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Protein-Ligand Scoring with Convolutional Neural Networks
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely onExpand
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  • 6
  • Open Access
Pharmit: interactive exploration of chemical space
Pharmit (http://pharmit.csb.pitt.edu) provides an online, interactive environment for the virtual screening of large compound databases using pharmacophores, molecular shape and energy minimization.Expand
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  • 5
  • Open Access
Open source molecular modeling.
The success of molecular modeling and computational chemistry efforts are, by definition, dependent on quality software applications. Open source software development provides many advantages toExpand
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  • 2
  • Open Access
A D3R prospective evaluation of machine learning for protein-ligand scoring
We assess the performance of several machine learning-based scoring methods at protein-ligand pose prediction, virtual screening, and binding affinity prediction. The methods and the manner in whichExpand
  • 10
  • 1
  • Open Access
Convolutional neural network scoring and minimization in the D3R 2017 community challenge
We assess the ability of our convolutional neural network (CNN)-based scoring functions to perform several common tasks in the domain of drug discovery. These include correctly identifying ligandExpand
  • 12
Characterization of Profilin Binding Kinetics using Ensemble Molecular Dynamics Simulations
We investigate the dynamics of profilin binding, including differences in binding for the loading and recruiting subregions of VASP, the effect of binding site mutations on peptide affinity forExpand
3D Convolutional Neural Networks and a CrossDocked Dataset for Structure-Based Drug Design.
One of the main challenges in drug discovery is predicting protein-ligand binding affinity. Recently, machine learning approaches have made substantial progress on this task. However, current methodsExpand
libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications
There are many ways to represent a molecule as input to a machine learning model and each is associated with loss and retention of certain kinds of information. In the interest of preservingExpand
  • 1
  • Open Access