Souvik-SNH's repositories
practical_cheminformatics_tutorials
Practical Cheminformatics Tutorials
DiffDock
Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
papers_for_protein_design_using_DL
List of papers about Proteins Design using Deep Learning
generative-ai-for-beginners
18 Lessons, Get Started Building with Generative AI đź”— https://microsoft.github.io/generative-ai-for-beginners/
Uni-Dock
Uni-Dock: a GPU-accelerated molecular docking program
awesome-molecular-generation
Awesome papers related to generative molecular modeling and design.
WHAM
An efficient, multithreaded weighted histogram analysis (WHAM) implementation for the post-processing of umbrella MD simulations.
chemprop
Message Passing Neural Networks for Molecule Property Prediction
rdkit-scripts
rdkit scripts making life easier
dgl-lifesci
Python package for graph neural networks in chemistry and biology
Jupyter_Dock
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.
DeepPurpose
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
DeepDelta
DeepDelta is a pairwise deep learning approach that processes two molecules simultaneously and learns to predict property differences between two molecules.
Predicting-Adverse-Drug-Reactions-with-Machine-Learning
The objective of this work is to develop machine learning (ML) methods that can accurately predict adverse drug reactions (ADRs) using the databases SIDER and OFFSIDES.
graph-neural-network-course
Free hands-on course about Graph Neural Networks using PyTorch Geometric.
mlcolvar
A unified framework for machine learning collective variables for enhanced sampling simulations
qMSM_tutorial
A Tutorial for quasi Markov State Model(qMSM) developed by Huang Group, Dept of Chemistry at UW-Madison
EMUS
Implementation of the EMUS algorithm for recombining multiple biased data sources in python
rdkit-tutorials
Tutorials to learn how to work with the RDKit
MultiGML
MultiGML
RoseTTAFold
This package contains deep learning models and related scripts for RoseTTAFold
deep-learning-slow-modes
Supporting data for the manuscript "Deep learning the slow modes for rare events sampling"
idse-HE
idse-HE: hybrid embedding graph neural network for drug side effects prediction