AI/ML in Drug Discovery & Biopharma – Hands-on Training Program with 3, 6 & 12-Month LIVE Projects
AI/ML in Drug Discovery & Biopharma – Hands-on Training Program
With 3, 6 & 12 Months LIVE Project Work
WORK IN PROJECTS | PUBLISH PAPERS | GET WORK EXPERIENCE in AI ML
STARTS 5th JAN 2026
With 100% Placement Assistance
Dive into the future of Chemistry and Pharma with our specialized AI and ML training program. Designed for professionals and students, this comprehensive course integrates cutting-edge AI and ML techniques with essential concepts in chemistry and pharmaceutical sciences. The program includes hands-on project work options of 3, 6, or 12 months, allowing you to apply your skills to real-world problems.
Program Overview:
- Start Date: 5th JAN 2026
- Time: 7-8 PM IST
- Venue: Online
- Project work: 3,6, & 12 months duration
Why Join This Training?
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Expert-Led Instruction: Learn from industry leaders and academic experts who bring practical knowledge and research-based insights into AI and ML applications.
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Hands-On Experience: Engage in practical exercises using industry-standard tools like TensorFlow, RDKit, and KNIME. Our project work options enable you to gain deep, hands-on experience.
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Real-World Applications: Explore how AI/ML is transforming drug discovery, molecular design, and personalized medicine. Work on projects that mimic real-life challenges in the chemistry and pharma industries.
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Flexible Learning Paths: With options for 3, 6, and 12 months of project work, you can choose a learning path that fits your schedule and career goals.
Why Learn AI/ML in Biopharma & Chemistry?
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Stay Ahead of the Curve: AI/ML is revolutionizing the pharma industry, enabling faster drug discovery, personalized treatments, and more efficient R&D processes.
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High Demand Skills: With the rise of AI/ML in the industry, professionals skilled in these areas are in high demand across the globe.
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Innovative Research Opportunities: AI/ML offers novel approaches to tackle complex problems in chemistry, such as molecular design and toxicity prediction.
Eligibility
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Educational Background: Ideal for students and professionals with a background in Biotech, Life Sciences, Chemistry, Pharmaceuticals, Bioinformatics, Computer Science, or related fields.
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Prerequisites: Basic understanding of Drug Discovery chemistry and computational tools. Prior experience in programming or data science is beneficial but optional.
COURSE CURRICULUM
Week 1: Foundations of Drug Discovery
- Day 1: Introduction to the Drug Discovery Pipeline – Target Identification → Screening → Optimization → Preclinical
- Day 2: Role of Bioinformatics & Cheminformatics – Ligand-based vs Structure-based methods
- Day 3: Molecular Representations – SMILES, InChI, SDF, FASTA formats
- Day 4: Visualization of Molecules – Tools: RDKit, PyMOL demo
- Day 5: Introduction to Drug Databases – DrugBank, ChEMBL, BindingDB, PubChem
- Day 6: Understanding Bioactivity Metrics – IC50, EC50, Ki and their usage in modeling
- Day 7: Target Identification & Validation – Omics data to gene prioritization (GEO, DisGeNET)
- Day 8: Molecular Docking & Virtual Screening – Concepts & tools overview
- Day 9: Lead Optimization & Drug Repurposing – Lipinski’s Rule, ADMET considerations
Week 2: ML Foundations & Python for Drug Discovery
- Day 10: Python Basics – Data types, functions, pandas, numpy
- Day 11: Visualizing Drug Data – matplotlib, seaborn for bioactivity & chemical structure
- Day 12: Introduction to Machine Learning – Supervised vs Unsupervised, ML pipeline
- Day 13: Data Preprocessing – Cleaning, scaling, encoding SMILES
- Day 14: Feature Extraction – RDKit descriptors, molecular fingerprints
- Day 15: ML Models I – Classification: Logistic Regression, Decision Trees, Random Forest
- Day 16: ML Models II – Regression: Linear Regression, Ridge, SVR
- Day 17: Model Evaluation – Confusion Matrix, ROC-AUC, MAE, RMSE
- Day 18: Case Study – Classify drug-like vs non-drug-like molecules
Week 3: QSAR, Pharmacophore Modeling & Bioactivity Prediction
- Day 19: Introduction to QSAR – Concepts & datasets
- Day 20: Building Regression Models – IC50 prediction
- Day 21: Model Tuning – GridSearchCV, Cross-validation
- Day 22: AI in COVID-19 Drug Discovery – Predicting SARS-CoV-2 Inhibitor Activity
- Day 23: Unsupervised Learning – K-Means, DBSCAN
- Day 24: Dimensionality Reduction – PCA, t-SNE; Case study: clustering molecules
- Day 25: AI in Pharmacophore Modeling
- Day 26: Drug Repurposing – Similarity-Based Clustering
- Day 27: Case Study – End-to-End Bioactivity Prediction Pipeline
Week 4: Deep Learning for Drug Discovery
- Day 28: Introduction to Deep Learning – Concepts and terminologies
- Day 29: Keras & PyTorch – Intro for molecular data
- Day 30: CNNs & RNNs – For SMILES sequences
- Day 31: Autoencoders – For molecular representation
- Day 32: Generative Models – VAE & GAN; Case study: generate molecules
- Day 33: Graph Neural Networks – For molecule structure
- Day 34: DeepDTA – Protein-ligand binding affinity prediction
- Day 35: Case Study – Toxicity prediction with deep learning
Week 5: Integrative AI Applications & Current Trends
- Day 36: Chemoinformatics for Natural Products
- Day 37: NLP in Drug Discovery – Literature mining, NER with PubMed
- Day 38: Reinforcement Learning – Drug optimization & reward modeling
- Day 39: ADMET Property Prediction – Blood-brain barrier, hERG toxicity, etc.
