2017
DOI: 10.1038/srep46710
|View full text |Cite
|
Sign up to set email alerts
|

Performance of machine-learning scoring functions in structure-based virtual screening

Abstract: Classical scoring functions have reached a plateau in their performance in virtual screening and binding affinity prediction. Recently, machine-learning scoring functions trained on protein-ligand complexes have shown great promise in small tailored studies. They have also raised controversy, specifically concerning model overfitting and applicability to novel targets. Here we provide a new ready-to-use scoring function (RF-Score-VS) trained on 15 426 active and 893 897 inactive molecules docked to a set of 10… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

4
308
0
5

Year Published

2017
2017
2024
2024

Publication Types

Select...
6
1
1

Relationship

1
7

Authors

Journals

citations
Cited by 277 publications
(317 citation statements)
references
References 50 publications
(84 reference statements)
4
308
0
5
Order By: Relevance
“…Second, we do not tune the hyperparameters of the ML algorithm to training data in the case of ML-based SFs (we used RF with the default values of the scikit-learn implementation except for 500 as the number of trees in the forest and 100 as the number of features to consider when looking for the best split at each node). Third, as in previous studies (Wójcikowski et al, 2017;Yasuo and Sekijima, 2019;Chen et al, 2019), ML-based SFs are trained on data from the DUD-E benchmark and tested on a second benchmark: DEKOIS2.0 (Bauer et al, 2013). In this way, the exploitation of DUD-E bias by the ML algorithm is avoided, as DEKOIS2.0 actives and decoys were selected in a different manner than those in DUD-E.…”
Section: Identifying Targets In Common Between Dud-e and Dekois20 Bementioning
confidence: 99%
See 2 more Smart Citations
“…Second, we do not tune the hyperparameters of the ML algorithm to training data in the case of ML-based SFs (we used RF with the default values of the scikit-learn implementation except for 500 as the number of trees in the forest and 100 as the number of features to consider when looking for the best split at each node). Third, as in previous studies (Wójcikowski et al, 2017;Yasuo and Sekijima, 2019;Chen et al, 2019), ML-based SFs are trained on data from the DUD-E benchmark and tested on a second benchmark: DEKOIS2.0 (Bauer et al, 2013). In this way, the exploitation of DUD-E bias by the ML algorithm is avoided, as DEKOIS2.0 actives and decoys were selected in a different manner than those in DUD-E.…”
Section: Identifying Targets In Common Between Dud-e and Dekois20 Bementioning
confidence: 99%
“…When a 3D structure of the protein target is available and the binding site is known, this problem is more specifically called Structure-Based Virtual Screening (SBVS). An active is here a small organic molecule modulating the 3 was trained on complexes with a range of targets (Wójcikowski et al, 2017). Such generic SF can hence be applied to molecules complexed with any of these targets without retraining.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Instead they are trained on large datasets to model the relationships between affinity and various descriptors. According to the latest literature data, the machine learning scoring functions can estimate the binding energy most successfully.…”
Section: Introductionmentioning
confidence: 99%
“…Docking simulations are biologically well-accepted but time consuming and require 3D structures of targets. Furthermore, some researchers report that standard molecular docking scoring functions may be replaced by machine learning based scoring functions with improved prediction results [17,18].…”
Section: Introductionmentioning
confidence: 99%