Statistical investigation of large volumes of data with respect to the biological activity of compounds III. Selection of a determinant for predicting biological activity

Avidon, V. V.; Arolovich, V. S.; Kozlova, S. P.; Piruzyan, L. A.

doi:10.1007/bf00779056

Cited by 1 publication

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“…The first attempts to develop computer-aided methods for the prediction of biological activity from chemical structures were made in the 1970s [14,15]. Further progress was made in the following decade [16][17][18][19], but for various reasons the problem was not completely solved at that time. However, the obtained experience had provided the basis for the later development of PASS [20,21].…”

Section: Pass Approachmentioning

confidence: 99%

Multi-Targeted Natural Products Evaluation Based on Biological Activity Prediction with PASS

Lagunin

Филимонов²,

Poroikov³

2010

CPD

134

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Natural products found a wide use in folk medicine. Presently, when routine development of new drugs faced a considerable challenge, they become an inspiration and valuable source in drugs discovery. Rather complex and diverse chemical structures of natural compounds provide a basis for modulation of different biological targets. Natural compounds exhibit a multitargeted action that may lead to additive/synergistic or antagonistic effects. Rational design of more safe and potent pharmaceuticals requires an estimation of probable multiple actions of natural products. Our software PASS can perform such estimation. It predicts with reasonable accuracy over 3500 pharmacotherapeutic effects, mechanisms of action, interaction with the metabolic system, and specific toxicity for drug-like molecules on the basis of their structural formulae. We analyzed PASS predictions utilizing PharmaExpert, which performs selection of compounds with multiple mechanisms of action, analysis of activity-activity relationships and drug-drug interactions. The paper describes an application of PASS and PharmaExpert to the evaluation of biological activity of natural compounds including marine sponge alkaloids, triterpenoids of lupane group, and their derivatives. Proposed computer-aided methods can generate combinatorial libraries of macrolides. They help to select the most promising pharmaceutical leads with the required properties. Case study, based on the analysis of biological activity spectra predicted for St John's Wort constituents, clearly demonstrates capabilities of computational methods in the evaluation of multitargeted actions, additive/synergistic and/or antagonistic effects of natural products.

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Section: Pass Approachmentioning

confidence: 99%