From 48f9d6c00d1adcc555e3daa71ee201f3c6016277 Mon Sep 17 00:00:00 2001
From: Phil Wang <lucidrains@gmail.com>
Date: Sat, 27 Feb 2021 07:36:58 -0800
Subject: [PATCH] readme

---
 README.md | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/README.md b/README.md
index 283f6d0..74d026a 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 <img src="./egnn.png" width="600px"></img>
 
-## EGNN - Pytorch (wip)
+## EGNN - Pytorch
 
 Implementation of <a href="https://arxiv.org/abs/2102.09844v1">E(n)-Equivariant Graph Neural Networks</a>, in Pytorch. May be eventually used for Alphafold2 replication. This technique went for simple invariant features, and ended up beating out all previous methods (including SE3 Transformer and Lie Conv) in both accuracy and performance.
 
@@ -43,14 +43,6 @@ feats, coors = layer1(feats, coors, edges)
 feats, coors = layer2(feats, coors, edges) # (1, 16, 512), (1, 16, 3)
 ```
 
-## Todo
-
-- [ ] masking
-- [x] add integration with pytorch geometric
-- [x] add tests for se3 equivariance
-- [x] add an EGAT (attention flavored variant)
-
-
 ## Citations
 
 ```bibtex