PubChem Transfer Learning for Tiny Bioactivity Sets

ISEF Category: Biochemistry

Subcategory: Other  ·  Difficulty: Advanced  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

A small data set can beat a bigger one if you teach the model chemistry first. That is the core idea behind transfer learning. You start with a model that has already seen millions of molecules, then ask it to solve a tiny bioactivity problem. Your job is to see whether that head start really beats a classic QSAR model.

What Is It?

This project asks whether a chemistry-language model can learn from PubChem first, then do better on a small bioactivity set than a traditional QSAR model. QSAR means quantitative structure-activity relationship. In plain language, it tries to predict what a molecule will do from its structure. You can think of it like comparing two students, one who studied general chemistry before the quiz and one who only saw the quiz sheet.

The pretrained model learns broad patterns from many molecules, then fine-tunes on a narrow natural-product data set. That matters when the data set is small, because a model that starts from scratch may not see enough examples to learn much at all. Your comparison can test whether the pretrained model keeps its edge when the data gets tiny, noisy, or split by scaffold, which means by chemical family.

Why This Is a Good Topic

This is a strong science fair topic because you can test a clear claim with public data and measurable scores. It connects to drug discovery, natural products, and the real problem of making predictions when examples are scarce. You can learn data cleaning, model comparison, cross-validation, and error analysis without needing a wet lab.

Research Questions

• How does PubChem pretraining change model accuracy on a small natural-product bioactivity set?
• What is the effect of sample size on transfer learning versus classical QSAR performance?
• Does a pretrained chemistry model beat a fingerprint-based QSAR baseline under scaffold splits?
• To what extent does class imbalance change the gap between transfer learning and QSAR?
• Which molecular representation, SMILES embeddings, fingerprints, or physicochemical descriptors, gives the best result on tiny data?
• How does adding a second validation split affect confidence in the model comparison?

Basic Materials

• Laptop or desktop computer with at least 16 GB RAM.
• Python 3 environment with Jupyter Notebook.
• RDKit, scikit-learn, pandas, and NumPy.
• Public bioactivity data from PubChem BioAssay or ChEMBL.
• Spreadsheet or lab notebook for tracking splits, metrics, and model settings.

Advanced Materials

• GPU-enabled workstation or cloud GPU access.
• PyTorch or TensorFlow.
• Hugging Face Transformers or a similar chemistry model library.
• RDKit for fingerprints, descriptors, and scaffold splitting.
• DeepChem for molecule learning workflows and baseline models.
• Curated natural-product bioactivity set with clear assay labels.

Software & Tools

• Python: Runs data cleaning, model training, and evaluation.
• Jupyter Notebook: Keeps code, plots, and notes in one place.
• RDKit: Builds fingerprints, descriptors, and scaffold splits.
• scikit-learn: Trains QSAR baselines and scores model performance.
• Hugging Face Transformers: Loads and fine-tunes a pretrained chemistry model.

Experiment Steps

1. Define one small bioactivity task, one label type, and one score that will judge success.
2. Build a classical QSAR baseline with the same split and the same input representation.
3. Pick a pretrained chemistry model and decide how you will map each molecule into its input format.
4. Plan a fair comparison using scaffold splits or repeated cross-validation, not just one lucky split.
5. Design an error analysis that checks which chemical families each model gets right or wrong.

Common Pitfalls

• Random train-test splits can put close chemical neighbors in both sets, which makes both models look better than they are.
• Comparing models with different splits or different metrics turns the result into an unfair race.
• Using too few active and inactive examples can make the fine-tuned model memorize labels instead of learning chemistry.
• Mixing assay endpoints can blur the target signal and hide any gain from transfer learning.
• Ignoring class imbalance can make accuracy look fine even when the model misses most actives.

What Makes This Competitive

A competitive version of this project goes beyond a simple score comparison. You need strong controls, like scaffold splits, repeated runs, and a clear baseline. You can push it further with an external test set, uncertainty intervals, or an ablation study that checks which input style helps most. The best entries explain not just which model wins, but why it wins on tiny chemical data.

Project Variations

• Compare transfer learning and QSAR on antibacterial natural products instead of a mixed bioactivity set.
• Test whether scaffold splitting versus random splitting changes the size of the transfer learning gain.
• Replace classification with regression by predicting pIC50 values for a curated assay set.

Learn More

• PubChem: Search compound records, assay pages, and bioactivity tables on the NIH PubChem site.
• ChEMBL: Find curated bioactivity data sets and assay records for open drug-discovery projects.
• RDKit documentation: Learn fingerprinting, descriptors, and scaffold splitting from the open-source cheminformatics toolkit docs.
• scikit-learn user guide: Read about cross-validation, baselines, metrics, and model selection.
• PubMed: Search review articles on transfer learning, QSAR, and machine learning for drug discovery.