An enhancement of Bayesian inference network for ligand-based virtual screening using minifingerprints

2011 ◽  
Author(s):  
Ali Ahmed ◽  
Ammar Abdo ◽  
Naomie Salim
Keyword(s):  
Bayesian Inference ◽  
Virtual Screening ◽  
Inference Network

scholarly journals Implementing Relevance Feedback in Ligand-Based Virtual Screening Using Bayesian Inference Network

2011 ◽  
Vol 16 (9) ◽  
pp. 1081-1088 ◽  
Author(s):  
Ammar Abdo ◽  
Naomie Salim ◽  
Ali Ahmed
Keyword(s):  
Bayesian Inference ◽  
Virtual Screening ◽  
Bayesian Network ◽  
Relevance Feedback ◽  
Data Sets ◽  
Reference Structure ◽  
Feedback Information ◽  
Screening Experiments ◽  
Inference Network ◽  
Report Data

Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets.

Similarity-Based Virtual Screening with a Bayesian Inference Network

ChemMedChem ◽  
2009 ◽  
Vol 4 (2) ◽  
pp. 210-218 ◽  
Author(s):  
Ammar Abdo ◽  
Naomie Salim
Keyword(s):  
Bayesian Inference ◽  
Virtual Screening ◽  
Inference Network

scholarly journals Ligand-Based Virtual Screening Using Bayesian Inference Network and Reweighted Fragments

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Ali Ahmed ◽  
Ammar Abdo ◽  
Naomie Salim
Keyword(s):  
Bayesian Inference ◽  
Virtual Screening ◽  
Structural Heterogeneity ◽  
Reference Structure ◽  
Molecular Fragments ◽  
Feedback Information ◽  
Screening Experiments ◽  
Inference Network ◽  
Higher Weights ◽  
High Degree

Many of the similarity-based virtual screening approaches assume that molecular fragments that are not related to the biological activity carry the same weight as the important ones. This was the reason that led to the use of Bayesian networks as an alternative to existing tools for similarity-based virtual screening. In our recent work, the retrieval performance of the Bayesian inference network (BIN) was observed to improve significantly when molecular fragments were reweighted using the relevance feedback information. In this paper, a set of active reference structures were used to reweight the fragments in the reference structure. In this approach, higher weights were assigned to those fragments that occur more frequently in the set of active reference structures while others were penalized. Simulated virtual screening experiments with MDL Drug Data Report datasets showed that the proposed approach significantly improved the retrieval effectiveness of ligand-based virtual screening, especially when the active molecules being sought had a high degree of structural heterogeneity.

scholarly journals Binding Ensembles of p53-MDM2 Peptide Inhibitors by Combining Bayesian Inference and Atomistic Simulations

Molecules ◽  
2021 ◽  
Vol 26 (1) ◽  
pp. 198
Author(s):  
Lijun Lang ◽  
Alberto Perez
Keyword(s):  
Bayesian Inference ◽  
Virtual Screening ◽  
Statistical Mechanics ◽  
Small Molecules ◽  
Rational Design ◽  
Driving Forces ◽  
Peptide Inhibitors ◽  
Atomistic Simulations ◽  
Binding Mechanism ◽  
Intrinsically Disordered

Designing peptide inhibitors of the p53-MDM2 interaction against cancer is of wide interest. Computational modeling and virtual screening are a well established step in the rational design of small molecules. But they face challenges for binding flexible peptide molecules that fold upon binding. We look at the ability of five different peptides, three of which are intrinsically disordered, to bind to MDM2 with a new Bayesian inference approach (MELD × MD). The method is able to capture the folding upon binding mechanism and differentiate binding preferences between the five peptides. Processing the ensembles with statistical mechanics tools depicts the most likely bound conformations and hints at differences in the binding mechanism. Finally, the study shows the importance of capturing two driving forces to binding in this system: the ability of peptides to adopt bound conformations (ΔGconformation) and the interaction between interface residues (ΔGinteraction).

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