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

Maciej Wójcikowski, Pedro J. Ballester, Pawel Siedlecki
2017 Scientific Reports  
We use the full DUD-E data sets along with three docking tools, five classical and three machine-learning scoring functions for model building and performance assessment.  ...  Recently, machine-learning scoring functions trained on protein-ligand complexes have shown great promise in small tailored studies.  ...  Acknowledgements This work was supported by the Polish Ministry of Science and Higher Education POIG.02.03.00-00-003/09-00 and POIG.02.02.00-14-024/08-00.  ... 
doi:10.1038/srep46710 pmid:28440302 pmcid:PMC5404222 fatcat:xzylkmmdybdydgkjcgol5xolcu

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Isabella A. Guedes, Felipe S. S. Pereira, Laurent E. Dardenne
2018 Frontiers in Pharmacology  
Empirical scoring functions are widely used for pose and affinity prediction.  ...  Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.  ...  Indeed, the preparation of protein-ligand complexes has a direct influence on training and evaluation of scoring functions, mainly for scoring functions based on force-field descriptors.  ... 
doi:10.3389/fphar.2018.01089 pmid:30319422 pmcid:PMC6165880 fatcat:johy46r7pzfclbpigg6kqpmqza

Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function

Li Zhang, Hai-Xin Ai, Shi-Meng Li, Meng-Yuan Qi, Jian Zhao, Qi Zhao, Hong-Sheng Liu
2017 OncoTarget  
To develop novel anti-influenza therapies, we have constructed a machine-learning-based scoring function (RF-NA-Score) for the effective virtual screening of lead compounds targeting the viral neuraminidase  ...  Scoring functions specific for other drug targets could also be established with the same method.  ...  Their results showed that the machine-learning-based scoring functions were a substantial improvement on classical scoring functions in both scoring power (binding affinity prediction) and ranking power  ... 
doi:10.18632/oncotarget.20915 pmid:29137330 pmcid:PMC5669956 fatcat:opeazl4s2zg37i466qk232h2ga

Molecular Docking for Drug Discovery: Machine-Learning Approaches for Native Pose Prediction of Protein-Ligand Complexes [chapter]

Hossam M. Ashtawy, Nihar R. Mahapatra
2014 Lecture Notes in Computer Science  
An essential component of molecular docking programs is a scoring function (SF) that can be used to identify the most stable binding pose of a ligand, when bound to a receptor protein, from among a large  ...  We assess the docking accuracies of these new ML SFs as well as those of conventional SFs in the context of the 2007 PDBbind benchmark datasets on both diverse and homogeneous (protein-family-specific)  ...  Our approach is to couple the modeling power of flexible machine learning algorithms with training datasets comprising hundreds of protein-ligand complexes with native poses of known high-resolution 3D  ... 
doi:10.1007/978-3-319-09042-9_2 fatcat:lswfwhehrzhmnnzb7wxhym3ijm

ResAtom System: Protein and Ligand Affinity Prediction Model Based on Deep Learning [article]

Yeji Wang, Shuo Wu, Yanwen Duan, Yong Huang
2021 arXiv   pre-print
The existing affinity prediction and evaluation functions based on deep learning mostly rely on experimentally-determined conformations.  ...  At the same time, we evaluated the performance of a variety of existing scoring functions in combination with ResAtom-Score in the absence of experimentally-determined conformations.  ...  Acknowledgements This work was supported in parts by the NSFC Grant 81473124 (to Y. H.); the Chinese Ministry of Education 111 Project BP0820034 (to Y. D.)  ... 
arXiv:2105.05125v1 fatcat:tcyp7xqwwfcx7bsknvo4eujdga

A review of mathematical representations of biomolecules [article]

Duc D Nguyen, Zixuan Cang, Guo-Wei Wei
2019 arXiv   pre-print
We elucidate how the physical and biological challenges have guided the evolution and development of these mathematical apparatuses for massive and diverse biomolecular data.  ...  Recently, machine learning (ML) has established itself in various worldwide benchmarking competitions in computational biology, including Critical Assessment of Structure Prediction (CASP) and Drug Design  ...  Kaifu Gao for his contribution to our team's pose prediction in D3R Grand Challenge 4.  ... 
arXiv:1912.04724v1 fatcat:q4rxq55c7bckbcn7hk56lnwmxq

Protein-RNA Complexes and Efficient Automatic Docking: Expanding RosettaDock Possibilities

Adrien Guilhot-Gaudeffroy, Christine Froidevaux, Jérôme Azé, Julie Bernauer, Jinn-Moon Yang
2014 PLoS ONE  
Docking approaches that have been developed for proteins are often challenging to adapt for RNA because of its inherent flexibility and the structural data available being relatively scarce.  ...  Protein-RNA complexes provide a wide range of essential functions in the cell.  ...  Acknowledgments The authors thank Sid Chaudhury and Jeff Gray for their help with RosettaDock. Author Contributions  ... 
doi:10.1371/journal.pone.0108928 pmid:25268579 pmcid:PMC4182525 fatcat:te7vtho4onbyzdkupeug6oiwxu

