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laidd-molecular-generation

laidd2022-math-for-ml

laidd_gnn

lecture

ligdream

Novel molecules from a reference shape!

m1-machine-learning-test

Code for testing various M1 Chip benchmarks with TensorFlow.

mol_reliable_gnn

Source codes for 'A baseline for reliable molecular prediction models via Bayesian learning'

mold_pipeline

moleculenet

Moleculenet.ai Datasets And Splits

moses

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

netpea

Re-implementation of Network-Based Pathway Enrichment Analysis

nlp-tutorial

Natural Language Processing Tutorial for Deep Learning Researchers

organic

Code repo for optimizing distributions of molecules.

pathdsp

Explainable Drug Sensitivity Prediction through Cancer Pathway Enrichment Scores

pdb-tools

A dependency-free cross-platform swiss army knife for PDB files.

pgmg

pybiomed

machine learning, molecular descriptor

pydata-book

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

pydti

pymol-open-source

Open-source foundation of the user-sponsored PyMOL molecular visualization system.

pymol-script-repo

Collected scripts for Pymol

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

q3

Mathematica application to help study quantum information processing, quantum many-body systems, and quantum spin systems.

qiskit-hackathon-korea-22

qiskit-optimization

Quantum Optimization

quantumworkbook

A compilation of the Mathematica(R) Notebook files that have been used to generate the demonstrations in "A Quantum Computation Workbook".

ray

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

rdkit

The official sources for the RDKit library

rdkit-tutorials

Tutorials to learn how to work with the RDKit

rec-system-for-drug-response

Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines