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ReFlex3D: Refined Flexible Alignment of Molecules Using Shape and Electrostatics.

Thomas C SchmidtDavid A CosgroveJonas Boström
Published in: Journal of chemical information and modeling (2018)
We present an algorithm, ReFlex3D, for the refinement of flexible molecular alignments based on their three-dimensional shape and electrostatic properties. The algorithm is designed to be used with fast conformer generators to refine an initial overlay between two molecules and thus to obtain improved overlaps as judged by an increase in calculated similarity values. ReFlex3D is open-source and built as a python package working in combination with the OEChem Toolkit. As such it can readily be implemented in existing workflows ranging from the selection of compounds from a virtual screening campaign to the construction of similarity based prediction models to estimate binding affinities. We evaluate ReFlex3D against the AstraZeneca Validation Test Set and illustrate its potential within a predictive model compared to an established method (Posit).
Keyphrases
  • machine learning
  • deep learning
  • molecular dynamics simulations
  • transcription factor
  • dna binding