Abstract
Chemical liabilities, such as adverse effects and toxicity, play a significant role in modern drug discovery process. In silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Herein, we propose an approach combining several classification and chemography methods to be able to predict chemical liabilities and to interpret obtained results in the context of impact of structural changes of compounds on their pharmacological profile. To our knowledge for the first time, the supervised extension of Generative Topographic Mapping is proposed as an effective new chemography method. New approach for mapping new data using supervised Isomap without re-building models from the scratch has been proposed. Two approaches for estimation of model’s applicability domain are used in our study to our knowledge for the first time in chemoinformatics. The structural alerts responsible for the negative characteristics of pharmacological profile of chemical compounds has been found as a result of model interpretation.
Highlights
During the past decade, computational technologies and predictive tools have been deeply integrated in the modern drug discovery process and changed this process extracting the useful knowledge embedded in the complex arrays of chemical and biological information to select the most promising compounds as early as possible and to reveal chemical liabilities in order to reduce the risk of late stage attrition [1,2]
It is seen that for Generative Topographic Mapping (GTM) approach IPLF descriptors shown to be less effective than others, while applying molecular fingerprints for both Support vector machines (SVM) and GTM approaches led to high values of Balanced Accuracy
Support Vector Machines, Generative Topographic Mapping and Probabilistic Neural Network were used for classification
Summary
Computational technologies and predictive tools have been deeply integrated in the modern drug discovery process and changed this process extracting the useful knowledge embedded in the complex arrays of chemical and biological information to select the most promising compounds as early as possible and to reveal chemical liabilities in order to reduce the risk of late stage attrition [1,2]. We propose an approach combining several classification and chemography [3] methods to assess chemical liabilities in silico and to interpret obtained results in the context of impact of structural changes of compounds on their implementations of these approaches have been used in a number of studies in chemoinformatics [25,26,27,28,29,30,31,32] These two representatives of nonlinear dimensionality reduction methods are related to two different families: distancebased approaches and topology based approaches. We use the representatives of two families of AD methods: distance-based (Ball) [50] and probability-based (Local Outlier Factor LOF) [51]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
