Bio-LLMs & Deep Learning: From Sequence Design to Autonomous Research

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.

• 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.

📅 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

• 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.

• 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.