Antibody–Drug Conjugates: Target Selection, Linker–Payload Design & AI Toxicity Prediction

This 3-day virtual hands-on workshop introduces participants to the design and evaluation of Antibody–Drug Conjugates (ADCs) for targeted therapeutics and precision drug delivery. Participants will learn how to identify suitable disease targets, assess tissue-specific expression, explore cytotoxic payloads and linker strategies, and use cheminformatics and machine learning to predict toxicity and prioritize safer ADC candidates. Guided computational exercises will integrate cBioPortal, Human Protein Atlas, ChEMBL, RDKit, XGBoost, and SHAP into a practical ADC discovery workflow.

The aim of this workshop is to provide participants with a practical understanding of ADC target selection, linker–payload design, and AI-assisted toxicity prediction . The workshop focuses on integrating cancer genomics, protein-expression data, chemical informatics, and interpretable machine learning for precision therapeutic development.

• Understand the fundamental structure, mechanism, and therapeutic principles of Antibody–Drug Conjugates.
• Identify and evaluate disease-associated targets using genomic and tissue-expression databases.
• Explore ADC linker chemistry, cytotoxic payload classes, and structure–property relationships.
• Apply cheminformatics and machine learning to predict toxicity and prioritize payload candidates.
• Use explainable AI to interpret toxicity-driving molecular features and support rational ADC design.

📅 Day 1: ADC Biology, Target Selection & Precision-Therapeutic Profiling

• Fundamentals of Antibody–Drug Conjugates and targeted therapeutics ADC components: antibody, linker, payload, and conjugation strategy Target binding, internalization, linker cleavage, and payload release Ideal ADC target characteristics and tumor-specific antigen expression Genomic alterations, target prevalence, and normal-tissue off-target risk Precision-oncology applications and target-prioritization principles
• 🛠️ Hands-on:
• Explore candidate targets using cBioPortal Assess tumor and normal-tissue expression using Human Protein Atlas Compare genomic prevalence, selectivity, and biological relevance Prepare a ranked shortlist of potential ADC targets
• 🧰 Tools Covered:
• cBioPortal, Human Protein Atlas (HPA), cancer genomics datasets, and target-ranking templates

📅 Day 2: Linker–Payload Design, Chemical Space & Molecular Profiling

• Cleavable and non-cleavable linker strategies Payload-release mechanisms and major cytotoxic payload classes Microtubule inhibitors, DNA-damaging agents, and topoisomerase inhibitors Potency, permeability, bystander effect, and therapeutic window Drug-to-antibody ratio and conjugation considerations Structure–activity and structure–property relationships Molecular descriptors influencing payload toxicity and developability
• 🛠️ Hands-on:
• Retrieve cytotoxic compounds and activity data from ChEMBL Generate molecular descriptors using RDKit Analyse molecular weight, logP, TPSA, and hydrogen-bond properties Compare payload chemical profiles and visualize chemical space Prepare an AI-ready payload dataset
• 🧰 Tools Covered:
• ChEMBL, RDKit, Python, pandas, Google Colab, and molecular-property datasets

📅 Day 3: AI Toxicity Prediction, Explainable AI & ADC Candidate Prioritization

• On-target, off-target, and payload-related toxicity Linker instability and premature payload release Machine learning for molecular toxicity prediction Feature engineering and XGBoost-based modelling Model validation and performance evaluation Explainable AI using SHAP Multi-parameter ADC prioritization Balancing target selectivity, payload potency, safety, and developability Translational considerations in ADC development
• 🛠️ Hands-on:
• Train an XGBoost model using molecular descriptors Evaluate toxicity-prediction performance Identify toxicity-driving features using SHAP Compare payload candidates by predicted safety Integrate target, payload, and toxicity data Rank preliminary ADC candidates for further development
• 🧰 Tools Covered:
• Google Colab, Python, RDKit, XGBoost, SHAP, pandas, scikit-learn, and ADC-ranking templates

• Explain the structure and mechanism of modern Antibody–Drug Conjugates.
• Identify potential ADC targets using cancer genomic and protein-expression data.
• Evaluate tumor specificity and potential off-target expression.
• Understand major linker strategies and payload classes used in ADC development.
• Retrieve and curate cytotoxic compounds from public chemical databases.
• Generate molecular descriptors using RDKit.
• Build AI-ready datasets for toxicity prediction.
• Train XGBoost models for molecular toxicity analysis.

🎓 Graduate & Postgraduate Students Suitable for students from: Biotechnology,Biochemistry,Bioinformatics,Pharmaceutical Sciences,Pharmacology,Life Sciences,Chemistry,Biomedical Sciences,Computational Biology 🔬 PhD Scholars & Researchers Useful for researchers working in: Cancer biology,Precision oncology,Targeted drug delivery,Antibody engineering,Drug discovery,Medicinal chemistry,Cheminformatics,Computational toxicology,Molecular pharmacology 👨‍🏫 Academicians & Faculty Suitable for faculty interested in: Translational cancer research,Precision therapeutics,Drug-discovery workflows,Computational pharmacology,AI-assisted drug development,Research and curriculum development 💼 Industry Professionals Relevant for professionals from: Pharmaceutical companies,Biotech companies,Oncology drug-development teams,Antibody-development groups,Drug-safety and toxicology teams,Computational chemistry groups,AI/ML drug-discovery teams,Translational research and preclinical development