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Probabilistic Approach for Virtual Screening Based on Multiple Pharmacophores

Pharmacophore modeling is usually considered as a special type of virtual screening without probabilistic nature. Correspondence of at least one conformation of a molecule to pharmacophore is considered as evidence of its bioactivity. We show that pharmacophores can be treated as one-class machine l...

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Autores principales: Madzhidov, Timur I., Rakhimbekova, Assima, Kutlushuna, Alina, Polishchuk, Pavel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7024325/
https://www.ncbi.nlm.nih.gov/pubmed/31963467
http://dx.doi.org/10.3390/molecules25020385
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author Madzhidov, Timur I.
Rakhimbekova, Assima
Kutlushuna, Alina
Polishchuk, Pavel
author_facet Madzhidov, Timur I.
Rakhimbekova, Assima
Kutlushuna, Alina
Polishchuk, Pavel
author_sort Madzhidov, Timur I.
collection PubMed
description Pharmacophore modeling is usually considered as a special type of virtual screening without probabilistic nature. Correspondence of at least one conformation of a molecule to pharmacophore is considered as evidence of its bioactivity. We show that pharmacophores can be treated as one-class machine learning models, and the probability the reflecting model’s confidence can be assigned to a pharmacophore on the basis of their precision of active compounds identification on a calibration set. Two schemes (Max and Mean) of probability calculation for consensus prediction based on individual pharmacophore models were proposed. Both approaches to some extent correspond to commonly used consensus approaches like the common hit approach or the one based on a logical OR operation uniting hit lists of individual models. Unlike some known approaches, the proposed ones can rank compounds retrieved by multiple models. These approaches were benchmarked on multiple ChEMBL datasets used for ligand-based pharmacophore modeling and externally validated on corresponding DUD-E datasets. The influence of complexity of pharmacophores and their performance on a calibration set on results of virtual screening was analyzed. It was shown that Max and Mean approaches have superior early enrichment to the commonly used approaches. Thus, a well-performing, easy-to-implement, and probabilistic alternative to existing approaches for pharmacophore-based virtual screening was proposed.
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spelling pubmed-70243252020-03-11 Probabilistic Approach for Virtual Screening Based on Multiple Pharmacophores Madzhidov, Timur I. Rakhimbekova, Assima Kutlushuna, Alina Polishchuk, Pavel Molecules Communication Pharmacophore modeling is usually considered as a special type of virtual screening without probabilistic nature. Correspondence of at least one conformation of a molecule to pharmacophore is considered as evidence of its bioactivity. We show that pharmacophores can be treated as one-class machine learning models, and the probability the reflecting model’s confidence can be assigned to a pharmacophore on the basis of their precision of active compounds identification on a calibration set. Two schemes (Max and Mean) of probability calculation for consensus prediction based on individual pharmacophore models were proposed. Both approaches to some extent correspond to commonly used consensus approaches like the common hit approach or the one based on a logical OR operation uniting hit lists of individual models. Unlike some known approaches, the proposed ones can rank compounds retrieved by multiple models. These approaches were benchmarked on multiple ChEMBL datasets used for ligand-based pharmacophore modeling and externally validated on corresponding DUD-E datasets. The influence of complexity of pharmacophores and their performance on a calibration set on results of virtual screening was analyzed. It was shown that Max and Mean approaches have superior early enrichment to the commonly used approaches. Thus, a well-performing, easy-to-implement, and probabilistic alternative to existing approaches for pharmacophore-based virtual screening was proposed. MDPI 2020-01-17 /pmc/articles/PMC7024325/ /pubmed/31963467 http://dx.doi.org/10.3390/molecules25020385 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Madzhidov, Timur I.
Rakhimbekova, Assima
Kutlushuna, Alina
Polishchuk, Pavel
Probabilistic Approach for Virtual Screening Based on Multiple Pharmacophores
title Probabilistic Approach for Virtual Screening Based on Multiple Pharmacophores
title_full Probabilistic Approach for Virtual Screening Based on Multiple Pharmacophores
title_fullStr Probabilistic Approach for Virtual Screening Based on Multiple Pharmacophores
title_full_unstemmed Probabilistic Approach for Virtual Screening Based on Multiple Pharmacophores
title_short Probabilistic Approach for Virtual Screening Based on Multiple Pharmacophores
title_sort probabilistic approach for virtual screening based on multiple pharmacophores
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7024325/
https://www.ncbi.nlm.nih.gov/pubmed/31963467
http://dx.doi.org/10.3390/molecules25020385
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