a<p>MP represents thirteen molecular properties. The best tree depth is 8 for RP models (MP, MP+EPFP_4 and MP+LPFP_6), 10 for RP models (MP+ECFP_6 and MP+LPFP_4), 11 for RP models (MP+FCFP_4, MP+FCFP_6, MP+LCFP_4, MP+LCFP_6 and MP+FPFP_6) and 12 for RP model (MP+FPFP_4). The detailed results can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095221#pone.0095221.s004" target="_blank">Table S1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095221#pone.0095221.s005" target="_blank">Table S2</a> in Supporting Information.</p
Supplementary Table 1: Hyperparameters values of five machine learning models trained to predict ...
<p>The PCM models (A) perform better as they have a lower prediction error for each drug class (0.53...
<p>Each boxplot summarizes Kappa for classifying the metabolic inheritance patterns (mIPs) from 41 e...
<p>The 243 RP models were constructed based on molecular properties (thirteen molecular descriptors)...
Protein–protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypoth...
<p>The performance of different models depicted graphically via receiver operating characteristic (R...
The performance of a model is dependent on the quality and information content of the data used to b...
Molecularly imprinted polymers (MIPs) are synthetic receptors engineered towards the selective bindi...
<p>(<b>A</b>) ROC curves for predictions using three functional constraints and a crystallographic e...
We investigate the impact of choosing regressors and molecular representations for the construction ...
Program to calculate Protein Stability Index from the sequence. ML models were developed based on e...
Datasets and splits of the manuscript "Chemprop: Machine Learning Package for Chemical Property Pred...
This article describes an application of high-throughput fingerprints (HTSFP) built upon industrial ...
A multidimensional analysis of machine learning methods performance in the classification of bioacti...
Abstract-As physical and chemical properties of protein guide to determine quality of the protein st...
Supplementary Table 1: Hyperparameters values of five machine learning models trained to predict ...
<p>The PCM models (A) perform better as they have a lower prediction error for each drug class (0.53...
<p>Each boxplot summarizes Kappa for classifying the metabolic inheritance patterns (mIPs) from 41 e...
<p>The 243 RP models were constructed based on molecular properties (thirteen molecular descriptors)...
Protein–protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypoth...
<p>The performance of different models depicted graphically via receiver operating characteristic (R...
The performance of a model is dependent on the quality and information content of the data used to b...
Molecularly imprinted polymers (MIPs) are synthetic receptors engineered towards the selective bindi...
<p>(<b>A</b>) ROC curves for predictions using three functional constraints and a crystallographic e...
We investigate the impact of choosing regressors and molecular representations for the construction ...
Program to calculate Protein Stability Index from the sequence. ML models were developed based on e...
Datasets and splits of the manuscript "Chemprop: Machine Learning Package for Chemical Property Pred...
This article describes an application of high-throughput fingerprints (HTSFP) built upon industrial ...
A multidimensional analysis of machine learning methods performance in the classification of bioacti...
Abstract-As physical and chemical properties of protein guide to determine quality of the protein st...
Supplementary Table 1: Hyperparameters values of five machine learning models trained to predict ...
<p>The PCM models (A) perform better as they have a lower prediction error for each drug class (0.53...
<p>Each boxplot summarizes Kappa for classifying the metabolic inheritance patterns (mIPs) from 41 e...