Name: Jianmin Wang
Type: User
Company: Yonsei University
Bio: Drug Design , Linux enthusiast , Medicinal_Chemistry_&_ Synthesis , Chemoinformatics , Data Science, Python and C/C++ programmer，Bioinformatics，Deep Learning，AI
Twitter: Jianmin4drugai
Location: **(China)
Blog: https://jianmin2drugai.github.io/
Jianmin Wang's Projects
Answers to 120 commonly asked data science interview questions.
simple rdkit script
Python source code for 3D/MI/QSAR models
Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation
WebGL accelerated JavaScript molecular graphics library
50 Layer Resnet to predict the regression values of Tetrahymena pyriformis IGC50 from 2d molecular images only
Tools to encode protein sequences using an Adversarial AutoEncoder
AARON 1.0, An Automated Reaction Optimizer for New catalysts
Data Files for "Deep diversification of an AAV capsid protein by machine learning"
:ab: ABC of chemoinformatics
Deep neural network based screening models for drug discovery
A deep learning model to predict anticancer peptides.
Chemoinformatics tool for ligand-based virtual screening
Prospective application of active machine learning to predict antimicrobial peptides
Framework for the reproducible classification of Alzheimer's disease using deep learning
Graph neural network message passing reframed as a Transformer with local attention
Python script that implements a random forest algorithm to predict several ADME-Tox classifications of bioactive molecules accompanied with a visualization technique called Uniform Manifold Approximation Projection (UMAP) [1]. This work is an amalgamation of previous great work by fellow researchers [2-5] with an extension towards our own research on predicting molecular ion fragmentation by a mass spectrometer (MS). In particular, we investigated the impact of different molecular encodings on the algorithm's prediction accuracy, sensitivity and specificity.
A platform for systematic ADME evaluation of drug molecules, thereby accelerating the drug discovery process.
Machine learning guided association of adverse drug reactions with in vitro off-target pharmacology
Simple autoencoder for smiles.
Implementation of AEMDA for inferring potential disease-miRNA associations.
Ready-to-go Jupyter notebook for plotting AlphaFold-generated MSAs, per-residue pLDDT, and PAE.
My Medicine Cabinet - Connecting you to the FDA
A free and collaborative space for Machine Learning applied to Biology
This is the code for "AI in Medicine " By Siraj Raval on Youtube
Artificial intelligence is used in drug development
Accepted Posters to present at the MIT's AI-powered Drug Discovery and Manufacturing Conference 2020.
Atoms In Molecules Neural Network Potential
A tool for retrosynthetic planning
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent