Skip to content

Update README.md #1

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
zacharyfralish wants to merge 2 commits into
base: main
Choose a base branch
from
Open

Update README.md #1

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
f47c5f0
Update README.md
zacharyfralish Mar 24, 2023
e3bee8d
Merge branch 'main' into patch-1
zacharyfralish Mar 30, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 5 additions & 1 deletion README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,8 +1,10 @@
# Active Subsampling
**Improving Molecular Machine Learning Through Adaptive Subsampling with Active Learning**


![toc_graphic_white](https://user-images.githubusercontent.com/56095399/227727724-404e2bfb-fcd4-497d-bc77-3acff026ff2b.png)


## Overview
We use active machine learning as an autonomous and adaptive data subsampling strategy and show that active learning-based subsampling can lead to better molecular machine learning performance when compared to both training models on the complete training data and 19 state-of-the-art subsampling strategies. We find that active learning is robust to errors in the data, highlighting the utility of this approach for low-quality datasets. Taken together, we here describe a new, adaptive machine learning pre-processing approach and provide novel insights into the behavior and robustness of active machine learning for molecular sciences.

Expand All @@ -12,12 +14,13 @@ If you use this data or code, please kindly cite: Wen, Y., Li, Z., Xiang, Y., &

<br>


## Files
- **code.py** contains all code and functions to run and evaluate active learning subsampling
- **Example_workflow_for_AL_Subsampling.ipynb** contains an example notebook that runs BBBP but can be run out of the box on a local machine or on Google Colab to apply this technique to new datasets

<br>

<br>

## Dependencies
* [numpy](https://numpy.org/)
Expand All @@ -29,6 +32,7 @@ If you use this data or code, please kindly cite: Wen, Y., Li, Z., Xiang, Y., &

<br>


## Quickstart

Datasets can be loaded from DeepChem
Expand Down