Multimodal AI for Drug Discovery: AlphaFold to Generative Therapeutics
From Protein Structures to AI-Designed Drugs—Redefining Discovery with Multimodal Intelligence
About This Course
Drug discovery is undergoing a paradigm shift with the integration of multimodal AI, which combines diverse data types such as protein structures, genomic data, chemical properties, and clinical insights. Breakthroughs like AlphaFold have revolutionized protein structure prediction, enabling researchers to understand molecular interactions with unprecedented accuracy. However, the next frontier lies in integrating these structural insights with generative AI models to design novel therapeutics efficiently.
This workshop explores the complete AI-driven drug discovery pipeline—from target identification and protein modeling to molecular docking, generative drug design, and lead optimization. Participants will learn how multimodal models integrate different biological data layers and how generative AI can create new drug candidates. The program focuses on dry-lab workflows using Python-based tools, preparing participants for next-generation pharma and biotech innovation.
Aim
This workshop aims to introduce participants to multimodal AI approaches in modern drug discovery, integrating protein structure prediction, omics data, and generative models. It focuses on leveraging tools like AlphaFold alongside AI-driven molecular design for therapeutic innovation. Participants will learn how to combine structural, chemical, and biological data for end-to-end drug discovery pipelines. The program bridges computational biology, AI, and pharmaceutical research.
Workshop Objectives
- Understand multimodal AI concepts in drug discovery.
- Learn protein structure prediction using tools like AlphaFold.
- Explore molecular docking and interaction analysis.
- Apply generative AI for novel drug design.
- Integrate multi-source biological data for therapeutic development.
Workshop Structure
Day 1: Foundations & AlphaFold
- Retrieval and preprocessing of protein sequences from UniProt & FASTA pipelines
- Running AlphaFold/OpenFold inference on selected protein targets (Colab/GPU)
- Structural validation using pLDDT scores & confidence metrics
- Visualization of predicted structures using PyMOL / NGL Viewer
- Extraction of secondary structure elements (α-helix, β-sheet analysis)
- Comparative analysis with experimental PDB structures (RMSD calculation)
- Feature extraction from protein structures for ML (distance maps, contact maps)
- Building sequence-to-structure embeddings using Transformer models
Day 2: Multimodal Integration & Drug Design
- Preparation of ligand datasets from PubChem/ChEMBL (SMILES standardization)
- Conversion of molecules into graph representations (RDKit + GNN-ready inputs)
- Protein-ligand docking using AutoDock Vina / DeepDock pipelines
- Binding affinity estimation and scoring function comparison
- Integration of protein embeddings + molecular descriptors (multimodal fusion)
- Training a basic GNN/Transformer model for interaction prediction
- Generating novel molecules using VAE/Diffusion-based models
- ADMET property prediction using AI-based screening tools
Day 3: Advanced Pipelines & Real-World Deployment
- Designing an end-to-end AI drug discovery workflow (target → lead)
- Protein-protein interaction prediction using AlphaFold-Multimer outputs
- Building a multimodal dataset (omics + structure + chemical data)
- Implementing cross-modal attention models for drug-target prediction
- Explainability analysis using SHAP / attention visualization techniques
- Virtual screening of compound libraries using AI + docking hybrid pipeline
- Benchmarking models using ROC-AUC, RMSE, enrichment factors
- Deployment of trained models using Streamlit / API-based inference system
Important Dates
Registration Ends
04/06/2026
IST 7:00 PM
Workshop Dates
04/06/2026 – 04/08/2026
IST 8:00 PM
Workshop Outcomes
Participants will be able to:
- Understand how multimodal AI integrates biological and chemical data.
- Use AlphaFold-based insights for structural analysis.
- Apply AI models for molecular design and optimization.
- Analyze protein–ligand interactions computationally.
- Build AI-driven pipelines for drug discovery workflows.
Fee Structure
Student Fee
₹2499 | $60
Ph.D. Scholar / Researcher Fee
₹3499 | $70
Academician / Faculty Fee
₹4499 | $80
Industry Professional Fee
₹5499 | $90
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
View All Feedbacks →