D3R Grand Challenge 3: blind prediction of protein–ligand poses and affinity rankings

Gaieb, Zied; parks, conor; Chiu, Michael; Yang, Huanwang; Shao, Chenghua; Walters, W. Patrick; Lambert, Millard H.; Nevins, Neysa; Bembenek, Scott D.; Ameriks, Michael K.; Mirzadegan, Taraneh; Burley, S.K.; Amaro, Rommie E.; Gilson, Michael K.

doi:10.1007/s10822-018-0180-4

Cited by 119 publications

(128 citation statements)

References 53 publications

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“…Unlike BACE1, the CatS free energy data set focuses on a single chemical scaffold. A detailed description of the binding assay conditions was published in our previous GC3 publication and a Janssen publication [6,30,45,51].…”

Section: Datasets and Subchallengesmentioning

confidence: 99%

“…GC4 constitutes the fourth D3R Grand Challenge to date. It followed a similar format to previous challenges [6,30,45], including pose prediction, affinity ranking, and free energy prediction components. GC4 followed a two-stage format.…”

Section: Posing the Challengementioning

confidence: 99%

“…D3R, built upon the prior work of Community Structure Activity Resource (CSAR) [41][42][43][44], provides opportunities for blinded prospective methods benchmarking and comparison on hitherto privately-held data sets kindly provided by (primarily) industrial partners. To date, the D3R has conducted four major challenges [6,30,45]. The results of Grand Challenge 4 (GC4) reported herein.…”

Section: Introductionmentioning

confidence: 99%

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D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies

parks

Gaieb

Chiu

et al. 2020

J Comput Aided Mol Des

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101

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The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.

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Section: Datasets and Subchallengesmentioning

confidence: 99%

Section: Posing the Challengementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies

parks

Gaieb

Chiu

et al. 2020

J Comput Aided Mol Des

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101

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“…Our first objective was to develop a docking workflow for the 154 ligands without crystal structures. It was shown in D3R GC3 that docking to the appropriate receptor structure is important for success [3]. In GC4 Stage 2, D3R provided SMILES codes of the 154 macrocyclic ligands for the affinity prediction test; 20 co-crystal structures were released earlier in D3R GC4 Stage 1B.…”

Section: Semi-automated Ligand Pose Generation and Dockingmentioning

confidence: 99%

Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4

Wang

2019

Preprint

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Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) offered a unique opportunity for designing and testing novel methodology for accurate docking and affinity prediction of ligands in an open and blinded manner. We participated in the beta-secretase 1 (BACE) Subchallenge which is comprised of cross-docking and redocking of 20 macrocyclic ligands to BACE and predicting binding affinity for 154 macrocyclic ligands. For this challenge, we developed machine learning models trained specifically on BACE. We developed a deep neural network (DNN) model that used a combination of both structure and ligand-based features that outperformed simpler machine learning models. According to the results released by D3R, we achieved a Spearman's rank correlation coefficient of 0.43 (7) for predicting the affinity of 154 ligands. We describe the formulation of our machine learning strategy in detail. We compared the performance of DNN with linear regression, random forest, and support vector machines using ligand-based, structurebased, and combining both ligand and structure-based features. We compared different structures for our DNN and found that performance was highly dependent on fine optimization of the regularization hyperparameter, alpha. We also developed a novel metric of ligand threedimensional similarity inspired by crystallographic difference density maps to match ligands without crystal structures to similar ligands with known crystal structures. This report demonstrates the detailed parameterization and careful data training and implementation necessary to obtain strong performance with more complex machine learning methods. Our DNN approach tied for fourth in predicting BACE-ligand binding affinities.

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“…The Drug Design Data Resource (D3R) 2019 Grand Challenge is the fourth iteration (GC4) of the major docking competition organized by the D3R consortium [1][2][3]. The competition has two main goals: i) assessing the ability of docking algorithms to accurately predict the binding poses of a protein against a diverse set of small molecules, and ii) evaluating of the performance of binding affinity predictors.…”

Section: Introductionmentioning

confidence: 99%

Coupling enhanced sampling of the apo-receptor with template-based ligand conformers selection: performance in pose prediction in the D3R Grand Challenge 4

Basciu¹,

Koukos

Malloci³

et al. 2019

J Comput Aided Mol Des

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We report the performance of our newly introduced Ensemble Docking with Enhanced sampling of pocket Shape (EDES) protocol coupled to a template-based algorithm to generate near-native ligand conformations in the 2019 iteration of the Grand Challenge organized by the D3R consortium. Using either AutoDock4.2 or HADDOCK2.2 docking programs (each software in two variants of the protocol) our method generated native-like poses among the top 5 submitted for evaluation for most of the 20 targets with similar performances. The protein selected for GC4 was the human beta-site amyloid precursor protein cleaving enzyme 1 (BACE-1), a transmembrane aspartic-acid protease. We identified at least one pose whose heavy-atoms RMSD was less than 2.5 Å from the native conformation for 16 (80%) and 17 (85%) of the twenty targets using AutoDock and HADDOCK, respectively. Dissecting the possible sources of errors revealed that: i) our EDES protocol (with minor modifications) was able to sample sub-ångstrom conformations for all 20 protein targets, reproducing the correct conformation of the binding site within ~1 Å RMSD; ii) as already shown by some of us in GC3, even in the presence of near-native protein structures, a proper selection of ligand conformers is crucial for the success of ensemble-docking calculations. Importantly, our approach performed best among the protocols exploiting only structural information of the apo protein to generate conformations of the receptor for ensemble-docking calculations.

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D3R Grand Challenge 3: blind prediction of protein–ligand poses and affinity rankings

Cited by 119 publications

References 53 publications

D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies

D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies

Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4

Coupling enhanced sampling of the apo-receptor with template-based ligand conformers selection: performance in pose prediction in the D3R Grand Challenge 4

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