Publication Projects

Research Projects

Industry Projects

Govt. Projects

Degree Based

Seasonal / Placement Oriented

Field Based

Mixed / Hybrid

Lab Works

Field Works

Legal & Ethical

Industrial

Corporate

Startup

Academic

Seasonal

Lab Techniques

Computational & Bioinformatics

Clinical & Medical

Industrial & Manufacturing

Regulatory & Compliance

Data Analysis & Statistics

Software & Digital Tools

Seasonal

Hands-On Lab Workshops

Computational & In-Silico Workshops

Faculty Development Programmes

Student Skill Workshops

Industry-Academia Workshops

Conference Workshops & Pre-Symposia

Certification Workshops

Seasonal

PhD Registration Assistance

PhD Practical Assistance

PhD Publication Assistance

PhD Thesis Assistance

ASCEND BY NTHRYS

Publication Projects

Research Projects

Industry Projects

Govt. Projects

Degree Based

Seasonal / Placement Oriented

Field Based

Mixed / Hybrid

Lab Works

Field Works

Legal & Ethical

Industrial

Corporate

Startup

Academic

Seasonal

Lab Techniques

Computational & Bioinformatics

Clinical & Medical

Industrial & Manufacturing

Regulatory & Compliance

Data Analysis & Statistics

Software & Digital Tools

Seasonal

Hands-On Lab Workshops

Computational & In-Silico Workshops

Faculty Development Programmes

Student Skill Workshops

Industry-Academia Workshops

Conference Workshops & Pre-Symposia

Certification Workshops

Seasonal

PhD Registration Assistance

PhD Practical Assistance

PhD Publication Assistance

PhD Thesis Assistance

Research Abroad Products

Structural Bioinformatics Internship Topics

Browse all focused areas across all internship categories under this field.

Structural Bioinformatics Internships with Accommodation

Choose an internship topic, then explore accommodation-enabled internship options at active NTHRYS branch locations in India.

Bengaluru → Chennai → Hyderabad → Kochi → Kolkata →

Showing 1–12 of 20 internship topics

AlphaFold2 Model Optimization and Validation

Interns will work on fine-tuning AlphaFold2 models for specific protein families and validating predictions against experimental structures from the PDB. They will implement custom training pipelines, evaluate model performance metrics, and identify failure cases to improve prediction accuracy for challenging protein classes.

AI Protein 3D Structure Prediction ResearchView internship →

Multi-Chain Protein Complex Structure Prediction

Interns will develop and implement methods for predicting quaternary structures of protein-protein complexes using deep learning approaches. This includes training models on complex interaction data, optimizing inter-chain distance predictions, and validating results against cryo-EM and X-ray crystallography data.

AI Protein 3D Structure Prediction ResearchView internship →

Structural Motif Recognition and Classification

Interns will create computational pipelines to identify, extract, and classify conserved structural motifs from predicted 3D protein structures using graph neural networks and clustering algorithms. They will build databases of recurring folds and domains across different protein families for functional annotation.

AI Protein 3D Structure Prediction ResearchView internship →

Confidence Score Analysis and Uncertainty Quantification

Interns will investigate methods to improve confidence metrics and uncertainty estimation in AI-generated protein structures, including pAE (predicted aligned error) refinement and Bayesian approaches. They will benchmark different confidence scoring systems and develop strategies to identify unreliable predictions.

AI Protein 3D Structure Prediction ResearchView internship →

Structure-Based Function Prediction Pipeline Development

Interns will build integrated pipelines that leverage predicted 3D structures to infer protein function, ligand binding sites, and enzymatic mechanisms using structure alignment, active site prediction, and machine learning models. They will validate predictions experimentally or through literature mining and contribute to functional annotation databases.

AI Protein 3D Structure Prediction ResearchView internship →

Deep Learning Models for Protein-Ligand Binding Affinity Prediction

Interns will develop and optimize neural network architectures (CNNs, GNNs, Transformers) to predict binding affinities between proteins and drug molecules. Work includes dataset preparation, model training on PDBbind and similar databases, and validation against experimental binding data to improve drug discovery pipelines.

Machine Learning Drug Docking ResearchView internship →

Molecular Docking Pose Generation and Ranking Algorithms

Interns will implement and refine algorithms for generating multiple ligand conformations within protein binding pockets and develop scoring functions to rank docking poses. This includes integrating physics-based and machine learning approaches to enhance pose selection accuracy.

Machine Learning Drug Docking ResearchView internship →

Graph Neural Networks for 3D Protein-Ligand Complex Analysis

Interns will apply GNN architectures to represent protein-ligand interactions as graph structures, capturing atomic and residue-level relationships. Research focuses on feature engineering, message passing mechanisms, and predicting interaction hotspots and binding modes from 3D structural data.

Machine Learning Drug Docking ResearchView internship →

Transfer Learning and Domain Adaptation in Drug Docking Models

Interns will explore transfer learning techniques to adapt pre-trained models across different protein families, ligand classes, and docking software outputs. Work includes fine-tuning models on limited experimental data and addressing domain shift challenges in heterogeneous structural datasets.

Machine Learning Drug Docking ResearchView internship →

Interpretability and Feature Analysis in Machine Learning Docking Predictions

Interns will investigate explainability methods (attention mechanisms, SHAP, gradient-based analysis) to understand which molecular and structural features drive ML docking model predictions. This research bridges computational predictions with biochemical insights for rational drug design.

Machine Learning Drug Docking ResearchView internship →

Deep Learning Models for Antibody CDR Prediction

Interns will develop and train neural network models to predict complementarity-determining regions (CDRs) in antibody sequences using datasets like OAS and SAbDab. They will implement sequence-to-structure prediction pipelines and evaluate model performance against experimental antibody structures.

AI Antibody Structure ResearchView internship →

Protein Language Models for Antibody Function Classification

Interns will fine-tune transformer-based protein language models (ESM, ProtBERT) to classify antibody binding specificity, affinity levels, and biological function from primary sequences. This involves data preprocessing, transfer learning implementation, and benchmarking against traditional bioinformatics methods.

AI Antibody Structure ResearchView internship →

Want to browse internship categories in Structural Bioinformatics? Explore all Structural Bioinformatics internship categories.