In Silico Structure-Activity-Relationship (SAR) Models From Machine Learning: A Review
نویسنده
چکیده
In this article, we review the recent development for in silico Structure-Activity-Relationship (SAR) models using machine-learning techniques. The review focuses on the following topics: machine-learning algorithms for computational SAR models, single-target-oriented SAR methodologies, Chemogenomics, and future trends. We try to provide the state-of-the-art SAR methods as well as the most up-to-date advancement, in order for the researchers to have a general overview at this area. Drug Dev Res, 2010. r 2010 Wiley-Liss, Inc.
منابع مشابه
Multi-Assay-Based Structure-Activity Relationship Models: Improving Structure-Activity Relationship Models by Incorporating Activity Information from Related Targets
Structure-activity relationship (SAR) models are used to inform and to guide the iterative optimization of chemical leads, and they play a fundamental role in modern drug discovery. In this paper, we present a new class of methods for building SAR models, referred to as multi-assay based, that utilize activity information from different targets. These methods first identify a set of targets tha...
متن کاملAffinity-based Structure-Activity-Relationship Models: Improving Structure-Activity-Relationship Models by Incorporating Activity Information from Related Targets
Structure-activity-relationship (SAR) models are used to inform and guide the iterative optimization of chemical leads, and play a fundamental role in modern drug discovery. In this paper we present a new class of methods for building SAR models, referred to as affinity-based, that utilize activity information from different targets. These methods first identify a set of targets that are relate...
متن کاملIn-silico prediction of Cellular Responses to Polymeric Biomaterials from Their Molecular Descriptors
In this work quantitative structure activity relationship (QSAR) methodology was applied for modeling and prediction of cellular response to polymers that have been designed for tissue engineering. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regressions (MLR) and artificial neural network (ANN) methods. The root m...
متن کاملImproved SAR Models - Exploiting the Target-Ligand Relationships
Small organic molecules, by binding to different proteins, can be used to modulate (inhibit/activate) their functions for therapeutic purposes and to elucidate the molecular mechanisms underlying biological processes. Over the decades structure-activity-relationship (SAR) models have been developed to quantify the bioactivity relationship of a chemical compound interacting with a target protein...
متن کاملProtein Secondary Structure Prediction: a Literature Review with Focus on Machine Learning Approaches
DNA sequence, containing all genetic traits is not a functional entity. Instead, it transfers to protein sequences by transcription and translation processes. This protein sequence takes on a 3D structure later, which is a functional unit and can manage biological interactions using the information encoded in DNA. Every life process one can figure is undertaken by proteins with specific functio...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010