Stars
A game theoretic approach to explain the output of any machine learning model.
Book about interpretable machine learning
[Legacy] Data & AI Notebook templates catalog organized by tools, following the IMO (input, model, output) framework for easy usage and discovery..
Making Protein folding accessible to all!
An interactive data visualization tool which brings matplotlib graphics to the browser using D3.
A small library for automatical adjustment of text position in matplotlib plots to minimize overlaps.
Therapeutics Commons (TDC): Multimodal Foundation for Therapeutic Science
Practical Cheminformatics Tutorials
Repository for scripts and notebooks from the book: Programming PyTorch for Deep Learning
Cloud-based molecular simulations for everyone
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
Install Conda and friends on Google Colab, easily
Explainer for black box models that predict molecule properties
Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.
repo for DynamicBind: Predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model
Jupyter/IPython notebooks about evolutionary computation.
Python for chemoinformatics
Scoring of shape and ESP similarity with RDKit
active learning for accelerated high-throughput virtual screening
Facilitates searching, screening, and organizing large chemical databases
The Annotated Encoder Decoder with Attention
Experimental Design via Bayesian Optimization
QSARtuna: QSAR model building with the optuna framework
The ATOM Modeling PipeLine (AMPL) is an open-source, modular, extensible software pipeline for building and sharing models to advance in silico drug discovery.
Code for "Multi-Objective De Novo Drug Design with Conditional Graph Generative Model" (https://arxiv.org/abs/1801.07299)
Probabilistic Random Forest: A machine learning algorithm for noisy datasets
Explainability techniques for Graph Networks, applied to a synthetic dataset and an organic chemistry task. Code for the workshop paper "Explainability Techniques for Graph Convolutional Networks" …