souvik-snh Goto Github PK

adaptive-sampling-pmhc

chemprop

Message Passing Neural Networks for Molecule Property Prediction

deep-learning-slow-modes

Supporting data for the manuscript "Deep learning the slow modes for rare events sampling"

deepdelta

DeepDelta is a pairwise deep learning approach that processes two molecules simultaneously and learns to predict property differences between two molecules.

deepdta

deeplearning.ai

This is my assignment on Andrew Ng's special course "Deep Learning Specialization" This course consists of five courses: Neural Networks and Deep Learning Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Structuring Machine Learning Projects Convolutional Neural Networks Sequence Models

deeppocket_models

deeppurpose

A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)

deepside

dgl-lifesci

Python package for graph neural networks in chemistry and biology

drugadr

Code consolidation for work done in summer@Complex Systems Lab (IIIT-Delhi)

drugsideeffects

Project for the prediction of drug side-effect occurrences in the general population with Graph Neural Networks.

emus

Implementation of the EMUS algorithm for recombining multiple biased data sources in python

generative-ai-for-beginners

18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

gnn_dti

graph-neural-network-course

Free hands-on course about Graph Neural Networks using PyTorch Geometric.

graph_neural_networks

iadrgse

idse-he

idse-HE: hybrid embedding graph neural network for drug side effects prediction

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.

mgpred

mlcolvar

A unified framework for machine learning collective variables for enhanced sampling simulations

msm_latest

multigml

MultiGML

ppi_gnn

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.

pse-kge

Predicting polypharmacy side-effects using knowledge graph embedding models.

qmsm_tutorial

A Tutorial for quasi Markov State Model(qMSM) developed by Huang Group, Dept of Chemistry at UW-Madison

rdkit-scripts

rdkit scripts making life easier

rdkit-tutorials

Tutorials to learn how to work with the RDKit