12345liu's Stars
tangyudi/Ai-Learn
人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域
dreadlesss/rdkit_summary
rdkit总结与实践
zhang-xuan1314/Molecular-graph-BERT
semi-supervised learning for molecular property prediction
yvquanli/GLAM
Code for "An adaptive graph learning method for automated molecular interactions and properties predictions".
rnepal2/Solubility-Prediction-with-Graph-Neural-Networks
GNN, GCN, Molecular Solubility, RDKit, Cheminformatics
pgniewko/solubility
My (small) research project in solubility of drug-like molecules
zhichunguo/GraSeq
GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction. In CIKM 2020.
swansonk14/chemprop-factor
Matrix factorization and deep learning for molecular property prediction
arhamshah/SolubilityPrediction
A web based application predicts water solubility of any given chemical compound known or unknown
iskyzh/ml-gcn
Course project for CS410. Drug Molecular Toxicity Prediction with GCN + Cloud ML Infra.
kirosc/dnn-toxicity-prediction
A convolutional neural network predicts the toxicity of a drug based on its molecular structure.
Abdulk084/HybridTox2D
In recent times, toxicological classification of chemical compounds is considered to be a grand challenge for pharma-ceutical and environment regulators. Advancement in machine learning techniques enabled efficient toxicity predic-tion pipelines. Random forests (RF), support vector machines (SVM) and deep neural networks (DNN) are often ap-plied to model the toxic effects of chemical compounds. However, complexity-accuracy tradeoff still needs to be ac-counted in order to improve the efficiency and commercial deployment of these methods. In this study, we implement a hybrid framework consists of a shallow neural network and a decision classifier for toxicity prediction of chemicals that interrupt nuclear receptor (NR) and stress response (SR) signaling pathways. A model based on proposed hybrid framework is trained on Tox21 data using 2D chemical descriptors that are less multifarious in nature and easy to calcu-late. Our method achieved the highest accuracy of 0.847 AUC (area under the curve) using a shallow neural network with only one hidden layer consisted of 10 neurons. Furthermore, our hybrid model enabled us to elucidate the inter-pretation of most important descriptors responsible for NR and SR toxicity.
YoucefBYu/Drug-descovery-project
With the rise of deep learning models and the successful result showing in different domains (such as Computer vision and Natural language processing)researchers and laboratories of chem-informatics try to apply these techniques in drug design and discovery. recently,the application of Deep Learning in this area of research has made a good progress but it is in the early stage and we can’t say that the results lead us to rational drug design,which mean designing new drugs without in Vivo and human trials. in this project project, we apply different machine learning models on drug design and discovery datasets with multiple tasks (each dateset has a task or goal to achieve from the analysis) after the evaluation and comparison of our results and the benchmarks we found that the huge problem is the small amount of data.
pstjohn/gnn-codecamp
Demonstrate training a GNN for molecular property prediction
aditya-jaishankar/solubility-prediction
Solubility prediction of organic molecules using convolutional neural networks on their molecular graphs
aretasg/SolPred
Machine learning model to predict aqueous solubility of organic compounds
austinguish/tox21-with-GCN
jerrylsu/Novel-Molecular-Toxicity-Prediction-Model
Novel molecular toxicity prediction model based on Softmax / Deep Neural Network / Stacked Autoencoder / Stacked Capsule Model.
sailfish009/MolHGCN
A Hypergraph Convolutional Neural Network for Molecular Properties Prediction using Functional Group
ArianGohari/tox21_toxicity_prediction
Predicting compound toxicity using DeepChem and the Tox21 dataset
noncomputable/molecular-property-prediction
Predicting molecular properties using transformer embeddings.
sanjaradylov/sparse-cheml
Molecular-property prediction with sparsity
Afra-S/Aqueous_solubility
CAVED123/MolRep111
lamawmouk/Tox21_FeatureSelection_SMOTEENN_RF
RohitHansdah/Molecular_Solubility_Prediction_App
treyvian/Molecular-Property-Prediction
You are asked to work with the BACE-1 dataset, providing quantitative (IC50) and qualitative (binary label) binding results for a set of inhibitors of human β-secretase 1(BACE-1).