YoVDO

Data-Efficient Graph Grammar Learning for Molecular Generation

Offered By: Neurosymbolic Programming for Science via YouTube

Tags

Machine Learning Courses Chemistry Courses Computational Chemistry Courses Generative Models Courses Neurosymbolic Programming Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a data-efficient neurosymbolic generative model for molecular generation in this 1-hour 8-minute workshop. Learn about a learnable graph grammar that generates molecules from a sequence of production rules, addressing the challenges of limited class-specific chemical datasets and the need to generate only physically synthesizable molecules. Discover how this method can be learned from significantly smaller datasets compared to common benchmarks, and how additional chemical knowledge can be incorporated through further grammar optimization. Gain insights into overcoming the limitations of deep neural network-based approaches that typically require large training datasets.

Syllabus

Workshop 1: Data-Efficient Graph Grammar Learning for Molecular Generation


Taught by

Neurosymbolic Programming for Science

Related Courses

Introduction to Artificial Intelligence
Stanford University via Udacity
Natural Language Processing
Columbia University via Coursera
Probabilistic Graphical Models 1: Representation
Stanford University via Coursera
Computer Vision: The Fundamentals
University of California, Berkeley via Coursera
Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent