AI/ML in Drug Discovery & Biopharma – Hands-on Training Program with 3, 6 & 12-Month LIVE Projects
AI/ML in Drug Discovery & Biopharma - Self Learning Hands-on Training Program with Mentor Support
Powered By Boltzmann’s AI Discovery Suite (AI-POWERED RESEARCH PLATFORM)
Special Access for AI-ML in Drug Discovery Students
With 3, 6 & 12 Months LIVE Project Work
WORK IN PROJECTS | PUBLISH PAPERS | GET WORK EXPERIENCE in AI ML
ADMISSIONS OPEN
With 100% Placement Assistance
OFFLINE & ONLINE PROJECTS AVAILABLE
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:
- Program Type: Self-Learning (Online) Only for Classes
- PROJECT - LIVE directly under our Scientists
- Course Access Duration: 45 Days (Project students will receive course access for the entire duration of their project.)
- Mentor Support: (Doubt Solving, guidance, feedback)
- Certification: Yes
- Project work: 3,6, & 12 months duration
- Work Experience Letter - Under 6 & 12 Months Project
- Reference Letter - Under 12 Months Project
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.
Boltzmann AI Drug Discovery Suite Access
Special Access for AI-ML in Drug Discovery Students
As part of the AI-ML in Drug Discovery Training Program, students will receive exclusive access to the Boltzmann AI Drug Discovery Suite - a powerful platform used by researchers and industry professionals to accelerate drug discovery using Artificial Intelligence and computational chemistry tools.
Through our academic partnership with Boltzmann, students enrolled in this program will get access to trhe Professional Suite.
This gives you the opportunity to work with real industry-grade tools used in modern drug discovery pipelines.
What You Will Get Access To?
1. Small Molecule Design
Design and explore potential drug molecules using AI-driven molecular design tools. Students can generate new compounds and evaluate their potential as drug candidates.
2. Virtual Screening
Screen thousands of molecules computationally to identify promising drug candidates faster than traditional laboratory screening.
3. QSPR Modelling
Use Quantitative Structure-Property Relationship (QSPR) models to predict chemical and biological properties of molecules based on their structures.
4. Molecular Generation
Leverage advanced AI models to generate novel molecules that may serve as potential drug leads for specific biological targets.
5. Molecular Optimization
Optimize molecules for important drug-like properties such as:
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Potency
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Stability
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ADMET properties
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Toxicity reduction
6. Molecular Dynamics Simulation (MDS)
Simulate how molecules behave in biological environments and study interactions with proteins or targets over time.
7. Drug Discovery Toolbox
Access additional computational tools within the platform to support structure analysis, modeling, and drug discovery workflows.
Working with this platform allows you to gain hands-on experience with real AI drug discovery tools, something that most academic courses do not provide.
You will learn how modern pharmaceutical and biotech companies use AI + computational chemistry to accelerate drug development.
This access is provided exclusively for students enrolled in the AI-ML in Drug Discovery training program.
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 & Cheminformatics 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.
