About Molecular AI Specialization
A 6-9 month specialized curriculum designed to help you gain advanced skills in applying machine learning and artificial intelligence to small-molecule drug discovery and build a successful career in this field.
4 min read
October 12th, 2023
What is Neovarsity's Molecular AI Specialization?
Molecular AI Specialization is a specialized program offered by Neovarsity that guides individuals in applying machine learning and artificial intelligence to advance small-molecule drug discovery processes.
This 6-9 months, intensive specialization caters to a diverse group of professionals including but not limited to:
- Computer-aided Drug Designers and Computational Chemists
- Medicinal Chemists and Organic Chemists
- Chemical Biologists
- Machine Learning Engineers
- Entrepreneurs building TechBio ventures
- Those intrigued by the intersection of AI and drug discovery
What is included?
The curriculum is thorough and covers key components:
I. Python Foundation (optional)
Acquiring hands-on experience with Python is essential for success in this domain, and this course is offered specifically for those who may lack prior Python programming experience.
This curriculum is entirely text-based, providing a thorough introduction to fundamental Python programming concepts in a well-structured manner, ensuring a quick and effective grasp of the essential concepts.
II. Advanced Cheminformatics for AI/ML
This is an intensive module and covers the entire cheminformatics pipeline. It aims to equip you with the necessary tools and concepts crucial for success.
This serves as the foundation, and a robust cheminformatics experience proves highly beneficial in developing expertise in small-molecules ML/AI.
III. Advanced Machine Learning for Drug Discovery
This module equips you with skills for implementing machine-learning algorithms in small-molecule drug discovery.
It addresses critical topics such as handling biases in molecular data modeling and explores advanced concepts in explainable and interpretable machine learning.
This is also an intensive module and is more focussed on practical implementation.
IV. Deep Learning and Reinforcement Learning for Drug Discovery
This is another intensive module where you will explore deep learning and reinforcement learning, cutting-edge areas revolutionizing drug discovery.
You'll gain foundational skills, understand theoretical backgrounds, navigate deep learning frameworks, and master theories and implementation of reinforcement learning.
The goal is to empower you to advance small-molecule drug discovery through artificial intelligence. As a learner, you will actively build your own models from scratch, cultivating a profound understanding of these tools.
This specialization highlights hands-on experience with essential packages such as RdKit, scikit-learn, Keras/Tensorflow, and more.
To achieve optimal success in this curriculum, we suggest the following path:
Begin by attaining proficiency in Python programming. This is an optional step in case you're already proficient with Python.
Next, enhance your understanding of cheminformatics before progressing to complete the Machine Learning for Drug Discovery module.
Afterward, explore the application of artificial intelligence, deep learning, and reinforcement learning in the context of small-molecule drug discovery.
Curriculum breakdown
I. Python Foundation
- Handling variables and data types
- Controlling program flow
- Creating functions
- Working with lists and loops
- Utilizing dictionaries for associations
- Managing files
- Exploring modules as toolkits
- Implementing basic error handling
II. Advanced Cheminformatics
- Chemical File Formats
- Conversion Methods
- Data Curation
- Search and Filtering Strategies
- Molecular Descriptor Analysis
- Common Substructure Analysis
- MinMax Diversity
- Chemical Similarity Assessment
- Activity Cliffs
- Molecular Graphs
- Clustering Techniques
- QSAR Modeling
- Chemical Databases
- Ring Mining
- Structure Transformations
- Combinatorial Library Construction
- Bioisoster Shape Similarity Analysis
- Virtual Screening
III. Machine Learning for Drug Discovery
- Exploratory Molecular Data Analysis
- Handling Partitioning
- Handling Chemical Data Bias
- Feature Selection
- Classification and Regression Model Development
- Ensemble Models
- Hyperparameters Optimization
- Bias Handling
- Explainable & Interpretable ML Techniques
- Feature Importance
IV. Deep and Reinforcement Learning for Drug Discovery
- Introduction to Deep Learning
- Neural Networks
- Fundamental Concepts
- Optimization Algorithms
- Activation Functions
- Model Evaluation Metrics
- Practical Application of Deep Learning in Drug Discovery
- Convolutional Neural Networks
- Fundamental Concepts
- Optimization Algorithms
- Activation Functions
- Model Evaluation Metrics
- Practical Application of Deep Learning in Drug Discovery
- Recurrent Neural Networks
- Fundamental Concepts
- Optimization Algorithms
- Activation Functions
- Model Evaluation Metrics
- Practical Application of Deep Learning in Drug Discovery
- Reinforcement Learning
- Fundamental Concepts
- Optimization Algorithms
- Activation Functions
- Model Evaluation Metrics
- Practical Application of Deep Learning in Drug Discovery
Things you should know
Projects
During this specialization, participants engage in over 16 capstone projects intricately tied to developing models for virtual screening, toxicity predictions, molecular property enhancements, and other applications specific to small-molecule-based drug discovery.
Program duration
The specialization is structured for completion within 6-9 months. We advise dedicating a minimum of 6 hours per week to fully leverage the specialization content and ensure sufficient hands-on practice.
Program fee
The total program fee, inclusive of tax, is 1640 EUR for individuals holding a Ph.D. or above and for working professionals within the industry.
This fee can be paid conveniently either as a one-time payment or through easy 5-month installments of 360 EUR per month (10% extra).
Access to a mentor
Mentorship is a critical element of this specialization, setting it apart from individual courses.
Upon registration, you'll be paired with a dedicated mentor who will be your main point of contact.
Your mentor will guide you through the study program and offer support for any project and career-related inquiries you may have.
Maximum duration
The maximum duration for completion is 1 year. It's important to note that dedicated mentor support is available for up to 9 months, and we encourage you to make the most of this valuable assistance.
Certification
Yes! Upon completing the specialization and submitting all capstone projects, you will be awarded a certificate of completion for the Molecular AI Specialization.
Software and tools
This comprehensive curriculum covers a range of cheminformatics and ML/AI tools including RDKit, KNIME, scikit-learn, Keras, Tensorflow, and PyTorch.
Expect active utilization of these tools throughout the course, as they form the basis for your future work.
Additionally, the curriculum emphasizes building strong theoretical foundations, enabling you to confidently work with various tools encountered in practical applications.
Learner stories
“ I completed my Molecular AI Specialization program with Neovarsity a few months ago. It helped me to get a better understanding of AI/ML in the drug discovery field and also gave me the chance to learn new and very useful tools with the cheminformatics course! Thanks to the program I was able to find a job in the industry. I recommend taking a look and contacting them to discuss your objectives. I'm sure they can help you decide which is the best course for you.”
- Stefaní Gamboa, Ph.D. now at Iktos
“ I have been actively applying my newly acquired skills to the medicinal chemistry projects I am involved in [at Rutgers]. My coursework at Neovarsity has provided me with the expertise needed.”
- Anastasiia Tsymbal. now PhD Scholar at UPenn
Professionals and researchers from both industry and academia are actively taking this specialization. Feel free to explore some publicly-shared stories here.
Enrollment and career counseling
Enrollment in this program is strictly through counseling, ensuring that the program aligns with your career goals.
Neovarsity also provides active assistance in career counseling, helping you make informed decisions about your professional path.
If you're eager to join this career-building specialization, start a chat or email Neovarsity at support[a]neovarsity.org today to learn more about this specialization and how it can benefit your career.