# Machine Learning for Molecular Engineering (3.C01/3.C51, 7.C01/7.C51, 10.C01/10.C51, 20.C01/20.C51)

Materials and problem sets for the course Machine Learning for Molecular Engineering taught at MIT.

**Instructors (cumulative)**: Prof. Connor Coley, Prof. Rafael Gomez-Bombarelli, Prof. Ernest Fraenkel, Prof. Joey Davis, Prof. Sergey Ovchinnikov

**Warning**: These assignments are continually being revised and may change. Do not start working on an assignment until it has been released on Canvas.

<!---
## Problem Sets

### PS0

[**All  (3.C01/3.C51, 7.C01/7.C51, 10.C01/10.C51, 20.C01/20.C51)**](https://github.com/coleygroup/ML4MolEng/blob/main/psets/ps0/)

Ungraded problem set to practice using Google Colab and NumPy.


### PS1

Linear classification problem to get you started for the course. You will use logistic regression to diagnose cancer (data size: ~10^2), applying linear methods with L1 and L2 regularization and understand what effects they have on your regression results. 

[**Perovskites (3.C01/3.C51, 10.C01/10.C51)**](https://github.com/coleygroup/ML4MolEng/tree/main/psets/ps1-nonbio)

You will then apply a MLP regressor to predict properties of perovskites (data size: ~10^3) and compare differences between representations of perovskite crystal chemical compositions.

[**MHC (7.C01/7.C51, 20.C01/20.C51)**](https://github.com/coleygroup/ML4MolEng/tree/main/psets/ps1-bio)

You will then apply an MLP regressor to predict MHC binding to peptides (data size: ~10^3) and compare differences between representations of peptide amino acid sequences.


### PS2

For this problem set, you will build a sequence-based model to predict DNA binding sequences (data size: ~10^4) generated with ChIP-seq.

[**Bubbles (3.C01/3.C51, 10.C01/10.C51)**](https://github.com/coleygroup/ML4MolEng/tree/main/psets/ps2-nonbio)

Next, you will train a model to perform segmentation on images of bubbles (data size: ~10^2) arising from the surface of a catalyst, with the goal of using these insights to improve the design of catalysts.

[**Cells (7.C01/7.C51, 20.C01/20.C51)**](https://github.com/coleygroup/ML4MolEng/tree/main/psets/ps2-bio)

Next, you will train a model to perform segmentation on images of cells (data size: ~10^2), with the goal of using these insights to better understand cell morphology.


### PS3

In this problem set, you will train a 2D Graph Neural Network to predict solubility from molecular features, and investigate its insufficiency in predicting chirally-aware properties, namely $IC_{50}$.

[**Molecular Properties (3.C01/3.C51, 10.C01/10.C51)**](https://github.com/coleygroup/ML4MolEng/tree/main/psets/ps3-nonbio)

In this problem set, you will train a Graph Neural Network (GNN) to predict synthetic lethal (SL) interactions between gene pairs.

[**SLGNN (7.C01/7.C51, 20.C01/20.C51)**](https://github.com/coleygroup/ML4MolEng/tree/main/psets/ps3-SLGNN)





This problem set is an application of computer vision to molecular engineering. You will use a deep learning model to classify steel surface defects and perform image segmentation to identify cell nuclei.

[**PS4**](https://github.com/coleygroup/ML4MolEng/blob/main/psets/ps4/)

Data size: ~10^6

This problem set will be more meaty than the previous ones. You will implement your own Graph Neural Nets to predict molecular properties and train a Variational Auto-Encoder to generate new molecules from a learned hidden continuous representation.

[**PS5**](https://github.com/coleygroup/ML4MolEng/blob/main/psets/ps5/)

Data size: ~10^3

This problem set is an application of ML to predicting Solvation Free Energies and participating in a private kaggle dataset challenge.

[**PS6 (SFE)**](https://github.com/coleygroup/ML4MolEng/blob/main/psets/ps6-sfe/)

Data size: ~10^3

You will complete a short clustering exercise and participate in a ML competition to predict drug screen.

[**PS6 (Drug Screen)**](https://github.com/coleygroup/ML4MolEng/blob/master/psets/ps6-drug-screen/)

Data size: ~10^3

You will participate in a ML competition to predict solvation free energies of solute/solvent pairs.
--->