Teaching & Tutorials

Courses

Shidang Xu teaches the following courses:

• Neural Networks and Deep Learning
• Data Mining and Big Data

Teaching and Mentoring

I teach courses in neural networks and deep learning, and in data mining and big data. My mentoring aims to help students become independent researchers who can formulate scientific questions, design methods, and develop meaningful evaluation criteria.

I encourage undergraduate students to explore scientific questions that spark their curiosity and to take an active role in shaping their research. I guide them in turning initial ideas into clear questions, developing suitable methods, and evaluating their findings with care.

PI profile · Our Lab Charter

Research tutorials

Molecular data and active discovery

Start here if you want to understand how a molecular design problem becomes a learning problem. Some familiarity with molecular structures, Python and tabular data will help.

1. Understand the records. Open the photosensitizer resource guide. Compare the DA and DAD tables, identify the prediction targets and check their units and computational origin.
2. Trace the representation. Inspect the donor, acceptor and bridge table and the molecular-space construction code. Ask which design choices define the space that can be searched.
3. Read a prediction example. Follow the Model Predictions Notebook, using the original repository’s model and environment instructions if you run it.
4. Examine the next-query decision. Read the active-learning implementation alongside the paper. Identify how new calculations enter the learning process.

Question to carry through the example: how would changing the available molecular building blocks change what the discovery system can find, even if the learning algorithm stayed the same?

Protein–ligand representations and prediction

Start here if you want to understand how structural information becomes input to an affinity model. Familiarity with Python, molecular structures and neural-network inference will help.

1. Inspect the inputs. Read the E-CloudBind resource guide and compare its protein, ligand, point-cloud and graph files.
2. Trace the feature construction. Follow the preprocessing workflow. Distinguish ligand electron-density generation from protein-pocket point-cloud construction.
3. Read the inference example. Inspect the demo script. Identify where the model produces predictions and where known labels enter the evaluation.
4. Separate a prediction from an evaluation. Before adapting the code to a new complex, decide which outputs you need and which checks can be performed without knowing its measured affinity.

Question to carry through the example: which features depend on the supplied three-dimensional geometry, and what would you need to check when that geometry is uncertain?

These routes use the authors’ existing research materials. Consult each original repository for its current dependencies, examples and usage instructions.