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Machine Learning in Drug Discovery.

The London College, Cranford

Dr. Anum Munir is a PhD computational biologist specialising in multiomics integration and acquired drug resistance. She is the creator of the Resistant Cancer Cell Line (RCCL) database and has authored over 67 peer-reviewed publications in high-impact peer-reviewed journals.

Currently a computational biology/bioinformatics researcher (honorary) at the University of Kent and a lecturer at The London College UCK, Dr Anum has extensive expertise in building bioinformatics pipelines using Python, R, and Bash. Her mentorship focuses on helping clinicians and scientists apply machine learning to solve complex problems in oncology and drug discovery.

Main Areas of Interest

• Machine learning applications in drug discovery and development
• Predictive modeling of drug response and resistance
• Integration of multi-omics data for therapeutic optimization
• Pharmacokinetic/pharmacodynamic (PK/PD) modeling using computational approaches
• Biomarker-driven precision medicine

Specialties

Drug Development

Computational Biology

Techniques You Will Learn

• Fundamentals of machine learning (supervised, unsupervised learning) in biomedical data
• Data preprocessing, feature selection, and dimensionality reduction for omics datasets
• Building predictive models for drug response using Python/R
• Integration of genomics, transcriptomics, and drug sensitivity data
• PK/PD modeling and its application in optimizing therapeutic outcomes
• Model evaluation, validation, and interpretation in a biomedical context
• Data visualization and communication of computational results

Modules

1. Study Design & Data Preparation for Machine Learning
Overview of drug discovery pipelines and how ML fits into them. Data types (omics, clinical, drug response) and preprocessing, feature engineering, and dataset curation.

2. Machine Learning Models for Drug Response Prediction
Introduction to key ML algorithms (regression, classification, clustering) and their application in predicting drug sensitivity and resistance.

3. Multi-Omics Integration & PK/PD Modeling
Combining genomics and transcriptomics with pharmacological data. Application of ML in PK/PD modeling and therapeutic optimization.

4. Biomarker Discovery & Model Interpretation
Identifying predictive biomarkers using ML. Model validation, interpretation, and translation into clinically meaningful insights.

Program Fee


Total Number of Modules	4
Time duration of Each module	45 minutes
Total Program Cost	$502.5

Who Can Apply

Postgraduate students (Master’s/PhD) in Bioinformatics, Computational Biology, Data Science, Biotechnology, Pharmacy, or related fields
Early-career researchers, postdoctoral fellows, and research scientists interested in drug discovery and precision medicine
Professionals from pharmaceutical, biotech, or healthcare sectors seeking to apply machine learning in biomedical research
Individuals with a basic understanding of molecular biology, pharmacology, and statistics
Prior experience with programming (Python or R) and data analysis is recommended but not mandatory

Host Details

Host Name: Anum Munir

Affiliation: The London College, Cranford

Address: The London College, Cranford

Website URL:

Disclaimer:It is mandatory that all applicants carry workplace liability insurance, e.g., https://www.protrip-world-liability.com (Erasmus students use this package and typically costs around 5 € per month - please check) in addition to health insurance when you join any of the onsite Trialect partnered fellowships.

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