Workshop Registration End Date :25 Feb 2026

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Virtual Workshop

Generative AI in Drug Discovery: From Molecules to Medicines

Designing the future of medicine with Generative AI.

Skills you will gain:

About Workshop:

This 3-day intensive workshop 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 workshop bridges artificial intelligence, cheminformatics, and biomedical research to equip participants with industry-relevant computational skills for next-generation drug development.

Aim:

To provide participants with conceptual foundations and practical skills in applying Generative AI tools and machine learning techniques to modern drug discovery workflows.

Workshop Objectives:

  • To understand the complete drug discovery pipeline from target identification to clinical stages
  • To introduce generative AI models (transformers, diffusion models) for molecular design
  • To apply AI tools for literature mining, hypothesis generation, and evidence synthesis
  • To perform molecular representation, screening, and drug-likeness prediction
  • To understand QSAR, ADMET prediction, and structure–property relationships
  • To explore AI-assisted docking and protein structure prediction
  • To examine translational, ethical, and regulatory considerations in AI-driven drug discovery

What you will learn?

Day 1: Foundations of Generative AI in Drug Discovery

  • Introduction to Drug Discovery Pipelines: From target identification to clinical translation
  • Overview of Generative AI in Life Sciences: Large Language Models (LLMs), molecular generators, and predictive AI
  • AI for Scientific Literature & Evidence Mining: Hypothesis generation, systematic reviews, and trend analysis
  • Understanding Molecular Data: SMILES, chemical descriptors, biological datasets, and drug-likeness concepts
  • Ethical and Responsible Use of AI in Drug Discovery and Biomedical Research

Hands-on Tools & Platforms:

  • ChatGPT / Gemini (scientific reasoning, hypothesis generation)
  • Consensus, SciSpace, Elicit (AI-assisted literature analysis)
  • Notebook LLM (working with research papers and datasets)
  • Python basics for molecular data handling
  • Pandas & Matplotlib for exploratory data visualization
  • Jupyter / Google Colab for interactive workflows

Day 2: Molecular Design, Screening & Optimization Using AI

  • AI-Driven Molecular Representation: SMILES-based learning and embeddings
  • Generative Models for Molecule Design: Conceptual understanding of transformers and diffusion models
  • Virtual Screening & Drug-Likeness Prediction: ADMET, QSAR, and bioactivity prediction
  • Structure–Property Relationships in Drug Optimization
  • AI-Assisted Molecular Docking and Binding Affinity Prediction

Hands-on Tools & Platforms:

  • RDKit (molecular fingerprints, visualization, and descriptors)
  • DeepChem (QSAR modeling and screening workflows)
  • PASS Online (biological activity prediction)
  • ChemBERTa (transformer-based molecular understanding – demo-based)
  • DiffDock (AI-powered docking – conceptual + demo)
  • Scikit-learn, NumPy, Pandas for model building and evaluation
  • Matplotlib for performance and result visualization

Day 3: Translation, Clinical Relevance & Future Directions

  • Clinical Applications of AI-Driven Drug Discovery: Oncology, infectious diseases, rare diseases
  • AI in Natural Product & Traditional Medicine-Based Drug Discovery
  • Translational Challenges: Model interpretability, validation, bias, and reproducibility
  • Regulatory & Ethical Considerations: AI governance, FDA perspectives, and compliance challenges
  • Future Trends: Foundation models, multimodal AI, personalized medicine, and AI-native drug design

Hands-on Tools & Platforms:

  • AlphaFold (protein structure prediction – workflow demonstration)
  • TensorFlow / Keras (introductory model workflows)
  • Scikit-learn (model deployment concepts)
  • Streamlit (building simple AI-driven interfaces for drug discovery insights)

Mentor Profile

Fee Plan

StudentINR 1699/- OR USD 70
Ph.D. Scholar / ResearcherINR 2699/- OR USD 80
Academician / FacultyINR 3699/- OR USD 95
Industry ProfessionalINR 4699/- OR USD 110

Important Dates

Registration Ends
25 Feb 2026 Indian Standard Timing 07:00 PM
Workshop Dates
25 Feb 2026 to
27 Feb 2026  Indian Standard Timing 08:00 PM

Get an e-Certificate of Participation!

2024Certfiacte

Intended For :

  • Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
  • Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
  • University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
  • Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
  • Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.

Career Supporting Skills

Workshop Outcomes

By the end of the workshop, participants will be able to:

  • Explain how generative AI integrates into pharmaceutical R&D pipelines
  • Use AI tools for scientific literature mining and hypothesis generation
  • Handle molecular datasets using SMILES and cheminformatics tools
  • Perform exploratory data analysis and build basic predictive models
  • Apply AI-based virtual screening and QSAR approaches
  • Interpret AI-assisted docking and protein structure prediction workflows
  • Understand regulatory and ethical challenges in AI-driven biomedical innovation
  • Conceptualize AI-enabled drug discovery projects for research or industry applications