CBSF: A New Empirical Scoring Function for Docking Parameterized by Weights of Neural Network

A new CBSF empirical scoring function for the estimation of binding energies between proteins and small molecules is proposed in this report. The final score is obtained as a sum of three energy terms calculated using descriptors based on a simple counting of the interacting protein-ligand atomic pairs. All the required weighting coefficients for this method were derived from a pretrained neural network. The proposed method demonstrates a high accuracy and reproduces binding energies of protein-ligand complexes from the CASF-2016 test set with a standard deviation of 2.063 kcal/mol (1.511 log units) and an average error of 1.682 kcal/mol (1.232 log units). Thus, CBSF has a significant potential for the development of rapid and accurate estimates of the protein-ligand interaction energies.

Abstract licence: CC BY

1. 17.

   Open Babel: An open chemical toolbox · 2011

2. 28.

   Performance of machine-learning scoring functions in structure-based virtual screening · 2017

3. 32.

   Development and evaluation of a deep learning model for protein–ligand binding affinity prediction · 2018

4. 36.

   Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges · 2018

36 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.