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Neeraj Kumar's Projects

adage icon adage

This is the repository for ADAGE (Analysis using Denoising Autoencoders for Gene Expression)

alphafold2 icon alphafold2

To eventually become an unofficial Pytorch implementation of Alphafold2, as details of the architecture get released

apollo1060 icon apollo1060

Data and models (with prediction scripts) used in publications related to Apollo1060 platform

bayesgan icon bayesgan

Tensorflow code for the Bayesian GAN (https://arxiv.org/abs/1705.09558) (NIPS 2017)

blues icon blues

Python package for molecular bond order estimation via natural population analysis.

boltzmann icon boltzmann

Simulation software for biochemical systems that use mass action kinetics based on maximum entropy production.

caffe icon caffe

Caffe: a fast open framework for deep learning.

cheminfor icon cheminfor

Generate Classfication RF,NB and SVM Models from SMILES data

chemputersoftware icon chemputersoftware

Software for running the Chemputer in association with the supporting publication in Science

cobrame icon cobrame

A COBRApy extension for genome-scale models of metabolism and expression (ME-models)

computation-thru-dynamics icon computation-thru-dynamics

Understanding computation in artificial and biological recurrent networks through the lens of dynamical systems.

confold2 icon confold2

Improved ab initio protein structure reconstruction

covid19ddc icon covid19ddc

Repository for COVID19 drug discovery consortium

d-gex icon d-gex

Deep learning for gene expression inference

darwin icon darwin

An open-source project dedicated to build foundational large language model for natural science, mainly in physics, chemistry and material science.

data.gov icon data.gov

Data.gov source code and issue tracker nkkchem

deep-review icon deep-review

A collaboratively written review paper on deep learning, genomics, and precision medicine

deepchem icon deepchem

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

deepfri icon deepfri

Deep functional residue identification

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