The AI for Drug Discovery and Medicine training program is an intensive, one-to-one masterclass designed to bridge the worlds of artificial intelligence, computational chemistry, and clinical pharmaceuticals. Modern drug discovery is undergoing a seismic transformation: while traditional drug development takes over 10 to 15 years and costs billions of dollars with high attrition rates, AI and Machine Learning empower researchers to screen billions of molecules, model complex biological targets, and design novel therapeutics with unprecedented speed and accuracy.
In this 40-Hour / 2-Month One-to-One Program, you will master the end-to-end computational drug design workflow. From handling chemical representations (SMILES, InChI, molecular graphs) using RDKit and predicting biological activity with Machine Learning (QSAR), to running high-throughput virtual screening, molecular docking, and leveraging cutting-edge Generative AI & Transformer Models (AlphaFold, ChemBERTa, VAEs) for de novo molecular generation. Furthermore, you will explore real-world applications of AI in clinical data analytics, diagnostic medical imaging, and optimized clinical trial design.
EkasCloud provides flexible training to all it's student. Here is our training schedule. Incase you find these timings difficult, please let us know. We will try to arrange appropriate timings based on your Convenience.
| 01-10-2026 | Thursday (Monday - Friday) Weekdays Regular | 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session |
| 03-10-2026 | Saturday (Monday - Friday) Weekdays Regular | 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session |
| 05-10-2026 | Monday (Monday - Friday) Weekdays Regular | 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session |
| 06-10-2026 | Tuesday (Monday - Friday) Weekdays Regular | 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session |
The AI for Drug Discovery and Medicine training program is an intensive, one-to-one masterclass designed to bridge the worlds of artificial intelligence, computational chemistry, and clinical pharmaceuticals. Modern drug discovery is undergoing a seismic transformation: while traditional drug development takes over 10 to 15 years and costs billions of dollars with high attrition rates, AI and Machine Learning empower researchers to screen billions of molecules, model complex biological targets, and design novel therapeutics with unprecedented speed and accuracy.
In this 40-Hour / 2-Month One-to-One Program, you will master the end-to-end computational drug design workflow. From handling chemical representations (SMILES, InChI, molecular graphs) using RDKit and predicting biological activity with Machine Learning (QSAR), to running high-throughput virtual screening, molecular docking, and leveraging cutting-edge Generative AI & Transformer Models (AlphaFold, ChemBERTa, VAEs) for de novo molecular generation. Furthermore, you will explore real-world applications of AI in clinical data analytics, diagnostic medical imaging, and optimized clinical trial design.
✅ Biotech & Pharmaceutical Professionals: Researchers, medicinal chemists, and pharmacologists looking to transition into data-driven and AI-guided drug discovery.
✅ Data Scientists & AI/ML Engineers: Professionals seeking high-impact careers in healthcare, life sciences, and computational biology.
✅ Bioinformaticians & Computational Biologists: Scientists aiming to integrate deep learning, generative molecular design, and structural biology.
✅ Healthcare & Clinical Researchers: Clinicians and research fellows working on clinical trial analytics, patient stratification, and precision medicine.
✅ Students & Life Science Graduates: Students in Biotechnology, Pharmacy, Bioinformatics, Computer Science, and Chemistry aspiring for high-paying international biotech roles.
1. Computational Cheminformatics: Master chemical data representation (SMILES, molecular fingerprints, 2D/3D descriptors) using Python and RDKit.
2. QSAR & Bioactivity Modeling: Build predictive machine learning models to forecast molecular properties, solubility, toxicity, and target binding affinity.
3. ADMET & Drug-Likeness: Apply Lipinski's Rule of Five, PAINS filters, and AI-driven pharmacokinetic profiling (Absorption, Distribution, Metabolism, Excretion, Toxicity).
4. Molecular Docking & Target Interaction: Perform 3D structure preparation, binding pocket identification, and molecular docking simulations.
5. Deep Learning for Molecular Graphs: Implement Graph Neural Networks (GNNs) and Message Passing Neural Networks (MPNNs) for chemical property prediction.
6. Generative AI & De Novo Molecular Design: Train Variational Autoencoders (VAEs), Diffusion Models, and Transformers to design novel candidate drug molecules.
7. Clinical AI & Healthcare Analytics: Analyze longitudinal clinical trial records, predict patient risk, and explore medical imaging deep learning (CNNs).
8. Responsible & Explainable AI: Ensure compliance with FDA/EMA guidelines, patient data privacy (HIPAA/GDPR), and interpret AI decisions using SHAP and LIME.
✔ 100% One-to-One Online Personalized Mentorship: Learn at your own pace with a dedicated expert trainer guiding your code and concepts in real time.
✔ 50% Theory & 50% Hands-On Practical Labs: Gain immediate practical experience through Jupyter notebook labs and real-world datasets.
✔ Industry-Grade Capstone Project Portfolio: Complete an end-to-end industry project that you can showcase on GitHub and resume for global biotech roles.
✔ Real-Time Biomedical Datasets: Work with premier public scientific repositories including ChEMBL, PubChem, DrugBank, and the Protein Data Bank (PDB).
✔ Dedicated Placement & Interview Support: Receive 1-on-1 resume optimization, mock interviews, and career guidance for pharmaceutical and AI research positions.
• One-to-one or one-to-two personalized training sessions.
