J Chem Inf Model - Localized heuristic inverse quantitative structure activity relationship with bulk descriptors using numerical gradients.

Tópicos

{ compound(1573) activ(1297) structur(1058) }
{ process(1125) use(805) approach(778) }
{ imag(2830) propos(1344) filter(1198) }
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{ studi(1119) effect(1106) posit(819) }
{ research(1218) medic(880) student(794) }
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Resumo

State-of-the-art quantitative structure-activity relationship (QSAR) models are often based on nonlinear machine learning algorithms, which are difficult to interpret. From a pharmaceutical perspective, QSARs are used to enhance the chemical design process. Ultimately, they should not only provide a prediction but also contribute to a mechanistic understanding and guide modifications to the chemical structure, promoting compounds with desirable biological activity profiles. Global ranking of descriptor importance and inverse QSAR have been used for these purposes. This paper introduces localized heuristic inverse QSAR, which provides an assessment of the relative ability of the descriptors to influence the biological response in an area localized around the predicted compound. The method is based on numerical gradients with parameters optimized using data sets sampled from analytical functions. The heuristic character of the method reduces the computational requirements and makes it applicable not only to fragment based methods but also to QSARs based on bulk descriptors. The application of the method is illustrated on congeneric QSAR data sets, and it is shown that the predicted influential descriptors can be used to guide structural modifications that affect the biological response in the desired direction. The method is implemented into the AZOrange Open Source QSAR package. The current implementation of localized heuristic inverse QSAR is a step toward a generally applicable method for elucidating the structure activity relationship specifically for a congeneric region of chemical space when using QSARs based on bulk properties. Consequently, this method could contribute to accelerating the chemical design process in pharmaceutical projects, as well as provide information that could enhance the mechanistic understanding for individual scaffolds.

Resumo Limpo

stateoftheart quantit structureact relationship qsar model often base nonlinear machin learn algorithm difficult interpret pharmaceut perspect qsar use enhanc chemic design process ultim provid predict also contribut mechanist understand guid modif chemic structur promot compound desir biolog activ profil global rank descriptor import invers qsar use purpos paper introduc local heurist invers qsar provid assess relat abil descriptor influenc biolog respons area local around predict compound method base numer gradient paramet optim use data set sampl analyt function heurist charact method reduc comput requir make applic fragment base method also qsar base bulk descriptor applic method illustr congener qsar data set shown predict influenti descriptor can use guid structur modif affect biolog respons desir direct method implement azorang open sourc qsar packag current implement local heurist invers qsar step toward general applic method elucid structur activ relationship specif congener region chemic space use qsar base bulk properti consequ method contribut acceler chemic design process pharmaceut project well provid inform enhanc mechanist understand individu scaffold

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