scholarly journals A Hybrid Knowledge-Based and Empirical Scoring Function for Protein–Ligand Interaction: SMoG2016

2017 ◽  
Vol 57 (3) ◽  
pp. 584-593 ◽  
Author(s):  
Théau Debroise ◽  
Eugene I. Shakhnovich ◽  
Nicolas Chéron
Keyword(s):  
Scoring Function ◽  
Ligand Interaction ◽  
Knowledge Based ◽  
Hybrid Knowledge ◽  
Protein Ligand Interaction ◽  
Empirical Scoring Function

scholarly journals CBSF: A New Empirical Scoring Function for Docking Parameterized by Weights of Neural Network

2019 ◽  
Vol 7 (1) ◽  
pp. 121-134
Author(s):  
Raulia R. Syrlybaeva ◽  
Marat R. Talipov
Keyword(s):  
Neural Network ◽  
Scoring Function ◽  
Binding Energies ◽  
Average Error ◽  
Ligand Interaction ◽  
Interacting Protein ◽  
Final Score ◽  
Protein Ligand Interaction ◽  
Weighting Coefficients ◽  
Empirical Scoring Function

AbstractA new CBSF empirical scoring function for the estimation of binding energies between proteins and small molecules is proposed in this report. The final score is obtained as a sum of three energy terms calculated using descriptors based on a simple counting of the interacting protein-ligand atomic pairs. All the required weighting coefficients for this method were derived from a pretrained neural network. The proposed method demonstrates a high accuracy and reproduces binding energies of protein-ligand complexes from the CASF-2016 test set with a standard deviation of 2.063 kcal/mol (1.511 log units) and an average error of 1.682 kcal/mol (1.232 log units). Thus, CBSF has a significant potential for the development of rapid and accurate estimates of the protein-ligand interaction energies.

Improving structure-based virtual screening performance via learning from scoring function components

2020 ◽  
Author(s):  
Guo-Li Xiong ◽  
Wen-Ling Ye ◽  
Chao Shen ◽  
Ai-Ping Lu ◽  
Ting-Jun Hou ◽  
...  
Keyword(s):  
Scoring Function ◽  
Scoring Functions ◽  
Ligand Interaction ◽  
Promising Alternative ◽  
Screening Performance ◽  
Comparable Performance ◽  
Protein Ligand Interaction ◽  
New Protein ◽  
Virtual Screening Performance ◽  
Energy Components

Abstract Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually emerged as a promising alternative to overcome the weaknesses of classical SFs. However, extensive efforts have been devoted to the development of SFs based on new protein–ligand interaction representations and advanced alternative ML algorithms instead of the energy components obtained by the decomposition of existing SFs. Here, we propose a new method named energy auxiliary terms learning (EATL), in which the scoring components are extracted and used as the input for the development of three levels of ML SFs including EATL SFs, docking-EATL SFs and comprehensive SFs with ascending VS performance. The EATL approach not only outperforms classical SFs for the absolute performance (ROC) and initial enrichment (BEDROC) but also yields comparable performance compared with other advanced ML-based methods on the diverse subset of Directory of Useful Decoys: Enhanced (DUD-E). The test on the relatively unbiased actives as decoys (AD) dataset also proved the effectiveness of EATL. Furthermore, the idea of learning from SF components to yield improved screening power can also be extended to other docking programs and SFs available.

scholarly journals Two Rules on the Protein-Ligand Interaction

2008 ◽  
Author(s):  
Xiaodong Pang ◽  
Linxiang Zhou ◽  
Lily Zhang ◽  
Lina Xu ◽  
Xinyi Zhang
Keyword(s):  
Ligand Interaction ◽  
Protein Ligand Interaction

scholarly journals Protein-ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange-correlation functional

2021 ◽  
Author(s):  
Lennart Gundelach ◽  
Christofer S Tautermann ◽  
Thomas Fox ◽  
Chris-Kriton Skylaris
Keyword(s):  
Drug Discovery ◽  
Ligand Binding ◽  
Dft Calculations ◽  
Quantum Mechanical ◽  
Free Energies ◽  
Ligand Interaction ◽  
Exchange Correlation ◽  
Ligand Free ◽  
Protein Ligand Interaction ◽  
Binding Free Energies

The accurate prediction of protein-ligand binding free energies with tractable computational methods has the potential to revolutionize drug discovery. Modeling the protein-ligand interaction at a quantum mechanical level, instead of...

scholarly journals A Hybrid Knowledge-Based Recommender for Product-Service Systems Mass Customization

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 296
Author(s):  
Laila Esheiba ◽  
Amal Elgammal ◽  
Iman M. A. Helal ◽  
Mohamed E. El-Sharkawi
Keyword(s):  
Recommender System ◽  
Mass Customization ◽  
Constraint Satisfaction Problem ◽  
Service Systems ◽  
Prototype System ◽  
Knowledge Based ◽  
Product Service ◽  
Wide Range ◽  
Hybrid Knowledge ◽  
Product Service Systems

Manufacturers today compete to offer not only products, but products accompanied by services, which are referred to as product-service systems (PSSs). PSS mass customization is defined as the production of products and services to meet the needs of individual customers with near-mass-production efficiency. In the context of the PSS mass customization environment, customers are overwhelmed by a plethora of previously customized PSS variants. As a result, finding a PSS variant that is precisely aligned with the customer’s needs is a cognitive task that customers will be unable to manage effectively. In this paper, we propose a hybrid knowledge-based recommender system that assists customers in selecting previously customized PSS variants from a wide range of available ones. The recommender system (RS) utilizes ontologies for capturing customer requirements, as well as product-service and production-related knowledge. The RS follows a hybrid recommendation approach, in which the problem of selecting previously customized PSS variants is encoded as a constraint satisfaction problem (CSP), to filter out PSS variants that do not satisfy customer needs, and then uses a weighted utility function to rank the remaining PSS variants. Finally, the RS offers a list of ranked PSS variants that can be scrutinized by the customer. In this study, the proposed recommendation approach was applied to a real-life large-scale case study in the domain of laser machines. To ensure the applicability of the proposed RS, a web-based prototype system has been developed, realizing all the modules of the proposed RS.

scholarly journals Fragment-centric topographic mapping method guides the understanding of ABCG2-inhibitor interactions

RSC Advances ◽  
2019 ◽  
Vol 9 (14) ◽  
pp. 7757-7766 ◽  
Author(s):  
Yao Wu ◽  
Xin-Ying Gao ◽  
Xin-Hui Chen ◽  
Shao-Long Zhang ◽  
Wen-Juan Wang ◽  
...  
Keyword(s):  
Mapping Method ◽  
Topographic Mapping ◽  
Ligand Interaction ◽  
Mapping Tool ◽  
Protein Ligand Interaction ◽  
Insight Into ◽  
Computational Strategy

Our study gains insight into the development of novel specific ABCG2 inhibitors, and develops a comprehensive computational strategy to understand protein ligand interaction with the help of AlphaSpace, a fragment-centric topographic mapping tool.

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