasites that remain genetically intractable, limiting functional characterization of their proteomes. Structural studies based on homology-based modeling and the use of deep learni…
  preprints 2026 置信度 0.74
• Proteome-Scale Mining and Multi-Objective Prioritization of Encrypted Antimicrobial Peptides with Experimental Validation
  Encrypted antimicrobial peptides (eAMPs) are bioactive fragments embedded within larger proteins and represent an underexplored source of antimicrobial candidates. We developed a multi-layer proteome-mining framework to identify and prioritise eAMPs from 95%-i…
  preprints 2026 置信度 0.74
• One of the Above, None of the Above: Why the Same Model Should Not Predict Both
  Abstract Novelty is often modeled as the probability that the next observation opens a new category. However, experimental setups often leave implicit the fact that novelty judgements are performed within a context that makes such judgements meaningful.This pa…
  preprints 2026 置信度 0.74
• ProtAug: An Empirical Investigation of pLM-Guided Data Augmentation for Protein Sequence Prediction Tasks
  Protein language models (pLMs) offer great potential for protein sequence analysis, yet the scarcity of labeled data often limits their effectiveness in fine-tuning. Data augmentation is a promising remedy, but systematic evaluation of augmentation strategies …
  preprints 2026 置信度 0.74
• Capabilities, specificity gaps and training-data dependence of AlphaFold3 across diverse application areas
  Structure prediction models have moved from single proteins to assemblies that include diverse biomolecules and their modifications. AlphaFold3 (AF3) and related models extended structural modelling via an all-atom framework, opening many new potential applica…
  preprints 2026 置信度 0.74
• Folding the unfoldable 2: using AlphaFold and ESMFold to explore spurious proteins
  Motivation Spurious protein sequences, resulting from gene prediction errors, theoretically should not yield folded structures. AlphaFold2 was previously shown to predict short spurious sequences with high pLDDT scores and was therefore unlikely to distinguish…
  preprints 2026 置信度 0.74
• Bioinf-Farma: supervised integration of epitope prediction and recombinant protein developability for automated vaccine candidate prioritization
  Vaccine antigen discovery requires prioritizing protein candidates according to both immunogenic potential and recombinant expression feasibility. These properties are typically evaluated using separate computational tools, requiring researchers to integrate h…
  preprints 2026 置信度 0.74
• VelocityFM: Short-Horizon Protein Trajectory Prediction via Flow Matching in Velocity Space
  Protein dynamics is fundamentally a trajectory prediction problem, but molecular dynamics (MD) simulation remains expensive and static structure predictors do not model time-ordered motion. We present VelocityFM , a short-horizon protein trajectory predictor t…
  preprints 2026 置信度 0.74
• ProtAff: Protein Binding Affinity Prediction via LoRA-Finetuned ESM-2
  Predicting the binding affinity of protein–protein interactions remains a central challenge in computational biology. Structure prediction models such as AlphaFold3 (AF3) and Boltz-2 can produce high-quality docking poses, and their confidence scores indicate …
  preprints 2026 置信度 0.74
• Whole-proteome structure/function prediction in Uropathogenic <i>E. coli</i> reveals previously missed host-microbe and microbe-phage interaction pathways
  ABSTRACT The rapid advancement of high-throughput sequencing technologies has vastly increased the number of known protein sequences, but the experimental characterization of their structures and functions lags behind. This gap in knowledge impedes our underst…
  preprints 2026 置信度 0.74
• Automated dataset integrating structural and bioactivity data for structure-based drug discovery
  Abstract Machine learning for structure-based drug design needs large datasets linking protein-ligand structures to quantitative bioactivity values. DockTData is an openly licensed structural bioactivity dataset that links experimentally determined protein-lig…
  preprints 2026 置信度 0.74
• AFilter: Improved Antibody Epitope Prediction by Machine Learning-Optimized Interface Energy Filtering of AlphaFold3-Predicted Complex
  AlphaFold3 (AF3) predicts protein-complex structures from sequence with near-experimental accuracy on many targets, substantially lowering the cost of mechanistic and therapeutic discovery. However, application to antibody epitope prediction is hampered by an …
  preprints 2026 置信度 0.74
• Peptide:MHC Binding Stability Prediction Using Protein Language Models
  A bstract Peptide:MHC class I (pMHC-I) binding stability governs the persistence of antigenic complexes at the cell surface and plays a key role in facilitating downstream immunological signals such as antigen presentation, T-cell activation, and immunodominan…
  preprints 2026 置信度 0.74
• An Evaluation Framework of Biomolecular Energetics Learned by Predictive Models
  Abstract Deep learning has revolutionized protein structure prediction, with function prediction on the horizon. Biomolecular properties, including structure and all aspects of function, emerge from atomic-level interactions and the probabilities of their form…
  preprints 2026 置信度 0.74
• GNMCADS: Sampling For Protein Conformation Diversity With Gaussian Network Model Guided Condition Annealed Diffusion Sampler
  Proteins are dynamic molecules existing in diverse conformational states underlying their biological functions. Although recent approaches have enabled diverse conformational sampling by emulating molecular dynamics simulations, perturbing evolutionary informa…
  preprints 2026 置信度 0.74
• MDFitML: Using machine learning to predict potency from molecular simulations
  Abstract Molecular dynamics (MD) simulations characterize ligand-protein conformational dynamics and can provide a strong structural rationale for measured potency changes accompanying ligand modifications. We present an automated workflow that streamlines not…
  preprints 2026 置信度 0.74
• Predicting the Stability of Biologics from Protein Structure and Kinetics
  Abstract Predicting the stability of biologics remains a critical challenge due to the multiple degradation pathways involved in the kinetics of biologics and the inherent complex relationship between protein structure and dynamics. A strict validation process…
  preprints 2026 置信度 0.74
• DDI_single: Single-Sequence-Based Protein Domain Assembly
  Domains are the basic units of protein structure and function. Appropriate inter-domain organization is critical to enable cooperative execution of multiple related functions. It is thus a crucial step to determine the full-length structure of multi-domain pro…
  preprints 2026 置信度 0.74
• SAPPTree: Identification of an S-Acylation Motif Drives a Novel S-Acylation Prediction Program
  S-acylation, the reversible addition of fatty acids to proteins, has emerged as an abundant post-translational modification that drives protein localization and function. With no known consensus sequence, current prediction programs rely on machine learning al…
  preprints 2026 置信度 0.74