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Machine Learning in Computational Drug Discovery
Jul 1, 2024
Machine Learning in Computational Drug Discovery 💊
Introduction
Presenter:
Head of the Center of Data Mining and Biomedical Informatics
Background:
127 research articles, 17 review articles, 5 book chapters
Roles:
Associate professor, YouTuber, blogger
YouTube Channels: Data Professor, Coding Professor
Blog: Medium (Data Science and Bioinformatics)
Focus: Simplifying Data Science and Bioinformatics for beginners
Challenges in Bioinformatics and Data Science
Awareness Issue:
Lack of familiarity with Scopus, LCBC databases
Technical Jargon:
Papers often use complex terms, making practical application difficult
Example: Random forest for bioactivity prediction goes unnoticed by practitioners
Outreach:
Blogs and YouTube reach wider audience more effectively than journals
Blog example: Article on mastering scikit-learn received 10,000 views in a month versus journal articles with few hundred reads per year
Impact:
Need for research to reach general public and practitioners for practical application
Disease and Drug Discovery Basics
Definition of Disease:
Illness caused by malfunction or infection affecting health
Role of Drugs:
Biological or chemical entities (proteins, peptides, small molecules)
Types:
Biological (e.g., antibodies) vs. Chemical (small molecules/compounds)
Drug Discovery Process:
Interaction between target protein and drug
Goal:
Inhibit or activate protein function, typically to inhibit for disease treatment
Drug Target Networks
Bioinformatics:
Studies protein-protein interactions and biological networks
Node Analysis:
Key proteins marked by connections
Pathway Analysis:
Understanding side-effects and rate-limiting steps
Example:
Aromatase inhibition to reduce estrogen levels in breast cancer
Drug Networks:
Analyze how drug interactions affect target and off-target proteins
Off-Target Binding:
Can lead to side-effects or serendipitous therapeutic benefits
Polypharmacology:
Drugs may affect multiple proteins causing various effects
Drug Discovery Process
Phases:
Takes 10-15 years, high failure rate (~90%), and costly (~$2 billion)
Steps: Target Discovery, Screening, Lead Optimization
High Throughput Screening:
Identify potential hits
Hit to Lead:
Optimize hits by modifying functional groups
Example:
Lead optimization through structure modification (analog generation)
Key Considerations:
Ki and IC50 values for determining compound effectiveness
Databases:
Example - Shambo, PubChem for bioactivity data
Computational Approaches
Tools and Databases:
High Throughput Screening, Virtual Screening, Molecular Docking
Molecular Descriptor Calculation:
Assess physical and chemical properties
Machine Learning:
Build prediction models (QSAR, PCM)
Conceptual Workflow:
From data collection, descriptor calculation, model building, to evaluation
Bayesian Models:
Focused on predicting bioactivity and toxicity from molecular descriptors
Approaches in Chemical Space
Nature-Inspired Drug Design:
Utilizing natural compounds as initial hits
Compound Enumeration:
Generating new compounds by modifying functional groups
Chemical Space:
Represents all possible compounds (e.g., 166 billion hypothetical compounds)
Lipinski Rule of Five:
Criteria for assessing drug-likeness
Lead-like Rule of Three:
Criteria for identifying promising lead compounds
Key Considerations in QSAR and PCM
QSAR:
Correlates molecular features with biological activity
Workflow:
Selecting activity, generating descriptors, applying ML models, performance evaluation
PCM:
Expansion of QSAR to multiple proteins
Applications:
Drug repositioning, off-target effect analysis
Utilization of Computational Drug Discovery
Prediction Models:
Assess structure-activity relationships, predict adverse effects
Practical Tools:
Software (e.g., PDB, PyMOL, scikit-learn)
Open Resources:
Utilization of free tools like Google Colab, R, Python for computational tasks
Summary and Conclusion
Data Integration:
Combining various omics data for enriched model development
Educational Resources:
Accessibility via blogs, YouTube channels
Impact of Research:
Reaching wider audiences through open access platforms and simplified learning aids
Q&A Highlights:
Scientists from both academia and pharmaceutical companies contribute to drug discovery, with patents playing a critical role in commercial viability.
Computational approaches enable drug repurposing by leveraging existing data for new therapeutic uses.
Building predictative models demands a mix of domain knowledge and computational skills, making step-by-step learning essential.
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