One-class approach: models for virtual screening of non-nucleoside HIV-1 reverse transcriptase inhibitors based on the concept of continuous molecular fields

Karpov, Pavel; Baskin, Igor I.; Zhokhova, N. I.; Nawrozkij, M. B.; Zefirov, A. N.; Yablokov, A. S.; Novakov, I. A.; Зефиров, Н. С.

doi:10.1007/s11172-011-0372-8

Cited by 7 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In conjunction with ker-nel-based methods of one-class classification (novelty detection), such as the One-Class Support Vector Machines (1-SVM) [11], the use of continuous molecular fields allows one to set up a ligand-based virtual screening procedure for searching huge databases of available chemical compounds and retrieving from them potentially biological active compounds (hits), molecular fields of which are similar to the "idealized configuration" (which reflects the structure of the corresponding binding site in biological macromolecule and can be viewed as a "negative image" of its molecular fields) learned by the corresponding one-class model. The feasibility of this approach in practice has also been demonstrated by us [12,13].…”

Section: Introductionmentioning

confidence: 86%

“… 13). where index s counts different ionization (protonation) states, index t counts tautomers, index c counts conformers, v s is the population of the ionization state s, v st is the relative population of tautomer t in ionization state s, while v stc is the relative population of the conformer c of the molecule in ionization state s and in tautomeric state t. Populations v s , v st and v stc can be assessed using molecular modeling simulations.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Continuous Molecular Fields Approach Applied to Structure-Activity Modeling

Baskin

Zhokhova

2014

Challenges and Advances in Computational Chemistry and Physics

View full text Add to dashboard Cite

The Method of Continuous Molecular Fields is a universal approach to predict various properties of chemical compounds, in which molecules are represented by means of continuous fields (such as electrostatic, steric, electron density functions, etc). The essence of the proposed approach consists in performing statistical analysis of functional molecular data by means of joint application of kernel machine learning methods and special kernels which compare molecules by computing overlap integrals of their molecular fields. This approach is an alternative to traditional methods of building 3D "structure-activity" and "structureproperty" models based on the use of fixed sets of molecular descriptors. The methodology of the approach is described in this chapter, followed by its application to building regression 3D-QSAR models and conducting virtual screening based on one-class classification models. The main directions of the further development of this approach are outlined at the end of the chapter.

show abstract

Section: Introductionmentioning

confidence: 86%

mentioning

confidence: 99%

Continuous Molecular Fields Approach Applied to Structure-Activity Modeling

Baskin

Zhokhova

2014

Challenges and Advances in Computational Chemistry and Physics

View full text Add to dashboard Cite

show abstract

“…It is worth noticing that many DABOs exhibit high activity against hepatitis B 61,247 and influenza virus es. 247,252 Due to the specificity of structure-activity relation ship in the series of DABO derivatives determined by the substitution pattern in the pyrimidine cycle, QSAR (Quan titative Structure Activity Relationship) modeling [253][254][255][256][257] based, in particular, on the continuous molecular field method 254 become recently necessary to the rational de sign of novel anti HIV agents of this group.…”

Section: Basic Structure-antiviral Activity Relationship Of Dabo Deri...mentioning

confidence: 99%

6-(Arylmethyl)pyrimidin-4(3H)-ones: anthology and prospects of highly efficient anti-HIV agents

et al. 2012

Self Cite

View full text Add to dashboard Cite

show abstract

Energy‐based Neural Networks as a Tool for Harmony‐based Virtual Screening

Zhokhova

Baskin

2017

Molecular Informatics

View full text Add to dashboard Cite

In Energy-Based Neural Networks (EBNNs), relationships between variables are captured by means of a scalar function conventionally called "energy". In this article, we introduce a procedure of "harmony search", which looks for compounds providing the lowest energies for the EBNNs trained on active compounds. It can be considered as a special kind of similarity search that takes into account regularities in the structures of active compounds. In this paper, we show that harmony search can be used for performing virtual screening. The performance of the harmony search based on two types of EBNNs, the Hopfield Networks (HNs) and the Restricted Boltzmann Machines (RBMs), was compared with the performance of the similarity search based on Tanimoto coefficient with "data fusion". The AUC measure for ROC curves and 1 %-enrichment rates for 20 targets were used in the benchmarking. Five different scores were computed: the energy for HNs, the free energy and the reconstruction error for RBMs, the mean and the maximum values of Tanimoto coefficients. The performance of the harmony search was shown to be comparable or even superior (significantly for several targets) to the performance of the similarity search. Important advantages of using the harmony search for virtual screening are very high computational efficiency of prediction, the ability to reveal and take into account regularities in active structures, flexibility and interpretability of models, etc.

show abstract

One-class approach: models for virtual screening of non-nucleoside HIV-1 reverse transcriptase inhibitors based on the concept of continuous molecular fields

Cited by 7 publications

References 21 publications

Continuous Molecular Fields Approach Applied to Structure-Activity Modeling

Continuous Molecular Fields Approach Applied to Structure-Activity Modeling

6-(Arylmethyl)pyrimidin-4(3H)-ones: anthology and prospects of highly efficient anti-HIV agents

Energy‐based Neural Networks as a Tool for Harmony‐based Virtual Screening

Contact Info

Product

Resources

About