Umesh1608

AGA

Antibiotic Genetic Algorithm

Analysis-of-Large-Scale-Molecular-Datasets-with-Python

Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-source multi-fidelity quantum chemical calculations of ground-state and excited-state properties

asl-ml-immersion

This repos contains notebooks for the Advanced Solutions Lab: ML Immersion

CH485---Artificial-Intelligence-and-Chemistry

CH485 - Artificial Intelligence and Chemistry

chemical_vae

Code for 10.1021/acscentsci.7b00572, now running on Keras 2.0 and Tensorflow

chemical_vae_keras-molecules

Autoencoder network for learning a continuous representation of molecular structures.

CheXpert

ConceptWhitening

Constrained-Bayesian-Optimisation-for-Automatic-Chemical-Design

Code to accompany the paper "Constrained Bayesian Optimisation for Automatic Chemical Design" https://pubs.rsc.org/en/content/articlehtml/2019/sc/c9sc04026a

CVAE

github for "Molecular generative model based on conditional variational autoencoder for de novo molecular design"

Umesh1608/AGA

Antibiotic Genetic Algorithm

Umesh1608/Analysis-of-Large-Scale-Molecular-Datasets-with-Python

Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-source multi-fidelity quantum chemical calculations of ground-state and excited-state properties

Umesh1608/asl-ml-immersion

This repos contains notebooks for the Advanced Solutions Lab: ML Immersion

Umesh1608/chemical_vae

Code for 10.1021/acscentsci.7b00572, now running on Keras 2.0 and Tensorflow

Umesh1608/chemical_vae_keras-molecules

Autoencoder network for learning a continuous representation of molecular structures.

Umesh1608/CheXpert

Umesh1608/Constrained-Bayesian-Optimisation-for-Automatic-Chemical-Design

Code to accompany the paper "Constrained Bayesian Optimisation for Automatic Chemical Design" https://pubs.rsc.org/en/content/articlehtml/2019/sc/c9sc04026a

Umesh1608/CVAE

github for "Molecular generative model based on conditional variational autoencoder for de novo molecular design"

Umesh1608/darkchem

Umesh1608/Deep-Drug-Coder

A tensorflow.keras generative neural network for de novo drug design, first-authored in Nature Machine Intelligence while working at AstraZeneca.

Umesh1608/deepchem

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

Umesh1608/Deep-Learning-Spectroscopy

Code for deep learning models to predict molecular electronic properties.

Umesh1608/deep-protein-generation

Umesh1608/dgl

Python package built to ease deep learning on graph, on top of existing DL frameworks.

Umesh1608/Drug_RNN

Umesh1608/FireflyAlgorithm

Implementation of Firefly Algorithm in Python

Umesh1608/gcn

Implementation of Graph Convolutional Networks in TensorFlow

Umesh1608/GMD-MO-LSO

Umesh1608/GraphINVENT

Graph neural networks for molecular design.

Umesh1608/machine-learning-notes

Collection of useful machine learning codes and snippets (originally intended for my personal use)

Umesh1608/MGCVAE

Code for "MGCVAE: Multi-Objective Inverse Design via Molecular Graph Conditional Variational Autoencoder" (https://doi.org/10.1021/acs.jcim.2c00487)

Umesh1608/ML_UVvisModels

Umesh1608/Molecular_VAE_Pytorch

PyTorch implementation of the paper "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules"

Umesh1608/mul_RL

Umesh1608/prada_net

Code for PrAda-net, available at https://arxiv.org/abs/2012.11369

Umesh1608/pyswarms

A research toolkit for particle swarm optimization in Python

Umesh1608/pytorch-sgvae

A pytorch implementation of the model presented in the paper Molecular Property Prediction and Molecular Design Using a Supervised Grammar Variational Autoencoder

Umesh1608/SGVAE

Umesh1608/Smiles-GEN

GEN: Highly Efficient SMILES Explorer Using Autodidactic Generative Examination Networks

Umesh1608/weighted-retraining