Gajendra P.S. Raghava

Pfeature. A software package for computing features of peptides and proteins

toxinpred3. An improved method for predicting toxicity of the peptides and designing of non-toxic peptides

toxinpred2. An improved method for predicting toxicity of proteins

hemopi2. HemoPI2: Prediction of hemolytic activity of peptides against mammalian RBCs

anticp2. AntiCP2 is an updated version of AntiCP developed for predicting, designing and scanning anticancer peptides.

algpred2. A machine learning based method for predicting, scanning and mapping allergenic regions in an allergen

clbtope. An ensemble method for predicting linear and conformational B-cell epitope

pptstab. PPTStab: Designing of thermostable proteins with a desired melting temperature

transfacpred. An ensemble method for predicting transcription factor in protein sequences

ifnepitope2. Prediction of interferon-gamma inducing peptides using alignment-based and alignment-free methods

phagetb. A multi-level prediction of interaction between bacteriophages and pathogenic bacterial hosts

AntiBP3. An improved method for predicting of antibacterial peptides using machine learning yechniques

NTxPred2. NTxPred2: An improved method for predicting neurotoxicity of peptides and proteins

il2pred. Prediction of IL2 inducing peptides

il5pred. Machine learning based method for predicting and scanning IL-5 inducing petides

exopropred. An ensemble method for predicting proteins secreted via exosomes

FluSPred. Prediction infectious strains of Influenza A virus for human

antifp2. Prediction of anti-fungal proteins using protein language models

Hemolytik2. A database of hemolytic and non-hemolytic peptides

PCPpred. A large language model for predicting membrane permeability of chemically modified peptides particularly for cyclic peptides. This method will help to discover novel orally deliverable peptide

anticp3. Prediction of anticancer proteins

EIPpred. EIPpred: Prediction of Inhibitory peptides against E.coli

hairpred. Prediction of Conformational B-cell epitopes in an antigen for human host

thppred. Prediction therapeutic peptides and proteins using machine learning techniques

hoppred. HopPred: A method for scanning peptide hormones in a protein

mutation_bench. Benchmarking of mutation calling techniques by developing classification and regresion prediction models to predict the high-risk cancer patients.

AlzScPred. Identification of Biomarkers of Alzheimer's from Single cell genome

il13pred. A method for predicting cytokine IL-13 inducing peptides

pprint2. An improved method for predicting RNA-interacting residues in a protein

il6pred. In silico model for predicting of Interleukin-6 inducing peptides

afpropred. AfProPred: A tool to predict anti-freezing proteins

cytolncpred. CytoLNCpred: A method for predicting cytoplasm associate lncRNA

cbtope2. CBtope2: An improved method for identification of conformational B-cell epitopes in an antigen

skcm_prognostic_biomarker. Pronostic biomarkers for SKCM

GuideWD. GuideWD: Guide for web development

il4pred2. An updated in silico tool for identification of IL4 Inducing Peptides.

RAIpred. In-silico tool for predicting Rhuematoid arthritis inducing peptides

pdac_pred_llm. Python

emirpred. EmiRPred: A computational approach to predict exosomal and non-exosomal miRNA

nfkbin. A computational approach to predict the NF-kB inhibitors

PlantDRPpred. PlantDRPpred: Prediction of plant resistance proteins

HNSCPred. A computational approach tool to predict Head and Neck Cancer affected patients from their single cell RNA seq data.

CovXpred. Jupyter Notebook

mrslpred. In silico method for predicting subcellular localisation of mRNA sequences

LHSpred. Jupyter Notebook

hladr4pred2. An improved method for predicting binders of HLA-DRB1-04:01

hlancpred. A method for predicting promiscuous non-classical HLA binding sites in an antigen

drderma. Jupyter Notebook

sambinder. A method for predicting SAM interacting residues in a protein

raghavagps.github.io. Home Page of Prof G P S Raghava, group work in the field of bioinformatics, chemoinformatics and pharmacoinformatics. Mainly developed databases and prediction methods using machine learning techniques

Cancer_Review. This site provides complete information on a review written on cancer resoureces

nagbinder. A method for predicting NAG interacting residues in a protein from its primary sequence

PLifePred. PlifePred: In Silico Prediction of Peptide Half-Life in Blood