Influence of Descriptor Implementation on Compound Ranking Based on Multiparameter Assessment

Most of the common molecular descriptors have numerous different implementations. This can influence the results of compound prioritization based on the multiparameter assessment (MPA) approach that allows a medicinal chemist to simultaneously analyze and achieve the desired balance of the diverse and often conflicting molecular and pharmacological properties. In this study, we analyzed the feasibility of using different implementations of common descriptors (logP, logS, TPSA, logBB, hERG, nHBA) interchangeably in predesigned sets of requirements in the course of multiparameter compound optimization. The influence of methods of descriptor calculation, continuity or discreteness of their values, their applicability domains, as well as of the nature of desirability functions in an MPA profile were examined in terms of the stability of MPA compound ranking. It was shown that the interchangeable use of different methods of descriptor calculation is reliably acceptable only for continuously distributed parameters transformed by a smooth desirability function. If a descriptor in an MPA scheme is discretely distributed, only the implementation that was used for building the scoring profile may be used for assessment. An inconsistency of assessment due to different applicability domains of descriptors was also demonstrated

Influence of Descriptor Implementation on Compound Ranking Based on Multiparameter Assessment

Most of the common molecular descriptors have numerous different implementations. This can influence the results of compound prioritization based on the multiparameter assessment (MPA) approach that allows a medicinal chemist to simultaneously analyze and achieve the desired balance of the diverse and often conflicting molecular and pharmacological properties. In this study, we analyzed the feasibility of using different implementations of common descriptors (logP, logS, TPSA, logBB, hERG, nHBA) interchangeably in predesigned sets of requirements in the course of multiparameter compound optimization. The influence of methods of descriptor calculation, continuity or discreteness of their values, their applicability domains, as well as of the nature of desirability functions in an MPA profile were examined in terms of the stability of MPA compound ranking. It was shown that the interchangeable use of different methods of descriptor calculation is reliably acceptable only for continuously distributed parameters transformed by a smooth desirability function. If a descriptor in an MPA scheme is discretely distributed, only the implementation that was used for building the scoring profile may be used for assessment. An inconsistency of assessment due to different applicability domains of descriptors was also demonstrated

Influence of Descriptor Implementation on Compound Ranking Based on Multiparameter Assessment

Most of the common molecular descriptors have numerous different implementations. This can influence the results of compound prioritization based on the multiparameter assessment (MPA) approach that allows a medicinal chemist to simultaneously analyze and achieve the desired balance of the diverse and often conflicting molecular and pharmacological properties. In this study, we analyzed the feasibility of using different implementations of common descriptors (logP, logS, TPSA, logBB, hERG, nHBA) interchangeably in predesigned sets of requirements in the course of multiparameter compound optimization. The influence of methods of descriptor calculation, continuity or discreteness of their values, their applicability domains, as well as of the nature of desirability functions in an MPA profile were examined in terms of the stability of MPA compound ranking. It was shown that the interchangeable use of different methods of descriptor calculation is reliably acceptable only for continuously distributed parameters transformed by a smooth desirability function. If a descriptor in an MPA scheme is discretely distributed, only the implementation that was used for building the scoring profile may be used for assessment. An inconsistency of assessment due to different applicability domains of descriptors was also demonstrated

Influence of Descriptor Implementation on Compound Ranking Based on Multiparameter Assessment

Most of the common molecular descriptors have numerous different implementations. This can influence the results of compound prioritization based on the multiparameter assessment (MPA) approach that allows a medicinal chemist to simultaneously analyze and achieve the desired balance of the diverse and often conflicting molecular and pharmacological properties. In this study, we analyzed the feasibility of using different implementations of common descriptors (logP, logS, TPSA, logBB, hERG, nHBA) interchangeably in predesigned sets of requirements in the course of multiparameter compound optimization. The influence of methods of descriptor calculation, continuity or discreteness of their values, their applicability domains, as well as of the nature of desirability functions in an MPA profile were examined in terms of the stability of MPA compound ranking. It was shown that the interchangeable use of different methods of descriptor calculation is reliably acceptable only for continuously distributed parameters transformed by a smooth desirability function. If a descriptor in an MPA scheme is discretely distributed, only the implementation that was used for building the scoring profile may be used for assessment. An inconsistency of assessment due to different applicability domains of descriptors was also demonstrated

Image_1_Experimental Evaluation of the Protective Efficacy of Tick-Borne Encephalitis (TBE) Vaccines Based on European and Far-Eastern TBEV Strains in Mice and in Vitro.PDF

<p>Tick-borne encephalitis (TBE), caused by the TBE virus (TBEV), is a serious public health threat in northern Eurasia. Three subtypes of TBEV are distinguished. Inactivated vaccines are available for TBE prophylaxis, and their efficacy to prevent the disease has been demonstrated by years of implication. Nevertheless, rare TBE cases among the vaccinated have been registered. The present study aimed to evaluate the protective efficacy of 4 TBEV vaccines against naturally circulating TBEV variants. For the first time, the protection was evaluated against an extended number of phylogenetically distinct TBEV strains isolated in different years in different territories. The protective effect did not strongly depend on the infectious dose of the challenge virus or the scheme of vaccination. All vaccines induced neutralizing antibodies in protective titers against the TBEV strains used, although the vaccines varied in the spectra of induced antibodies and protective efficacy. The protective efficacy of the vaccines depended on the individual properties of the vaccine strain and the challenge virus, rather than on the subtypes. The neutralization efficiency appeared to be dependent not only on the presence of antibodies to particular epitopes and the amino acid composition of the virion surface but also on the intrinsic properties of the challenge virus E protein structure.</p

Various synthetic and natural insect repellents and attractants.

<p>Various synthetic and natural insect repellents and attractants.</p

Chemical structures of test set compounds.

<p>Chemical structures of test set compounds.</p

3D predicted binding mode for YF24 ((1S,2S)-2-phenylcyclohexanol).

<p>(<b>B</b>) Atomic details on how <b>YF24</b> binds to OBP1, as depicted by ICM Browser (MolSoft, <a href="http://www.molsoft.com" target="_blank"><u>www.molsoft.com</u></a>). An anchoring interaction that defines the position and orientation of the ligand is the hydrogen bond between the hydroxyl-group of <b>YF24</b> and the backbone carbonyl group of Phe123. The rest of interaction is driven by a set of aromatic and hydrophobic residues, Phe59, Leu76, Trp114, Tyr122 and Phe123, that accommodates the cyclohexylbenzene core. Only proximate residues making contacts with <b>YF24</b> are shown.</p

MFTA model: (a) molecular super-graph, (b) factor dynamics, and (c) fit plot.

<p>(a) The molecular supergraph is shown with two superimposed structures: DEET and <i>N</i>-cyclohexyl-<i>N</i>-ethyl-3-methylbutanamide (5m). The manner in which structures appear on MSG depends on how they can be superimposed onto the MSG as a whole. (b) The plot displays the change in correlation coefficient (R) and squared cross-validation coefficient (Q²) change as the number of factors changes. The best model is the one with the minimum possible number of factors and with R and Q² at their highest values.</p

Chemical structures of 27 assorted compounds.

<p>The most active compounds are marked with squares.</p