+
Skip to content

forrestjgq/dockcpp

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

39 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Requirement

  • python >= 3.6
  • pytorch
  • cuda development environment

Build & Install

cd dockcpp
mkdir build
cd build
cmake ..
make install-python-package

Verify installation

import pydock

Use

step 1: create a cuda context with specified CUDA device id

device = 0
ctx = pydock.CudaContext(device)

step 2: prepare CPU data

  • vt (torch.Tensor or type acceptable for torch.tensor()): x for f(x), 1 dimension
  • init_coord (torch.Tensor): initial ligand position, dim: (N, 3)
  • torsions (list): M x 2 int list for torsion angle, dim 0 for start node index, dim 1 for end node index
  • masks (list of torch.Tensor(dtype=bool)): torsion masks, length should be M, sub length should be N
  • pocket_coords (torch.Tensor): pocket positions, K x 3 float tensors
  • pred_cross_dist (torch.Tensor): predicted distance from ligand to pociekts, N x K
  • pred_holo_dist (torch.Tensor): predicted ligand holo distance, N X N

step3: calculate loss and grads

    t = ctx.dock_grad(vt, init_coord, torsions, masks, pocket_coords, pred_cross_dist, pred_holo_dist)
    # t will be one dimention float tensor, size len(vt) + 1
    # the first float will be loss value, and rest will be grad for each value in vt

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Packages

No packages published

Contributors 2

  •  
  •  
点击 这是indexloc提供的php浏览器服务,不要输入任何密码和下载