• We assess your knowledge background before commencing each module.
• Build foundational computational biology first, advancing to deep learning & generative AI.
• 50% Theory combined with 50% Hands-On Coding Labs on real scientific datasets.
• Live coding demonstrations and architectural guidance on every topic.
• Comprehensive Capstone project portfolio reviewed by industry experts.
• Dedicated Mock Interviews and international career preparation support.
Course Duration: 2 Months (40 Hours) | 50% Theory + 50% Practical Labs | 1-to-1 Training
✅ Module 1: Introduction to Drug Discovery & AI (Week 1)
Drug discovery lifecycle, target identification & validation, hit-to-lead, traditional vs AI-driven workflows, molecular biology fundamentals, biomedical data sources (ChEMBL, PubChem, PDB).
Lab 1: Exploring Public Drug & Bioactivity Datasets using Python & Pandas.
✅ Module 2: Python for Computational Drug Discovery (Week 2)
Python scientific stack, chemical data representations (SMILES, InChI, molecular graphs), hands-on RDKit installation and manipulation, 2D/3D visualization, molecular fingerprints (Morgan/ECFP4), chemical similarity and chemical space clustering.
Lab 2: Molecular Property Explorer & Chemical Space Mapping.
✅ Module 3: Machine Learning for Drug Discovery (Week 3)
Supervised & unsupervised learning in chemistry, regression vs classification, molecular feature engineering, QSAR (Quantitative Structure-Activity Relationship) modeling, solubility & toxicity prediction, scaffold-based cross-validation.
Lab 3: Building a QSAR-Based Target Bioactivity Prediction Model.
✅ Module 4: ADMET & Virtual Screening (Week 4)
Pharmacokinetics (Absorption, Distribution, Metabolism, Excretion, Toxicity), drug-likeness rules (Lipinski, Veber, PAINS), Ligand-Based & Structure-Based Virtual Screening (HTVS), multi-parameter candidate ranking.
Lab 4: Building an End-to-End AI-Assisted Virtual Screening Pipeline.
✅ Module 5: Protein Structure & Drug–Target Interaction (Week 5)
3D protein structure hierarchy, active binding pockets, non-covalent interactions, molecular docking workflows (receptor preparation, grid generation, docking score interpretation), deep learning for drug-target interaction (DTI).
Lab 5: Computational Molecular Docking & Target Interaction Prediction.
✅ Module 6: Deep Learning & Generative AI for Drug Discovery (Week 6)
Deep learning fundamentals (PyTorch), Graph Neural Networks (GNNs) on molecular graphs, Generative AI models (VAEs, GANs, Diffusion) for de novo drug design, molecular language models (ChemBERTa, MolGPT), AlphaFold & Protein Transformers (ESM).
Lab 6: Generative AI for Novel Molecule Generation & Property Optimization.
✅ Module 7: AI in Medicine & Clinical Research (Week 7)
Clinical decision support systems (CDSS), disease prediction & precision medicine, EHR & longitudinal clinical data analytics, AI in medical imaging (CNNs for pathology/radiology), optimizing clinical trial patient recruitment & pharmacovigilance.
Lab 7: Clinical Patient Risk & Trial Outcome Prediction.
✅ Module 8: Responsible AI, Research & Capstone (Week 8)
Responsible AI in pharmaceuticals, patient privacy (HIPAA/GDPR), algorithmic fairness & bias, model explainability (SHAP, LIME), reproducible scientific research workflows, and final Capstone project execution.
Lab 8: Capstone Project Execution & Portfolio Review.
Q: What if I miss a scheduled class?
A: Because our training is conducted 1-on-1, your classes are completely flexible. If you cannot attend a scheduled session, simply notify your trainer in advance, and the class will be paused and rescheduled at your convenience.
Q: Do I need a strong background in both coding and biology?
A: No. The course is thoughtfully designed for both life science professionals (who want to learn Python and AI) and computer science/data science professionals (who want to master molecular biology and pharmaceutical concepts). Foundational concepts are covered step-by-step.
Q: What software tools and libraries will I use?
A: You will work with industry-standard open-source tools including Python, Jupyter, NumPy, Pandas, RDKit, Scikit-learn, PyTorch, DeepChem, PyMOL, and modern generative AI frameworks.
Q: Will I work on real-world pharmaceutical datasets?
A: Yes! All practical labs and capstone projects utilize real-world scientific datasets from ChEMBL, PubChem, DrugBank, the Protein Data Bank (PDB), and clinical trial repositories.
Q: Will I receive job assistance and portfolio guidance?
A: Absolutely. Once certified, our career team provides 1-on-1 resume optimization, GitHub portfolio reviews for your capstone project, and technical mock interview sessions.
Q: What is the average salary in AI Drug Discovery and Bioinformatics?
A: AI Drug Discovery and Computational Biology roles command premium compensation globally, with average salaries ranging between £70,000 - £120,000 in the UK and $130,000 - $185,000 in the US.
Q: How soon after enrolling do classes start?
A: Once you complete your enrollment, our Student Success Manager coordinates with you to schedule your preferred class timings and timezone slots with your dedicated instructor.
If a student want to take admission in any course he has to go with the following steps
Upon successful completion of the assessment, candidates receive a customized learning path, course schedule, and payment options. Candidates can finalize their enrollment by agreeing to the course structure and payment plan.
