2018
DOI: 10.1007/s10822-018-0143-9
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Alchemical Grid Dock (AlGDock) calculations in the D3R Grand Challenge 3

Abstract: We participated in Subchallenges 1 and 2 of the Drug Design Data Resource (D3R) Grand Challenge 3. To prepare our submissions, we performed molecular docking with UCSF DOCK 6 and binding potential of mean force (BPMF) calculations-free energy calculations between flexible ligands and rigid receptors-using our open-source software package Alchemical Grid Dock (AlGDock). For each system, submissions were based on the minimum BPMF calculated for a selected set of crystal structures. In Subchallenge 1, our workflo… Show more

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Cited by 5 publications
(2 citation statements)
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“…The two lat-115 est Grand Challenges are also remarkable for the first 116 demonstrations of the 3D convolutional neural network-117 based methods [15]. Other approaches included molec-118 ular dynamics-based sampling and thermodynamic av-119 eraging [16] and implicit ligand theory [17] for binding 120 free energy predictions.…”
Section: Docking Strategies In Previous Exercisesmentioning
confidence: 99%
“…The two lat-115 est Grand Challenges are also remarkable for the first 116 demonstrations of the 3D convolutional neural network-117 based methods [15]. Other approaches included molec-118 ular dynamics-based sampling and thermodynamic av-119 eraging [16] and implicit ligand theory [17] for binding 120 free energy predictions.…”
Section: Docking Strategies In Previous Exercisesmentioning
confidence: 99%
“…Results from our purely physics-based approach were competitive with methods using knowledge-based potentials, which were the best performers in the challenge. As discussed in Xie and Minh 8 , several issues led to weaker performance in other subchallenges, including the neglect of DMSO and SO 4 in the binding site of Cathepsin S and poor selection of receptor snapshots for free energy calculations with Janus Kinase 2…”
mentioning
confidence: 99%