Gajendra P. S. Raghava

AntiOxiPred: Predicting Antioxidant Peptides using Machine Learning Techniques and BLAST

AntiOxiPred predicts antioxidant peptides using machine learning and BLAST, achieving high AUC and MCC on testing and external validation datasets.

AntiCapt: Fine-Tuned Nucleotide Language Models for Predicting and Designing Anticancer Aptamers

AntiCapt uses fine-tuned nucleotide language models to predict and design anticancer aptamers, achieving high accuracy in distinguishing them from non-aptamers.

EdiProPred: Prediction of Edible Plant Tissue-Associated Proteins Using Large Language Models

The paper develops EdiProPred, a model using large language models to classify plant proteins as edible or non-edible based on sequence data, achieving high accuracy.

AptViralDB: A Repository of Experimentally Validated Antiviral Aptamers

AptViralDB is a manually curated database of 1,768 experimentally validated antiviral aptamers against 40 viruses and 104 targets, offering annotations, predicted structures, and search tools.

CPPLocPred: Subcellular Localization of Cell-Penetrating Peptides

CPPLocPred predicts subcellular localization of cell-penetrating peptides using a hierarchical machine learning framework, achieving high accuracy on validation datasets.

PEPstrMOD2: Next-generation tertiary structure prediction of chemically modified and non-natural peptides

PEPstrMOD2 improves peptide structure prediction by using deep learning and expanding chemical modifications support, outperforming previous methods on benchmark datasets.

AptBacterialDB: A Comprehensive, Manually Curated Database of Antibacterial Aptamers

AptBacterialDB is a manually curated database of antibacterial aptamers with detailed annotations and integration tools to support research and development.

AptCancerDB: A Curated Knowledgebase and Translational Discovery Platform for Anticancer Aptamers

AptCancerDB curates experimentally validated anticancer aptamers, providing a resource for discovery and development of diagnostics and therapeutics.

A PLM-Based Method for Predicting Protein Ion Channel Modulators for Drug Discovery and Safety Evaluation

This paper introduces IonNTXpred, a PLM-based method for predicting ion channel modulators, achieving high accuracy and supporting drug discovery and safety evaluation.

HAIRpred2: Human Host-Specific Prediction of Antibody-Interacting Residues Using Hybrid Physicochemical and Structural Features

HAIRpred2 predicts antibody-interacting residues in human antigens using structural features, achieving an AUC of 0.78 on an independent test set.

An ensemble method for predicting and designing of druggable proteins.

The paper develops an ensemble method combining machine learning and motif analysis to predict druggable proteins, achieving high accuracy on an independent validation dataset.

Integrative Bioinformatics Approach to Identify Prognostic Gene Signatures for Risk Stratification in Thyroid Carcinoma

This study identifies a 20-gene prognostic signature for thyroid cancer risk stratification using transcriptomic data and machine learning, achieving high predictive accuracy.

PCPpred: Prediction of Chemically Modified Peptide Permeability Across Multiple Assays for Oral Delivery

The paper develops PCPpred, a model predicting peptide permeability across assays, achieving high performance on Caco-2 and PAMPA tests using molecular features and ensemble methods.

Hemolytik2: An Updated Database of Hemolytic Peptides and Proteins

Hemolytik2 is a manually curated database of hemolytic and non-hemolytic peptides with detailed annotations and structural information.

Identification of Multiple Prognostic Biomarker Sets for Risk Stratification in SKCM

This paper identifies seven distinct sets of prognostic biomarkers for predicting high-risk SKCM patients using transcriptomics data and machine learning models.

AntiFP2: Genome and Metagenome-Wide Prediction of Antifungal Proteins

AntiFP2 predicts antifungal proteins from genomic and metagenomic data using an ensemble of ESM2, BLAST, and MERCI, achieving high accuracy through cross-validation and offering a scalable pipeline.

Prediction of lncRNA-protein interacting pairs using LLM embeddings based on evolutionary information

The paper predicts lncRNA-protein interacting pairs using embeddings from DNABERT-2 and ESM-2-t30, achieving high accuracy on an independent dataset.

In Silico Tool for Predicting and Scanning Rheumatoid Arthritis-Inducing Peptides in an Antigen

This paper develops models to predict RA-inducing peptides using machine learning, deep learning, and large language models, achieving high AUC scores and implementing them in a web tool.

Prediction of exosomal miRNA-based biomarkers for liquid biopsy

The paper develops models to predict exosomal miRNAs using alignment-based and AI approaches, achieving improved performance with an ensemble method.

NTxPred2: A large language model for predicting neurotoxic peptides and neurotoxins

The paper develops separate models for predicting neurotoxic peptides and neurotoxins using machine learning and protein language models, achieving high AUC scores and outperforming existing methods.

HAIRpred: Prediction of human antibody interacting residues in an antigen from its primary structure

HAIRpred predicts human antibody-interacting residues in antigens using machine learning and features like evolutionary profiles and surface accessibility, achieving an AUC of 0.72.

In silico tool for Predicting, Designing and Scanning IL-2 inducing peptides

The paper develops a tool to predict, design, and scan IL-2-inducing peptides using machine learning, deep learning, and ensemble methods, achieving high accuracy on multiple datasets.

Saliva-based Biomarkers for Predicting Gastric Cancer

This paper identifies a panel of eight salivary exRNA biomarkers for gastric cancer detection using machine learning, achieving high accuracy and outperforming existing biomarker panels.

Designing of thermostable proteins with a desired melting temperature

This paper develops models to predict and design proteins with desired melting temperatures using machine learning and large language models, achieving high accuracy and providing tools for application.

PEPlife2: A Updated Repository of the Half-life of Peptides

PEPlife2 is an updated manually curated database with 4,412 peptide half-life entries, including structural data and advanced analysis tools.

Benchmarking of Quantum SVM and Classical ML Algorithms for Prediction of Therapeutic Proteins

This paper benchmarks quantum SVM and classical ML algorithms for predicting therapeutic proteins, finding CML outperforms QSVM but both show promise in peptide prediction tasks.

AntiCP3: Prediction of Anticancer Proteins Using Evolutionary Information from Protein Language Models

AntiCP3 predicts anticancer proteins using evolutionary information and hybrid methods, achieving an AUC of 0.91.

IL4Pred2: Prediction of Interleukin-4 Inducing Peptides in Human and Mouse

The paper introduces IL4Pred2, a host-specific method for predicting IL-4 inducing peptides in human and mouse, achieving high AUC and MCC scores on independent datasets.

AdmirePred: A method for predicting abundant miRNAs in Exosomes

This paper develops AdmirePred, a method to predict abundant miRNAs in exosomes using machine learning and hybrid approaches, achieving an AUC of 0.854.

A large language model for predicting pancreatic ductal adenocarcinoma patients from blood-derived exosomal transcriptomics data

The paper uses large language models to predict pancreatic ductal adenocarcinoma from gene expression data by converting numerical features into peptide sequences.