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AI ML in Drug Discovery, Bio-Pharma & Chemistry Hands-on Training Program With 3, 6 & 12 Months LIVE Project Work

Original price Rs. 6,995.00 - Original price Rs. 48,995.00
Original price
Rs. 6,995.00
Rs. 6,995.00 - Rs. 48,995.00
Current price Rs. 6,995.00

AI ML in Drug Discovery, Bio-Pharma Hands-on Training Program

 With 3, 6 & 12 Months LIVE Project Work

WORK IN PROJECTS | PUBLISH PAPERS | GET WORK EXPERIENCE in AI ML 

Starts 11th NOV 2024

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: 11th Nov 2024
  • Time: 7-8 PM IST
  • Venue: Online
  • Project work: 3,6, & 12 months duration

Why Join This Training?

  1. Expert-Led Instruction: Learn from industry leaders and academic experts who bring practical knowledge and research-based insights into AI and ML applications.

  2. 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.

  3. 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.

  4. 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 Chemistry and Pharma?

  1. Stay Ahead of the Curve: AI/ML is revolutionizing the pharma industry, enabling faster drug discovery, personalized treatments, and more efficient R&D processes.

  2. High Demand Skills: With the rise of AI/ML in the industry, professionals skilled in these areas are in high demand across the globe.

  3. Innovative Research Opportunities: AI/ML offers novel approaches to tackle complex problems in chemistry, such as molecular design and toxicity prediction.


Eligibility

  • Educational Background: Ideal for students and professionals with a background in Biotech, Life Sciences, Chemistry, Pharmaceuticals, Bioinformatics, Computer Science, or related fields.

  • Prerequisites: Basic understanding of Drug Discovery chemistry and computational tools. Prior experience in programming or data science is beneficial but optional.


Meet Your Instructor

Dr. Dolly Sharma
PhD | Chembiotech
Lead Scientist in Cheminformatics and Drug Discovery

Dr. Dolly Sharma is a distinguished expert in cheminformatics, drug design, and discovery, with a Ph.D. in Biotechnology from Amity University and an M.Tech. from Gautam Buddha University. Her groundbreaking research focuses on the design of biorelevant small molecules aimed at cancer treatment, which has led to the filing of three patents and the publication of over ten highly cited research papers.

Dr. Sharma has made significant strides in drug design and discovery, particularly in the development of DNA origami-based nanodevices and the establishment of advanced synthesis methodologies. Her expertise and innovative approach have been recognized through the successful acquisition of research grants totaling ₹92 lakhs from DST SERB, DBT, and UPCST. Additionally, she has secured an impressive ₹55 lakhs from the DBT Wellcome Alliance and EMBO for organizing a key scientific event.

With a profound understanding of cheminformatics and a proven track record in project management, Dr. Sharma is dedicated to pushing the boundaries of scientific discovery and is eager to bring her extensive knowledge and experience to your team.
30-Day Course Curriculum

Week 1: Foundations and Introduction to Tools

  • Day 1: Introduction to AI, ML, and Cheminformatics
  • Day 2: Data Handling and Preprocessing
  • Day 3: Molecular Descriptors and Fingerprints
  • Day 4: Introduction to Machine Learning Algorithms
  • Day 5: Model Evaluation and Validation

Week 2: Advanced Techniques in Cheminformatics and ML

  • Day 6: Quantitative Structure-Activity Relationship (QSAR)
  • Day 7: Molecular Docking and Virtual Screening
  • Day 8: Deep Learning in Cheminformatics
  • Day 9: Neural Networks for Drug Discovery
  • Day 10: Generative Models for Molecular Design
  • Day 11: Cheminformatics Data Integration and Workflow Automation
  • Day 12: Feature Engineering in Cheminformatics

Week 3: Specialized Applications and Case Studies

  • Day 13: AI for Predicting ADMET Properties
  • Day 14: AI in Pharmacophore Modeling
  • Day 15: Machine Learning in Toxicology Prediction
  • Day 16: AI-Driven Target Identification
  • Day 17: Case Study: AI in COVID-19 Drug Discovery
  • Day 18: Computational Chemistry and Molecular Dynamics
  • Day 19: Chemoinformatics for Natural Products
  • Day 20: Molecular Libraries and Databases

Week 4: Future Trends, Projects, and Presentations

  • Day 21: AI in Personalized Medicine
  • Day 22: Current Trends in AI/ML for Chemistry and Pharma
  • Day 23: Regulatory Considerations in AI-driven Drug Discovery
  • Day 24: Advanced AI Techniques: Reinforcement Learning in Drug Design
  • Day 25-27: Project Work (Model Design, Development, Testing, Optimization)
  • Day 28: Project Presentations
  • Day 29: Course Recap and Future Directions
  • Day 30: Optional Workshop/Networking Event

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.


Project Topics for AI & ML in Chemistry and Pharma

1. Prediction of Drug-Likeness Using Machine Learning on Molecular Descriptors
2. In Silico Screening of Natural Compounds for Anti-Cancer Activity
3. Cheminformatic approaches for Drug Design and Drug Discovery
4. AI-Assisted Virtual Screening of Potential Inhibitors for SARS-CoV-2 Main Protease
5. AI-Assisted Virtual Screening of Potential Inhibitors for SARS-CoV-2 Mpox
6. Molecular Docking and Machine Learning-Based Analysis of Anti-Inflammatory
Compounds
7. Molecular Docking and Machine Learning-Based Analysis of Antiviral Comppunds
8. Exploring the Use of Deep Learning for Predicting Toxicity in Chemical Compounds
9. Application of Neural Networks in Predicting the Solubility of Pharmaceutical
Compounds
10. AI-Driven Prediction of Drug-Target Interactions for New Chemical Entities
11. Computational Prediction of Binding Affinity for Enzyme-Substrate Interactions
12. AI-Assisted Designing of Novel Anticancer Compounds
13. Application of AI in the Prediction of Protein-Ligand Binding Sites
14. Virtual Screening and Docking Studies of Phytochemicals Against Cancer Targets
15. AI/ML-Assisted Molecular Modelling Studies for Protein Stability Analysis
16. Cheminformatics Approaches to Predict the Reactivity of Organic Compounds
17. AI-Assisted Approaches for Structure based Drug Design
18. AI-Assisted Approaches for Target based Drug Design
19. Machine Learning Models for Predicting Drug Side Effects Based on Chemical
Structure
20. Predictive Modeling of Drug Permeability Using AI Techniques
21. In Silico Prediction of Allosteric Sites in Proteins Using Machine Learning
22. AI-Based Screening of Bioactive Compounds from Marine Natural Products
23. Cheminformatics Approaches to Discovering New Antimalarial Compounds
24. AI-Driven Identification of Potential Drug Synergies for Combination Therapy
25. In Silico Study of Pharmacophore Models for Target-Based Drug Design

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.

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