NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function

By Jacob D. Durrant, James Andrew McCammon. 2011

336 citations recorded by the source corpus.

NNScore is a neural-network-based scoring function designed to aid the computational identification of small-molecule ligands. While the test cases included in the original NNScore article demonstrated the utility of the program, the application examples were limited. The purpose of the current work is to further confirm that neural-network scoring functions are effective, even when compared to the scoring functions of state-of-the-art docking programs, such as AutoDock, the most commonly cited program, and AutoDock Vina, thought to be two orders of magnitude faster. Aside from providing additional validation of the original NNScore function, we here present a second neural-network scoring function, NNScore 2.0. NNScore 2.0 considers many more binding characteristics when predicting affinity than does the original NNScore. The network output of NNScore 2.0 also differs from that of NNScore 1.0; rather than a binary classification of ligand potency, NNScore 2.0 provides a single estimate of the pKd. To facilitate use, NNScore 2.0 has been implemented as an open-source python script. A copy can be obtained from http://www.nbcr.net/software/nnscore/.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

Latest developments in molecular docking: 2010–2011 in review (2013, methodology): “[96,97] Although NNScore is a knowledge-based function, as it uses structural data, the use of NNs in this context is innovative.”
Structure-Based Virtual Screening: From Classical to Artificial Intelligence (2020, methodology): “Some scoring functions of this type are NNScore 2.0 (Durrant and McCammon, 2011), RF-Score-VS (Wójcikowski et al., 2017), SFCscoreRF (Zilian and Sotriﬀer, 2013), SVR-KB (Li et al., 2011), SVR-EP (Li et al., 2011), ID-Score (Li et al., 2013) and CScore (Ouyang et al., 2011).”
Boosting Docking-Based Virtual Screening with Deep Learning (2016, methodology): “7,11,24 There is a problem in the partial charges of the original version of DUD that makes it trivial to discriminate between ligands and decoys.”
Development and evaluation of a deep learning model for protein–ligand binding affinity prediction (2017, methodology): “Prime examples of ML scoring functions are RF-Score (Ballester and Mitchell, 2010), which uses random forest, and NNscore (Durrant and McCammon, 2010, 2011), which uses an ensemble of shallow neural networks.”