Agentic AI for Drug Discovery & Biotechnology - Hands-on Training Program With Mini Projects

Agentic AI for Drug Discovery & Biotechnology
Exclusive Hands-on Training Program

Build Intelligent AI Agents That Discover Drugs, Analyze Omics Data & Accelerate Biotech Innovation

The future of drug discovery is no longer just machine learning it is Agentic AI.

In This 60-Day Intensive Program, You Will Design, Build & Deploy Agentic AI Systems That Can:

• Autonomously search and interpret scientific literature
• Integrate PubMed, UniProt, PDB, ChEMBL, GEO & Open Targets into unified workflows
• Discover and score drug targets programmatically
• Generate, filter and rank drug-like molecules
• Evaluate ADMET and drug-likeness properties
• Analyse RNA-Seq, variants, and biological networks
• Design CRISPR guides and perform codon optimisation
• Simulate virtual screening and optimisation pipelines

You won’t just learn AI concepts you will engineer real, multi-tool scientific agents used in computational drug discovery and biotechnology.

This is hands-on, tool-integrated, production-ready Agentic AI for biotech professionals.

Program Format

• Course Start Date: 24th March 2026
• Duration: 60 days - 60 Structured Sessions
  
• Time: 7:00 -8:00 pm IST
• Format: LIVE Classes + Assignments + Mini Projects
• QnA & Concept Validation Sessions
• Capstone & Deployment

Certification: Participants who:

• Complete assignments
• Build mini-project
• Publish GitHub portfolio

Will receive a Completion Certificate in Agentic AI for Drug Discovery & Biotechnology.

What Makes This Program Unique?

Unlike generic AI courses, this program integrates:

• Biology foundations
• Python for biological data
• Cheminformatics (RDKit, SMILES, Lipinski)
• PubMed, UniProt, PDB, ChEMBL integrations
• LLM-powered ReAct agents
• Retrieval-Augmented Generation (RAG)
• Drug discovery workflow automation
• Deployment using Gradio
• GitHub portfolio building

You will graduate with multiple working AI agents in your GitHub portfolio.

Curriculum Overview (60 Sessions)

MODULE 1: Bootcamp — Foundation of Biology + Python

Gain biological literacy first, then master Python using real biological datasets.

Key Concepts Covered:

• Central Dogma (DNA → RNA → Protein)

• Proteins & PDB analysis

• Drug Discovery pipeline

• Omics overview

• Biological databases (NCBI, GEO, SRA)

• Python fundamentals (variables, loops, functions, APIs)

• Pandas, Matplotlib

• Cheminformatics with RDKit

• Mini Data Collector Project

• AI & LLM fundamentals

Hands-on includes:

• Transcribing genes in Colab

• Fetching UniProt sequences

• RNA-Seq data analysis

• Plotting molecular distributions

• Building your first biological data script

MODULE 2: Agentic AI Foundations

Understand LLMs, prompting, and build your first ReAct agents.

You will learn:

• Prompt Engineering (zero-shot, few-shot)

• Structured JSON outputs

• Tool integration

• Memory for agents

• Retrieval-Augmented Generation (RAG)

• PubMed search automation

• UniProt, PDB, ChEMBL integration

• Disease-to-Target Agent Mini Project

You will build agents that:

• Search literature

• Fetch protein sequences

• Retrieve PDB structures

• Rank drug targets

MODULE 3: Drug Discovery Agents

Apply agents to target validation, ADMET, screening & optimization.

Hands-on:

• Target scoring system

• Literature mining agent

• Molecular similarity search

• ADMET prediction integration

• Generative SMILES models

• Virtual screening simulation (AutoDock concept)

• Multi-tool error handling

• Evaluation metrics

• Deploy agent using Gradio

You will build a complete screening agent prototype.

MODULE 4: Biology & Multi-Omics Agents

Expand into genomics, networks & knowledge graphs.