- Day 40: AI in Docking & MD Simulations – Integration overview, ML-based binding score prediction
- Day 41: Building ML Pipeline – End-to-end model, deployment with Streamlit
- Day 42: AI in Personalized Medicine & Healthcare
- Day 43: Regulatory Guidelines & Ethics – Bias, explainability, validation, clinical risk
- Day 44: Current Trends – Career guidance & Capstone project discussion
- Day 45: Open Doubt-Solving & Discussion Session
Project Work Options
3 Months: Short-term projects focusing on specific applications like QSAR modelling or molecular docking.
6 Months: Intermediate projects involving more complex models and integration with real-world datasets.
12 Months: Long-term research projects aiming at publication-quality results or industrial application.
- Prediction of Drug-Likeness Using Machine Learning on Molecular Descriptors – 6 months
- In Silico Screening of Natural Compounds for Anti-Cancer Activity – 6 months
- Cheminformatic Approaches for Drug Design and Drug Discovery – 6 months
- Virtual Screening of Potential Inhibitors for SARS-CoV-2 Main Protease – 6 months
- Virtual Screening of Potential Inhibitors for SARS-CoV-2 Mpox – 6 months
- Molecular Docking and Machine Learning-Based Analysis of Anti-Inflammatory Compounds – 3 months
- Molecular Docking and Machine Learning-Based Analysis of Antiviral Compounds – 6 months
- Exploring the Use of Deep Learning for Predicting Toxicity in Chemical Compounds – 6 months
- Machine Learning Based Screening of Curcuma longa for Drug Discovery – 6 months
- Application of Neural Networks in Predicting the Solubility of Pharmaceutical Compounds – 6 months
- AI-Driven Prediction of Drug-Target Interactions for New Chemical Entities – 6 months
- Computational Prediction of Binding Affinity for Enzyme-Substrate Interactions – 6 months
- AI-Assisted Designing of Novel Anticancer Compounds – 6 months
- Application of AI in the Prediction of Protein-Ligand Binding Sites – 6 months
- Virtual Screening and Docking Studies of Phytochemicals Against Cancer Targets – 6 months
- AI/ML-Assisted Molecular Modelling Studies for Protein Stability Analysis – 6 months
- Prediction of Protein Secondary Structure Using Basic Supervised Learning Algorithms – 6 months
- Cheminformatics Approaches to Predict the Reactivity of Organic Compounds – 6 months
- AI-Assisted Approaches for Structure-Based Drug Design – 6 months
- AI-Assisted Approaches for Ligand-Based Drug Design – 6 months
- Machine Learning Models for Predicting Drug Side Effects Based on Chemical Structure – 6 months
- Predictive Modeling of Drug Permeability Using AI Techniques – 6 months
- Molecular Docking for HIV Inhibitor Screening (Bioinformatics, AI/ML in Drug Discovery) – 3 months
- Host-Pathogen Interaction Modeling for HIV (Bioinformatics, AI/ML in Drug Discovery) – 6 months
- Network-Based Drug Repurposing for HIV (Bioinformatics, AI/ML in Drug Discovery) – 6 months
- Meta-Learning Framework for Bioactivity Prediction on Low-Data Orphan Targets Using Neural Processes – 12 months
- Explainable AI (XAI) for Toxicity/ADMET Predictions – 3 months
- Unifying Transcriptomics & Chemoinformatics for Personalized Drug Repurposing – 6 months
- Explainable AI for ADMET Prediction Using Multi-Modal Data – 3 months
- Machine Learning-Based Virtual Screening of Natural Compounds for Antibacterial Activity (Proteases) – 3 months
- Predictive Modeling and Lead Optimization for Anticancer Agents Using AI – 6 months
- Deep Learning Framework for Structure-Based Drug Design Targeting Viral Proteins – 12 months
- Reinforcement Learning-Based Drug Design Against Kinase Targets – 6 months
- AI-Based QSAR Modeling for Anti-Cancer Drug Lead Optimization – 12 months
- Deep Learning for Promoter Region Prediction: Use CNNs (in Keras/TensorFlow) to Detect Promoter Regions in Genomic DNA – 6 months
- Transfer Learning for Cross-Species Drug Repurposing – 6 months
Enrol Today!
Equip yourself with the skills to transform the future of Chemistry and Pharma. Whether you are looking to enhance your career, pivot to a new field, or contribute to groundbreaking research, this program offers the tools and knowledge to succeed.
