Bio-LLMs & Deep Learning: From Sequence Design to Autonomous Research
Learn how Bio-LLMs and deep learning are shaping the future of biological sequence analysis, protein modeling, and AI-assisted research.
Skills you will gain:
About Program:
Bio-LLMs & Deep Learning: From Sequence Design to Autonomous Research is a 3-day online workshop designed to introduce participants to the use of advanced AI models in biological and biomedical research. The workshop covers Bio-RAG pipelines for scientific literature retrieval, deep learning-based biological sequence modeling, synthetic sequence generation using PyTorch, and protein transformer interpretation using ESM-2. Through mentor-led sessions and hands-on demonstrations, participants will understand how modern AI tools can support literature analysis, sequence design, protein representation learning, and autonomous research workflows in life sciences.
Aim: To provide participants with a practical and research-oriented understanding of Bio-LLMs, deep learning models, and protein language models for biomedical literature analysis, biological sequence design, and AI-assisted autonomous research.
Program Objectives:
- Understand the fundamentals of Bio-LLMs and deep learning in life sciences research.
- Learn how Bio-RAG pipelines support biomedical literature search and knowledge retrieval.
- Explore semantic chunking, vector indexing, and AI-based scientific information search.
- Understand how prompt guardrails improve the reliability of AI-generated research responses.
- Learn the basics of biological sequence data, including DNA, RNA, protein, and FASTA formats.
- Explore PyTorch-based deep learning workflows for biological sequence modeling.
- Understand how AI models can support synthetic biological sequence generation.
- Learn the role of protein language models such as ESM-2 in protein analysis.
- Explore attention mapping for interpreting protein transformer models.
What you will learn?
📅 Day 1: Autonomous Bio-Research – Building Smart AI Literature Engines
- Core Objective: Build an AI-powered biomedical literature engine that can retrieve, search, and answer research questions from scientific documents.
- Architecture of a modern, domain-specific Bio-RAG pipeline
- Semantic chunking for parsing complex biomedical and medical text
- Creating deep-learning searchable vector indices from scientific knowledge
- Prompt guardrails to reduce hallucination and improve research reliability
🛠️ Hands-on:
- Hands-on Lab: Build and deploy a local Literature QA Bot in Google Colab
🧰 Tools Covered: Bio-RAG, Vector Indexing, Prompt Engineering, Google Colab
📅 Day 2: AI-Driven Design – Generating Synthetic Biological Sequences with PyTorch
- Core Objective: Learn how deep learning models can generate synthetic biological sequences from sequential DNA data.
- Deep learning for DNA sequence modeling using LSTMs
- Processing large FASTA files using PyTorch data handling workflows
- Setting up, optimizing, and training a neural network from scratch
- Autoregressive inference for generating new biological sequences
🛠️ Hands-on:
- Hands-on Lab: Train an AI sequence generator and observe real-time synthetic biological sequence generation
🧰 Tools Covered: PyTorch, LSTM Models, FASTA Files, Google Colab
📅 Day 3: Protein Transformers & Attention Mapping
- Core Objective: Understand how protein transformer models learn molecular patterns and visualize attention signals in structural biology.
- Transformer architectures in the context of molecular biology
- Feature extraction using Meta’s ESM-2 protein language model
- Slicing transformer layers and mapping attention arrays
- Translating attention matrices into structural alignments and biological insights
🛠️ Hands-on:
- Hands-on Lab: Map and visualize protein transformer attention on a 3D structural model
🧰 Tools Covered: ESM-2, Protein Transformers, Attention Mapping, 3D Structural Visualization
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Graduate and postgraduate students from life sciences, biotechnology, bioinformatics, pharmacy, data science, and related fields.
- PhD scholars and researchers working in biological or biomedical research.
- Academicians and faculty members interested in AI applications in life sciences.
- Industry professionals from biotechnology, healthcare, diagnostics, pharma, and AI sectors.
- Basic knowledge of biology or life sciences is helpful.
- Prior experience with Python or machine learning is useful but not mandatory.
Career Supporting Skills
Program Outcomes
- Understand the role of Bio-LLMs in biological and biomedical research.
- Use Bio-RAG concepts for AI-assisted literature search and knowledge retrieval.
- Apply semantic chunking and vector indexing for scientific text analysis.
- Understand deep learning workflows for biological sequence data.
- Gain exposure to PyTorch-based sequence modeling.
- Learn the basics of AI-assisted synthetic sequence generation.
- Understand protein language models such as ESM-2.
- Interpret attention maps for protein sequence analysis.
- Explore AI applications in genomics, bioinformatics, drug discovery, and computational biology.
- Gain practical exposure to Google Colab-based Bio-AI workflows.