You will build agents that:

• Analyse RNA-Seq data

• Perform variant lookup (ClinVar)

• Build co-expression networks

• Query Hetionet for drug repurposing

• Generate CRISPR guides

• Perform codon optimization

• Add caching & optimization layers

Mini Project Options:

• Target Validation Agent

• Drug Repurposing Agent

• CRISPR Guide Designer

Advanced & Critical Thinking Sessions

• Responsible AI in drug discovery

• Clinical translation failure analysis

• Intellectual property in AI-generated molecules

• Critical appraisal of AI-drug discovery papers

• Career & portfolio workshop

Eligibility

This program is ideal for:

• Life Science Graduates (BSc, MSc, MTech, PhD)

• Biotechnology / Bioinformatics Students

• Pharmacy Graduates

• Computational Biology Enthusiasts

• AI/ML learners wanting domain specialization

• Research Scholars

• Industry Professionals in biotech

• CSIR-NET / DBT aspirants exploring applied careers

Basic Requirements:

• Basic understanding of biology (helpful but not mandatory)

• No prior coding required (Python taught from scratch)

• Laptop with internet connection

Who Should Take This Program?

✔ Students who want AI + Biology career edge
✔ Researchers wanting to automate literature & database mining
✔ Professionals shifting into computational drug discovery
✔ Founders building biotech AI startups
✔ Anyone wanting to build a serious AI portfolio in life sciences

What You Will Be Able to Do After Training

By the end of this program, you will be able to:

• Build autonomous AI agents for biological tasks

• Automate PubMed and database research

• Create target validation workflows

• Design molecule screening pipelines

• Perform ADMET analysis

• Develop RAG-based literature agents

• Deploy biotech AI web apps

• Publish projects on GitHub

• Critically evaluate AI-drug claims

You will move from AI user → AI builder → AI architect for biotech.

Career-Level Skills You Will Graduate With

After completing this program, you will have the capability to:

✔ Design domain-specific AI agents for biotech problems
✔ Integrate scientific APIs into production-grade pipelines
✔ Build LLM-powered research assistants
✔ Develop automated target validation systems
✔ Construct AI-driven screening workflows
✔ Deploy scientific AI tools as web applications
✔ Publish reproducible GitHub repositories

These are applied, portfolio-ready skills, not theoretical exposure.

Career Outcomes Based on This Skillset

With the exact competencies built in this program, you can pursue roles such as:

• AI Scientist – Drug Discovery

• Computational Drug Discovery Associate

• Bioinformatics AI Engineer

• Cheminformatics Developer

• Scientific LLM Application Developer

• Research Automation Engineer

• AI Product Developer – Biotech

• Genomics Data Scientist

You will be positioned at the intersection of:

• Biology
• Drug Discovery
• Agentic AI
• Data Engineering

What Makes This Career-Defining?

By the end of this program:

You will not just know AI.

You will not just know biology.

You will know how to build AI systems that operate on biological and chemical data autonomously.

That is the exact skill gap emerging in modern biotech and AI-driven drug discovery companies.

Key Benefits

• Structured 60-day roadmap

• Live + hands-on implementation

• Real biological datasets

• API-based practical learning

• Deployment training

• Career guidance session

• Q&A and troubleshooting sessions

• Concept validation exercises

• Portfolio building support

Tools & Platforms Covered

• Google Colab

• Python

• Pandas

• Matplotlib

• RDKit

• PubMed (NCBI E-utilities)

• UniProt API

• PDB

• ChEMBL

• Open Targets

• Hugging Face

• Gradio

• GitHub

Why Agentic AI Is the Future of Drug Discovery?

Traditional ML predicts. Agentic AI reasons, plans, uses tools, and acts.

Biotech companies are rapidly moving toward:

• LLM-powered scientific assistants

• Autonomous research copilots

• AI-guided molecule design

• Data-integrated target discovery systems

This program prepares you for that shift.

Transform Your Career

AI will not replace biologists.

But biologists who build AI agents will replace those who don’t.

Become the next-generation AI-enabled biotech professional.