DAKB-GPCRs: An Integrated Computational Platform for Drug Abuse Related GPCRs

Maozi Chen, Yankang Jing, Lirong Wang, Zhiwei Feng, Xiang-Qun (Sean) Xie
2019 Journal of Chemical Information and Modeling  
) target classification via machine learning methods that utilize both docking scores and similarity scores, and (5) a drug-target interaction network via Spider Plot.  ...  Our DAKB-GPCRs provides the following results for a query compound: (1) blood-brain barrier (BBB) plot via our BBB predictor, (2) docking scores via HTDocking, (3) similarity score via TargetHunter, (4  ...  The authors thank Si Chen, Yu Zhang, and Nan Wu for preparing the images of the website. The authors thank Yan Zhang for wiring a program that can automatically calculate the ROC curve.  ... 
doi:10.1021/acs.jcim.8b00623 pmid:30835466 pmcid:PMC6758544 fatcat:s2olrqklvnfkdn5qcafsj54v44

Computational prediction of potential inhibitors for SARS-COV-2 main protease based on machine learning, docking, MM-PBSA calculations, and metadynamics

Isabela de Souza Gomes, Charles Abreu Santana, Leandro Soriano Marcolino, Leonardo Henrique França de Lima, Raquel Cardoso de Melo-Minardi, Roberto Sousa Dias, Sérgio Oliveira de Paula, Sabrina de Azevedo Silveira, Alessio Lodola
2022 PLoS ONE  
Regarding the identification of molecules that can potentially inhibit the main protease of SARS-COV-2, compounds output by the machine learning step went through a docking experiment against SARS-COV-  ...  To assess the ability of our machine learning strategy to retrieve potential compounds we calculated the Enrichment Factor of compound datasets for three well known protein targets: HIV-1 reverse transcriptase  ...  There is a current trend to replace classical score functions with more sophisticated methods involving machine learning, to increase the accuracy of the tools [18] .  ... 
doi:10.1371/journal.pone.0267471 pmid:35452494 pmcid:PMC9032443 fatcat:bvankbfdajdsfdngzrjigqqkia

Assessing Molecular Docking Tools to Guide Targeted Drug Discovery of CD38 Inhibitors

Eric D. Boittier, Yat Yin Tang, McKenna E. Buckley, Zachariah P. Schuurs, Derek J. Richard, Neha S. Gandhi
2020 International Journal of Molecular Sciences  
The rankings based on scoring power were: Vina, PLANTS > Glide, Gold > Molegro >> AutoDock 4 >> rDock.  ...  Out of the top four performing programs, Glide had the only scoring function that did not appear to show bias towards overpredicting the affinity of the ligand-based on its size.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijms21155183 pmid:32707824 pmcid:PMC7432575 fatcat:igs7tekmtrft3dzcd2bzpwb5zu

Machine Learning Scoring Functions Based on Random Forest and Support Vector Regression [chapter]

Pedro J. Ballester
2012 Lecture Notes in Computer Science  
The scoring functions that attempt such computational prediction exploiting structural data are essential for analysing the outputs of Molecular Docking, which is in turn an important technique for drug  ...  Here the performance of the two most popular machine learning scoring functions for this task is analysed under exactly the same experimental conditions.  ...  The author thanks the Medical Research Council for a Methodology Research Fellowship.  ... 
doi:10.1007/978-3-642-34123-6_2 fatcat:swrzu4f3vbef7mrt2rlfgy3fvu

A case-based meta-learning algorithm boosts the performance of structure-based virtual screening

Xi Yun, Weiwei Han, Susan L. Epstein, Lei Xie
2013 2013 IEEE International Conference on Bioinformatics and Biomedicine  
State-of-the-art consensus scoring or ensemble learning methods assume each scoring function performs uniformly for all cases.  ...  Scoring functions from a diverse set of existing protein-ligand docking tools, however, often poorly distinguish bioactive compounds from inactive ones.  ...  at the College of Staten Island.  ... 
doi:10.1109/bibm.2013.6732464 dblp:conf/bibm/YunHEX13 fatcat:uehvu52s6zf2pcih2flm5snedm

Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins

Hossam M Ashtawy, Nihar R Mahapatra
2015 BMC Bioinformatics  
An essential component of molecular docking programs is a scoring function (SF) that can be used to identify the most stable binding pose of a ligand, when bound to a receptor protein, from among a large  ...  We also observed steady gains in the performance of these scoring functions as the training set size and number of features were increased by considering more protein-ligand complexes and/or more computationally-generated  ...  Declarations The publication costs for this article were sourced from the National Science  ... 
doi:10.1186/1471-2105-16-s6-s3 pmid:25916860 pmcid:PMC4416170 fatcat:3p5d7svtwvfjxowdm5j3o3puea

The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction

Hongjian Li, Jiangjun Peng, Yee Leung, Kwong-Sak Leung, Man-Hon Wong, Gang Lu, Pedro Ballester
2018 Biomolecules  
It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those  ...  Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs).  ...  Introduction A Scoring Function (SF) for structure-based protein-ligand binding affinity prediction has an essential influence on the reliability of molecular docking.  ... 
doi:10.3390/biom8010012 pmid:29538331 pmcid:PMC5871981 fatcat:k24riuwbmrga7gkkrxjo3apuzq

Machine‐learning scoring functions for structure‐based drug lead optimization

Hongjian Li, Kam‐Heung Sze, Gang Lu, Pedro J. Ballester
2020 Wiley Interdisciplinary Reviews. Computational Molecular Science  
Against the expectations of many experts, SFs employing deep learning techniques were not always more predictive than those based on more established machine learning techniques and, when they were, the  ...  The performance gap between classical and machine-learning SFs was large and has now broadened owing to methodological improvements and the availability of more training data.  ...  In contrast, approaches based on machine learning (ML) circumvent the limitation of imposing a fixed functional form for the SF, which is learnt instead from training data.  ... 
doi:10.1002/wcms.1465 fatcat:qnrk4qw3h5gjtcxncourqzhkxe
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