Deep Learning Meets Chemistry

Code

sPhysNet-MT⁴: GitHub models_cal models_exp

sPhysNet and PhysNet²: GitHub

Extended Functional Groups²: GitHub

Frag20_prepare²: GitHub

DTNN_7ib¹: GitHub Tutorial

A3D-PNAConv³: GitHub

Datasets

Reference

1. Predicting Molecular Energy Using Force-Field Optimized Geometries and Atomic Vector Representations Learned from an Improved Deep Tensor Neural Network

2. Dataset Construction to Explore Chemical Space with 3D Geometry and Deep Learning

3. Accurate prediction of aqueous solvation free energies using 3D atomic feature-based graph neural network with transfer learning

4. Multi-task Deep Ensemble Prediction of Molecular Energetics in Solution: From Quantum Mechanics to Experimental Properties