What does the Generative AI in Drug Discovery course cover?

The course covers generative artificial intelligence for molecular design, chemical-data preparation, molecular representations, property prediction, virtual screening, drug-likeness assessment, ADMET analysis, candidate prioritization, and lead optimization.

Who should take this generative AI drug discovery course?

The course is suitable for students, researchers, academicians, pharmaceutical scientists, biotechnology professionals, computational chemists, bioinformaticians, data scientists, and learners interested in applying artificial intelligence to drug discovery.

Is this course suitable for beginners?

Yes. The course is designed at a beginner level and introduces the essential concepts of generative AI, molecular data, computational drug discovery, and pharmaceutical research workflows before progressing to practical applications.

What is generative AI in drug discovery?

Generative AI in drug discovery uses machine-learning models to create or propose new molecular structures with desired chemical, biological, and pharmaceutical properties. These models can support compound ideation, lead discovery, and molecular optimization.

Does the course cover molecular generation and de novo drug design?

Yes. Learners are introduced to generative approaches for proposing new molecules, representing chemical structures, defining molecular objectives, evaluating generated compounds, and prioritizing candidates for further investigation.

Are virtual screening and ADMET prediction included?

Yes. The course explains how virtual screening, molecular-property prediction, drug-likeness evaluation, and ADMET assessment can be used to identify and prioritize promising drug candidates.

What skills can learners gain from this course?

Learners can develop foundational skills in molecular-data preparation, AI-assisted molecule generation, property prediction, virtual screening, candidate evaluation, drug-likeness analysis, ADMET interpretation, and AI-supported drug discovery workflow design.

Generative AI In Drug Discovery: From

Attribute

Detail

Format

Recorded Lectures (Self-Paced)

Level

Beginner

Duration

3 Days (1.5 Hour/day)

Certification

e-Certification + e-Marksheet

Tools

AutoDock Vina, PyRx, Schrödinger Suite, GROMACS, ChemDraw, Discovery Studio

About the Generative Ai In Drug Discovery Course

This 3-day intensive course explores how Generative AI is transforming modern drug discovery—from molecular design to clinical translation.

With 1.5 hours of expert-led lecture each day supported by guided demonstrations and conceptual hands-on sessions, participants will gain practical exposure to AI-driven molecular modeling, virtual screening, predictive analytics, and translational considerations. The course bridges artificial intelligence, cheminformatics, and biomedical research to equip participants with industry-relevant computational skills for next-generation drug development.

Program Highlights

• Comprehensive coverage of Generative AI in Drug Discovery from fundamentals to advanced applications

• Hands-on projects and real-world case studies in Drug Discovery & Design

• Expert-curated curriculum aligned with current industry standards

• Access to recorded lectures and e-LMS platform for flexible, self-paced learning

• e-Certification and e-Marksheet upon successful completion

• Dedicated mentor support and interactive doubt-clearing sessions

• Exposure to industry-standard tools and platforms used in Drug Discovery & Design

• Career-oriented training for academic and professional growth in Drug Discovery & Design

Course Curriculum

Module 1: Introduction to Generative AI in Drug Discovery

Overview and historical evolution of Generative AI in Drug Discovery
Key terminology, definitions, and core concepts in Drug Discovery & Design
Current industry landscape, trends, and career opportunities
Setting up the learning environment and essential tools

Module 2: Fundamentals and Theoretical Foundations

Core principles and scientific/theoretical underpinnings of Generative AI in Drug Discovery
Mathematical and analytical frameworks relevant to Drug Discovery & Design
Comparative analysis of major approaches and methodologies
Understanding key standards, guidelines, and best practices

Module 3: From Molecules to Medicines

Core concepts and techniques in From Molecules to Medicines
Practical implementation and hands-on exercises
Integration of From Molecules to Medicines with Generative AI in Drug Discovery workflows
Case study: Real-world application of From Molecules to Medicines

Module 4: Molecular Docking

Introduction to Molecular Docking concepts and methodologies
Step-by-step practical implementation of Molecular Docking techniques
Tools and platforms commonly used for Molecular Docking
Troubleshooting, optimization, and best practices

Module 5: Virtual Screening

Introduction to Virtual Screening concepts and methodologies
Step-by-step practical implementation of Virtual Screening techniques
Tools and platforms commonly used for Virtual Screening
Troubleshooting, optimization, and best practices

Module 6: Advanced Topics and Emerging Trends in Drug Discovery & Design

Cutting-edge research and innovations in Generative AI in Drug Discovery
Integration with AI, automation, and modern technologies
Industry case studies and real-world problem solving
Future directions and career pathways in Drug Discovery & Design

Module 7: Capstone Project and Assessment

End-to-end project implementation using Generative AI in Drug Discovery skills
Peer review, collaborative exercises, and expert feedback
Portfolio-ready project documentation and presentation
Final assessment and course completion evaluation

Tools, Techniques, or Platforms Covered

AutoDock Vina
PyRx
Schrödinger Suite
GROMACS
ChemDraw
Discovery Studio
ADMET Predictor

Real-World Applications

Apply Generative AI in Drug Discovery skills directly to academic research, thesis work, and publications
Build a professional portfolio showcasing practical Drug Discovery & Design competencies
Solve industry-relevant problems using Generative AI in Drug Discovery methodologies and tools
Contribute to open-source projects and collaborative research in Drug Discovery & Design
Prepare for competitive examinations, interviews, and professional certifications in Drug Discovery & Design

Who Should Attend & Prerequisites

Students pursuing degrees in Drug Discovery & Design, science, engineering, or related disciplines
Working professionals seeking to upskill or transition into Drug Discovery & Design roles
Researchers and academicians looking to adopt modern techniques in Drug Discovery & Design
Entrepreneurs, freelancers, and self-learners interested in practical Drug Discovery & Design knowledge

Prerequisites: No prior experience in Drug Discovery & Design is required. Basic computer literacy and a stable internet connection are sufficient. This course is designed to be beginner-friendly.

Frequently Asked Questions

1. What is the format of this Generative AI in Drug Discovery: From Molecules to Medicines course?

This is an Recorded Lectures (Self-Paced) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.

2. Will I receive a certificate after completing this course?

Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from NanoSchool (NSTC) that you can showcase on your CV and LinkedIn profile.

3. What are the prerequisites for this course?

No prior experience is required. This course is designed for beginners and takes you step by step from the basics to advanced topics.

4. How long will I have access to the course materials?

You will have access to all course materials for the duration of 3 Days (1.5 Hour/day). The self-paced format allows you to learn according to your own schedule through our online learning management system.

5. Is mentor support available during the course?

Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to Drug Discovery & Design. Our mentors are industry experts and experienced professionals.

Enroll in Generative AI in Drug Discovery: From Molecules to Medicines today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering Drug Discovery & Design skills that matter.

Brand	NSTC
Format	Recorded Lectures
Duration	3 Days (1.5 Hour/day)
Level	Beginner
Hands-On	Yes – Practical projects with industrial datasets

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Generative AI in Drug Discovery: From Molecules to Medicines