Search results for "Applicability domain"
showing 5 items of 5 documents
Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review
2006
Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complementing the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationship (QSAR) analysis, a field with established methodology and successful history. In this review, we discuss the computational methods for building QSAR models. We start with outlining their usefulness in high-throughput screening and identifying the general scheme of a QSAR model. Following, we focus on the methodologies in constructing three main components of QSAR model, namely the methods for describing the molecular structure …
Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices
2012
In this paper, the authors present an effort to increase the applicability domain (AD) by means of retraining models using a database of 701 great dissimilar molecules presenting anti-tyrosinase activity and 728 drugs with other uses. Atom-based linear indices and best subset linear discriminant analysis (LDA) were used to develop individual classification models. Eighteen individual classification-based QSAR models for the tyrosinase inhibitory activity were obtained with global accuracy varying from 88.15-91.60% in the training set and values of Matthews correlation coefficients (C) varying from 0.76-0.82. The external validation set shows globally classifications above 85.99% and 0.72 fo…
Chemometric and chemoinformatic analyses of anabolic and androgenic activities of testosterone and dihydrotestosterone analogues
2008
Predictive quantitative structure-activity relationship (QSAR) models of anabolic and androgenic activities for the testosterone and dihydrotestosterone steroid analogues were obtained by means of multiple linear regression using quantum and physicochemical molecular descriptors (MD) as well as a genetic algorithm for the selection of the best subset of variables. Quantitative models found for describing the anabolic (androgenic) activity are significant from a statistical point of view: R2 of 0.84 (0.72 and 0.70). A leave-one-out cross-validation procedure revealed that the regression models had a fairly good predictability [q2 of 0.80 (0.60 and 0.59)]. In addition, other QSAR models were …
A comparative assessment of energy demand and life cycle costs for additive- and subtractive-based manufacturing approaches
2020
Abstract The applicability domain of Additive Manufacturing (AM) processes, apart from technological and quality results, relies on environmental and cost performance. These aspects still need to be better understood. To this aim, comparative analyses with conventional manufacturing routes are needed. In this paper, empirical cost and energy requirement models are suggested to assess subtractive- (machining) and additive- (Electron Beam Melting) based manufacturing approaches for the production of Ti-6Al-4V components. A life-cycle perspective is adopted, and all the steps from the input material production to the post-AM processing operations and the use phase are included. The analyses ha…
Modeling Chronic Toxicity: A Comparison of Experimental Variability With (Q)SAR/Read-Across Predictions
2018
This study compares the accuracy of (Q)SAR/read-across predictions with the experimental variability of chronic lowest-observed-adverse-effect levels (LOAELs) from in vivo experiments. We could demonstrate that predictions of the lazy structure-activity relationships (lazar) algorithm within the applicability domain of the training data have the same variability as the experimental training data. Predictions with a lower similarity threshold (i.e., a larger distance from the applicability domain) are also significantly better than random guessing, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended.