Agentic AI for Drug Discovery & Biotechnology - Hands-on Training Program With Project Work & Placement Assistance
Agentic AI for Drug Discovery & Biotechnology
Exclusive Hands-On Training Programme With Project work & Job Placement Assistance
GET TRAINED | WORK ON PROJECTS | BUILD REAL AGENTS |
GET WORK EXPERIENCE & RECOMMENDATION LETTER* | GET PLACEMENT ASSISTANCE
AI will not replace biologists.
But biologists who build AI agents will replace those who don't. Join the next generation of AI-enabled biotech professionals.
Build Intelligent AI Agents That Discover Drugs, Analyse Omics Data & Accelerate Biotech Innovation
STARTS 20th July 2026
The future of drug discovery is no longer just machine learning - it is Agentic AI.
In this 60-day intensive programme, you will design, build and 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.
Programme Format
- Course Start Date: 20th July 2026
- Duration: 60 Days
- Format: Hands-on training + project
- Mode: Online LIVE + Recorded Sessions
- Project: Starts After Training Completion, Followed by a Quick Interview
- Project Duration: 3,6 & 12 Months
- Time: 7:00 pm to 8:00 PM IST (Mon-Fri)
- Outcome: Portfolio-ready project + certificate
Certification
Participants who:
- Complete all assignments
- Build their mini-project
- Complete and publish a capstone project
- Build a GitHub portfolio
...will receive a Completion Certificate in Agentic AI for Drug Discovery & Biotechnology.
What Makes This Programme Unique?
Unlike generic AI courses, this programme 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
- Real-world capstone project in drug discovery or omics
You will graduate with multiple working AI agents and a published capstone project 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, and rank drug targets.
Module 3: Drug Discovery Agents
Apply agents to target validation, ADMET, screening & optimisation.
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 optimisation
- Add caching & optimisation 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
Check Scientist Profile, Course & Project Details, Download Brochure
Capstone Projects — Build Something Real
This programme includes a structured capstone project component. Every participant selects, builds, and publishes a real-world agentic AI project to their GitHub portfolio.
Choose from 30 industry-relevant project titles across drug discovery, cheminformatics, genomics, and multi-omics — each scoped with a defined objective and recommended timeline.
Agentic AI Project Topics
| Project Title | Objective | Duration |
|---|---|---|
| Autonomous Virtual Screening & Hit Triage Agent | Given a protein target name, automatically dock a compound library and return top-scoring, drug-like hits | 6 months |
| Automated QSAR Model Builder & Evaluator | Build, validate, and deploy a QSAR model from a user-provided CSV of molecules and activity | 6 months |
| Semantic Search Agent with Self-Reflective Retrieval for Drug Repurposing | For a given disease, autonomously mine literature and propose repurposable drugs with supporting evidence | 3 months |
| Multi-Objective Lead Optimisation Agent | Start from a hit molecule and iteratively generate analogues that improve bioactivity, ADMET, and synthetic accessibility | 12 months |
| Reverse Docking Target Identification Agent | Given a small molecule or natural product, identify the most likely protein targets by docking against known drug targets | 6 months |
| NeuroRepurpose Agent: AI-Driven Drug Repurposing for Alzheimer's Disease | AI agent for Alzheimer's drug repurposing using knowledge graphs and literature mining | 3 months |
| Autonomous Disease-to-Target Mining Agent | Accept a disease name, search recent literature, and extract a structured list of putative gene or protein targets | 3 months |
| Cheminformatics Agent for Drug-Likeness Assessment | Accept a SMILES string, calculate physicochemical properties, and assess drug-likeness | 3 months |
| Network-Based Drug Repurposing Hypothesis Generator | Navigate biological networks to suggest new indications for existing drugs based on overlapping gene targets | 6 months |
| AI Agent for ADMET Property Prediction | Agentic AI-driven prediction of drug-likeness and ADMET properties in drug discovery | 3 months |
| AI Agent for Predicting Anticancer Activity Using QSAR Models | Screen chemical compounds and predict their potential anticancer activity | 3 months |
| AI Agent for End-to-End Drug Discovery | Autonomous virtual screening pipeline that screens compounds, predicts biological activity, and identifies promising drug candidates | 6 months |
| Drug Discovery Agent for De Novo Molecular Design | Autonomously generate, evaluate, and prioritise novel drug-like compounds | 12 months |
| BioLit-Agent: Automated Literature Mining & Research Gap Identification | Search scientific literature, extract key findings, summarise papers, and identify research gaps in a chosen biological domain | 3 months |
| Bio-Agent: Disease Mechanism Analysis & Therapeutic Target Identification | Autonomous multi-agent platform that integrates biological databases and literature, mimicking the workflow of a biomedical researcher | 3 months |
| DrugRepurpose-Agent: Multi-Agent System for Disease-Specific Drug Repurposing | Identify candidate drugs by integrating literature, target databases, molecular interactions, and AI-based ranking | 3 months |
| PharmaAgent: End-to-End Target Discovery, Drug Repurposing & Lead Prioritisation | Transform disease knowledge into actionable therapeutic leads using AI and bioinformatics | 6 months |
Genomics, Omics & Biological Systems Projects
| Project Title | Objective | Duration |
|---|---|---|
| OncoTarget Agent: Cancer Biomarker Discovery & Precision Oncology Platform | Autonomous multi-agent platform for cancer biomarker discovery and therapeutic target prioritisation | 3 months |
| GenoInsight Agent: Clinical Genomics & Variant Interpretation Assistant | Intelligent genomics agent for variant interpretation and rare disease diagnosis | 3 months |
| SingleCell Agent: Autonomous Single-Cell Transcriptomics Analysis Platform | AI agent for cell-type identification and cellular heterogeneity analysis | 6 months |
| Microbiome Agent: Gut Microbiome Profiling & Functional Prediction System | AI agent for microbiome characterisation and microbial function prediction | 6 months |
| ProteinStructure Agent: Protein Structure Prediction & Functional Annotation | AI agent for protein structure analysis and function prediction | 6 months |
| PathwayInsight Agent: Biological Pathway Reconstruction & Network Analysis | AI agent for pathway discovery and molecular network analysis | 6 months |
| Epigenome Agent: Intelligent DNA Methylation Analysis Platform Using NGS Data | AI agent for DNA methylation profiling and epigenetic biomarker discovery | 3 months |
| BioImage Agent: Autonomous Biomedical Image Analysis & Disease Classification | AI agent for analysing medical and microscopic images for disease classification | 12 months |
| NGS-QC Agent: Autonomous Sequencing Quality Assessment & Data Validation | AI agent for automated NGS quality control, sequencing error detection, and data validation | 12 months |
| Automated Sequence Profiling Agent | Take a gene or protein name, retrieve its biological sequence, and compute fundamental biomolecular properties | 3 months |
| CRISPR gRNA Heuristic Scoring Agent | Scan a target DNA sequence, identify CRISPR/Cas9 cut sites, and score them based on GC content and other heuristics | 6 months |
| Self-Supervised Cross-Omics Fusion Agent for Cancer Analysis | Integrate genomics and transcriptomics datasets autonomously to uncover biological insights and potential biomarkers | 6 months |
| BioDesign-Agent: Gene Analysis, Codon Optimisation & Protein Design | Analyse DNA/protein sequences and suggest optimisation strategies for expression and protein engineering | 3 months |
Project completion is required for the Completion Certificate.
Eligibility
This programme is ideal for:
- Life science graduates (BSc, MSc, MTech, PhD)
- Biotechnology / bioinformatics students
- Pharmacy graduates
- Computational biology enthusiasts
- AI/ML learners wanting domain specialisation
- 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 is taught from scratch. Laptop with internet connection.
What You Will Be Able to Do After Training
By the end of this programme, 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 Outcomes
With the competencies built in this programme, 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, and Data Engineering.
Tools & Platforms Covered
Google Colab, Python, Pandas, Matplotlib, RDKit, PubMed (NCBI E-utilities), UniProt API, PDB, ChEMBL, Open Targets, Hugging Face, Gradio, GitHub
Key Programme Benefits
- Structured 60-day roadmap
- Live + hands-on implementation
- Real biological datasets
- API-based practical learning
- Deployment training
- 30 capstone project options
- GitHub portfolio building support
- Career guidance session
- Q&A and doubt-solving sessions
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, and data-integrated target discovery systems. This programme 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.