Update README.md to include instructions on how to create the Conda environment

README.md

@@ -2,13 +2,10 @@
 
 Deep learning-based molecular generation has extensive applications in many fields, particularly drug discovery. However, majority of current deep generative models (DGMs) are ligand-based and do not consider chemical knowledge in molecular generation process, often resulting in a relatively low success rate. We herein propose a structure-based molecular generative framework with chemical knowledge explicitly considered (named PocketFlow), which generates novel ligand molecules inside protein binding pockets. In various computational evaluations, PocketFlow showed a state-of-the-art performance with generated molecules being 100% chemically valid and highly drug-like. Ablation experiments prove a critical role of chemical knowledge in ensuring the validity and drug-likeness of the generated molecules. We applied PocketFlow to two new target proteins that are related to epigenetic regulation, HAT1 and YTHDC1, and successfully obtained wet-lab validated bioactive compounds. The binding modes of the active compounds with target proteins are close to those predicted by molecular docking, and further confirmed by the X-ray crystal structure. All the results suggest that PocketFlow is a useful deep generative model, capable of generating innovative bioactive molecules from scratch given a protein binding pocket.
 
-Requirements:
-* Python 3.8
-* pytorch 1.12
-* Pytorch_Geometric 2.1.0
-* RDKit
-* Openbabel
-* PyMol
+### Create the Conda environment
+~~~
+conda env create -f environment.yml
+~~~
 
 ### Molecular generation
 The molecule can be generated by running the following command, where the pocket pdb file and the model parameter file are required, and the rest of the parameters are optional