Docking execution time.

<p>CPU time and elapsed time in hours of docking 3,000 clean ligands of 3 molecular weight sets against 12 diverse receptors by AutoDock Vina and idock. idock outperforms AutoDock Vina by at least 8.69 times and at most 37.51 times.</p

Scatter plot of the highest RF-Score of the 9 docked conformations output by idock against the experimental binding affinity on PDBbind v2012 core set ( = 201) in the redocking benchmark.

<p>The RF-Score was re-trained on PDBbind v2012 refined set ( = 2,897) for prospective prediction purpose. Values are in or unit.  = 0.815,  = 0.817,  = 0.75,  = 0.76.</p

Impact of number of rotatable bonds of the ligand on the success rates of idock and AutoDock Vina benchmarked on PDBbind v2012 core set ( = 201).

<p>Very often the of 2.0Å is regarded as the positive control for correct bound structure prediction. Out the 201 cases, there are 109 and 114 successful cases for idock and AutoDock Vina respectively. The average number of rotatable bonds of the ligand in successful cases are 7.52 and 7.30 respectively for idock and AutoDock Vina. The average number of rotatable bonds of the ligand in unsuccessful cases are 10.36 and 10.82 respectively for idock and AutoDock Vina. Docking a ligand with no greater than 10 rotatable bonds has a higher chance to succeed.</p

Scatter plot of the lowest idock score of the 9 docked conformations output by idock against the experimental binding affinity on PDBbind v2012 core set ( = 201) in the redocking benchmark.

<p>Values are in or unit.  = 0.502,  = 0.530,  = 1.31,  = 1.32.</p

Impact of number of metal ions in the binding site on the success rates of idock and AutoDock Vina benchmarked on PDBbind v2012 core set ( = 201).

<p>Out the 201 cases, there are 158, 31 and 12 cases in which there are 0, 1 and 2 metal ions respectively in the binding site. Very often the of 2.0Å is regarded as the positive control for correct bound structure prediction. For idock, the success rates are 0.58, 0.39 and 0.42 when there are 0, 1 and 2 metal ions respectively in the binding site. For AutoDock Vina, they are 0.60, 0.42 and 0.50 respectively. Docking a ligand with no metal ions in the binding site has a higher chance to succeed.</p

Scatter plot of the RF-Score of the first docked conformation against the experimental binding affinity on PDBbind v2012 core set ( = 201) in the redocking benchmark.

<p>The RF-Score was re-trained on PDBbind v2012 refined set ( = 2,897) for prospective prediction purpose. Values are in or unit.  = 0.855,  = 0.859,  = 0.73,  = 0.73.</p

Pairwise correlations of experimental binding affinity and predicted binding affinity by RF-Score, AutoDock Vina and idock on the PDBbind v2012 refined set ( = 2,897).

<p>Values are in or unit. The three scoring functions are all trained on the PDBbind v2007 refined set ( = 1,300).  = 0.765,  = 0.755,  = 1.26,  = .26 for RF-Score,  = 0.466,  = 0.464,  = 1.74,  = 1.74 for Vina, and  = 0.451,  = 0.453,  = 1.75,  = .75 for idock.</p

Comparison of 21 scoring functions on PDBbind v2007 core set ( = 195).

<p>Pearson's correlation coefficient , Spearman's correlation coefficient and standard deviation of the difference between predicted and experimental binding affinity on PDBbind v2007 core set ( = 195). The scoring functions are sorted in the descending order of . RF-Score, AutoDock Vina and idock rank 1st, 7th and 8th respectively in terms of Pearson's correlation coefficient . RF-Score, ID-Score, SVR-Score and X-Score are the only scoring functions whose training set do not overlap with the PDBbind v2007 core set. The statistics for AutoDock Vina and idock are reported in this study and the statistics for the other 19 scoring functions are collected from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085678#pone.0085678-Ballester1" target="_blank">[31]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085678#pone.0085678-Li2" target="_blank">[45]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085678#pone.0085678-Ballester4" target="_blank">[46]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085678#pone.0085678-Cheng1" target="_blank">[48]</a>.</p

IC₅₀ prediction.

<p>Predictions are achieved with 8-fold cross-validations. Performance values are exclusively calculated on the test sets. (A) Correlation between predicted to experimental observed log(IC₅₀) values (Pearson correlation R_p = 0.85 ; coefficient of determination R² = 0.72, root mean square error RMSE  = 0.83). Although there is an enrichment of resistant cell lines, which tend to have higher log(IC₅₀) values than sensitive cell lines, the lower log(IC₅₀) values are still decently predicted. (B) Expected improvement of the IC₅₀ prediction by filling experimentally gaps in the cell-to-drug matrix. The vertical grey line corresponds to the published data set (filled to ∼58%, due to logistic reasons), which corresponds to the results in panel (A). However, similar accuracies (R_p of 0.84 instead of 0.85, R² of 0.70 instead of 0.72) can be achieved using exclusively 20% of the whole matrix.</p

IC₅₀ prediction workflow.

<p>Our method is based on two different input streams: (1) cell line features of 77 oncogenes and their mutation state, (2) drug features that are generated with PaDEL software <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061318#pone.0061318-Yap1" target="_blank">[19]</a> from the simplified molecular-input line entry system (SMILES), see method section for details. The continuous IC₅₀ value is predicted with state-of-the-art machine learning algorithms (neural networks and random forests).</p