Biomedical Chemistry: Research and Methods, 2018, 1(3), e00060
The 40th Anniversary of the Institute of Physiologically Active Compounds of the Russian Academy of Sciences

Hydrogen Bond Contribution to Drug Bioavailability: cheminformatics approach

O.A. Raevsky

Institute of Physiologically Active Compounds of the Russian Academy of Sciences,
1 Severny proezd, Moscow region, Chernogolovka, 142432 Russia;*e-mail: raevsky@ipac.ac.ru

Key words: QSAR; HYBOT; hydrogen bond descriptors; bioavailability

DOI: 10.18097/BMCRM00060

The whole version of this paper is available in Russian.

A review, based mainly on own publications, is devoted to methods of investigation of “structure-bioavailability” relationships. The first part of this review contains information about classification of hydrogen bond descriptors, original 2D hydrogen bond thermodynamic descriptors, program HYBOT, original 3D hydrogen bonding potentials, original hydrogen bond surface area descriptors. The second part includes the results of applications of the above mentioned of hydrogen bond descriptors for prediction of bioavailability components such as lipophilicity, solubility in water and in physiological fluids, absorption and blood brain barrier permeability.
Figure 1. Hierarchy of the information level of various hydrogen bond descriptors [10].

Figure 2. TUnified scale of donor and acceptor factors of hydrogen bond (example for simple organic compounds).

Figure 3. Hydrogen bond potentials. The dependence of optimal energy from bond length.

Figure 4. The dependences of the fractions absorption (FA) from surface's HYBOT hydrogen bonds descriptors (OFEASA + OFEDSA).

CLOSE
Table 1. The simple and consensus AMP and LoReP models of solubility in water of 2,615 crystalline compounds.

CLOSE
Table 2. The coefficients of equation (19) and their statistical characteristics [60].

CLOSE
Table 3. Protocol of "CNS/not-CNS" classification of compounds by intuitive approaches and statistical methods. TP-correct recognition of "CNS", FN-incorrect recognition of "CNS", TN-correct recognition of "not-CNS", EP-misdiagnosis, "not CNS", SE-sensitivity (TP / TP + EN), SP- (TN / TN ++ EP), ACC- accuracy (TP + TN) / (TP + EN + TN + EP).

ACKNOWLEDGEMENTS

The work was performed within the framework of the state task for 2018 (the topic number 0090-2017-0020).

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