AI ML in Chemistry & Cheminformatics – Hands-on Industrial Training Program with Project Work + Paper Publication Assistance
🧪 AI ML in Chemistry & Cheminformatics Hands-on Industrial Training Program
Project Work + Paper Publication Assistance
Admissions Open
Starts 28th October 2025
Master the Future of Chemical Research with Artificial Intelligence & Machine Learning
Explore how AI and ML are transforming chemistry, drug discovery, and materials science. Learn to apply powerful computational tools for molecular modeling, virtual screening, and predictive analytics in chemistry.
🎯 Program Highlights:
- 30-Day Hands-on Training on AI/ML Applications in Chemistry
- 3/6/12-Month Optional Project Work with Paper Publication Assistance
- Learn from Industry Experts & Research Scientists
- Hands-on Projects using DeepChem, RDKit, DiffDock & AlphaFold
- Work Experience Certificate + Publication Guidance
🔬 Program Overview
Mode: Online (via Zoom)
Start Date: 28th October 2025
Timings: 7:00 PM – 8:00 PM IST
Duration: 30 Days (with optional 3, 6, or 12-month research projects)
Ideal For:
-
Chemistry, Pharmaceutical, and Biotech Students
-
Research Scholars & Industry Professionals in Drug Discovery / Cheminformatics
-
Aspirants interested in AI/ML applications in Chemical & Pharmaceutical R&D
🧠 About the Program
Welcome to AI ML in Chemistry & Cheminformatics – Hands-on Training Program,
an advanced skill-building course designed for chemists and life scientists who want to integrate Artificial Intelligence into chemical research.
You’ll gain practical exposure to machine learning workflows, QSAR modeling, DeepChem, RDKit, DiffDock, and AlphaFold, and learn to build predictive models for molecular design, ADMET analysis, and virtual screening.
Participants opting for 3, 6, or 12-month project extensions will work on real-world computational chemistry projects and receive paper publication assistance.
🧩 Detailed Day-Wise Curriculum
AI in Cheminformatics and Chemistry
| Day | Topic |
|---|---|
| Day 1 | Introduction to AI/ML and Cheminformatics: scope, tasks, and open benchmarks |
| Day 2 | Molecular representations: SMILES, graphs, and featurizers overview in DeepChem |
| Day 3 | RDKit basics: molecules from SMILES, sanitization, drawing, and core operations |
| Day 4 | Descriptors and fingerprints: ECFP/Morgan, MACCS, atom-pairs, and similarity |
| Day 5 | Data handling and preprocessing: featurization, and dataset objects in DeepChem |
| Day 6 | Train/valid/test splits: random vs scaffold; preventing leakage and fair comparisons |
| Day 7 | Open Discussion |
| Day 8 | Classical ML baselines for QSAR: regression/classification with fingerprints on benchmark datasets |
| Day 9 | Metrics and validation: ROC-AUC, PR-AUC, RMSE/R², cross-validation, and stratification |
| Day 10 | QSAR best practices and OECD validation principles; internal vs external validation and reporting |
| Day 11 | Applicability domain and data uncertainty; pitfalls under data scarcity and imbalance |
| Day 12 | DeepChem deep learning: Graph Convolution feature pipelines and model |
| Day 13 | GNN property prediction: training and evaluating graph models on MoleculeNet tasks |
| Day 14 | Open Discussion |
| Day 15 | Alternative featurizations: circular fingerprints vs graph vs image and sequence featurizers |
| Day 16 | ADMET prediction tasks: Toxicological evaluations |
| Day 17 | Toxicology prediction with Tox21 via MoleculeNet/DeepChem tutorials |
| Day 18 | Virtual screening foundations: docking concepts, poses, scoring, and evaluation |
| Day 19 | Generative models overview for molecular design |
| Day 20 | Practical docking with DiffDock: setup and pose analysis |
| Day 21 | Open Discussion |
| Day 22 | ML-augmented screening for natural products |
| Day 23 | Structure-based ML scoring: integrating docking outputs into ML workflows |
| Day 24 | Immunoinformatics and Protein-Protein docking tools demonstration: Lzerd, HADDOCK, and Cluspro |
| Day 25 | AI-driven Drug Discovery: AI-based cascades for CADD |
| Day 26 | AI-based Target Designing/Modeling: Demonstration of AlphaFold |
| Day 27 | Molecular Dynamics Simulations overview and importance |
| Day 28 | In-silico validation methods: For drug discovery, protein modeling, and energy function evaluations |
| Day 29 | Troubleshooting Session |
| Day 30 | Project and Test Discussion |
PROJECT TOPIC LIST
💻 Tools & Technologies Covered
- DeepChem – For molecular modeling & featurization
- RDKit – For chemical informatics and descriptor generation
- DiffDock – For docking simulations
- AlphaFold – For protein structure prediction
- MoleculeNet – For benchmarking and QSAR datasets
- Graph Neural Networks (GNNs) – For molecular property prediction
- Python, TensorFlow, Scikit-learn – For machine learning workflows
🚀 Career Opportunities After Completion
After this training, you’ll be prepared for roles such as:
- Computational Chemist / Cheminformatics Analyst
- AI & ML Scientist in Drug Discovery
- QSAR Model Developer
- Data Scientist (Pharma / Chemical Research)
- Research Fellow in AI-driven Chemistry Projects
You’ll also receive:
- Certificate of Completion
- Work Experience Letter (for project durations)
- Research Paper Publication Assistance
🧑🏫 Expert Faculty & Mentorship
Learn directly from Biotecnika’s panel of AI/ML researchers, computational chemists, and bioinformatics experts who specialize in modern computational drug discovery.
You’ll receive live mentoring, personalized guidance, and access to recorded sessions.
📚 What You’ll Get
- Live Instructor-led Classes (30 days)
- Project-based Learning (3/6/12 months)
- Paper Publication & Mentorship Support
- Work Experience Certificate (for extended projects)
- Recorded Class Access
- Career Guidance + Networking Opportunities
⚠️ Disclaimer
We provide complete guidance and mentorship for project execution and research paper writing. However, publication acceptance is subject to journal peer review, and job placement is not guaranteed. Our goal is to empower you with research-ready skills and experience to advance your scientific career.
🧾 Enrollment Options
🟢 30-Day Online Training Only
🟢 30-Day Training + 3-Month Project Work + Paper Assistance
🟢 30-Day Training + 6-Month Project Work + Paper & Work Experience Letter
🟢 30-Day Training + 12-Month Research Project + Publication + Work Experience Letter
👉 Limited Seats – Enroll Now!
Learn from the best, work on real projects, and publish your research in 2025.
🧭 Join the Future of Chemical Research
AI is changing the landscape of chemistry and drug discovery.
Be part of this revolution — where data meets molecules, and intelligence meets innovation.