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
Quantum Nature
A graph neural network based QM descriptor predictor
A suite of computational materials science tools.
Neural Network based model relevant for drug discovery
ChEMBL Database used to create Lipinski Descriptors (ADME Pharmokinetic Profile) to use in a Random Forest Regression Model
Data and regression models for QSAR
Scripts for assisting in modeling quantitative structure activity relationships from 2D chemical data
QSAR Tutorial - Obtaining data from ChEMBL and playing around with RDKit
tools for building qsar models
Quantum Machine Learning, Quantum Approximation Optimization Algorithms, VQE, Quantum Chemistry, PennyLane and Qiskit
Quantum deep field for molecule
Arbitrary-order derivatives of popular electronic structure methods, such as Hartree-Fock and coupled cluster theory.
Quantum Mechanical Bespoke Force Field Derivation Toolkit
Accurately speed up AutoDock Vina
Pytorch implementation of R-BERT: "Enriching Pre-trained Language Model with Entity Information for Relation Classification"
Preforms De novo protein design using machine learning and PyRosetta to generate a novel protein structure
Retrosynthetic Accessibility (RA) score learned from computer aided synthesis planning
Molecular informatics toolkit with a comprehensive integration of bioinformatics and cheminformatics tools for drug discovery.
RDKit integration to SQLAlchemy
Simple RDKit molecule editor GUI using PySide
The official sources for the RDKit library
A collection of cheminformatics scripts that use rdkit
rdkit scripts making life easier
Utilities for working with the RDKit
Tutorial on the usage of Rdkit, Pandas, sklearn, machine learning, descriptor calculation, etc.. in the context of bioactivity predictive modeling