35 Result(s)
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Neural Networks Predict Protein Structure and Function
Both supervised and unsupervised neural networks have been applied to the prediction of protein structure and function. Here, we focus on feedforward neural networks and describe how these learning machines ca...
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Artificial Neural Networks in Biology and Chemistry—The Evolution of a New Analytical Tool
Once regarded as an eccentric and unpromising algorithm for the analysis of scientific data, the neural network has been developed in the last decade into a powerful computational tool. Its use now spans all a...
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Bayesian Regularization of Neural Networks
Bayesian regularized artificial neural networks (BRANNs) are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation. Bayesian regularization is a math...
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Artificial Neural Network Modeling in Environmental Toxicology
Artificial neural networks are increasingly used in environmental toxicology to find complex relationships between the ecotoxicity of xenobiotics and their structure or physicochemical properties. The raison d...
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Application of Artificial Neural Networks for Decision Support in Medicine
The emergence of drug resistant pathogens can reduce the efficacy of drugs commonly used to treat infectious diseases. Human immunodeficiency virus (HIV) is particularly sensitive to drug selection pressure, r...
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Peptide Bioinformatics- Peptide Classification Using Peptide Machines
Peptides scanned from whole protein sequences are the core information for many peptide bioinformatics research subjects, such as functional site prediction, protein structure identification, and protein funct...
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Associative Neural Network
An associative neural network (ASNN) is an ensemble-based method inspired by the function and structure of neural network correlations in brain. The method operates by simulating the short- and long-term memor...
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The Extraction of Information and Knowledge from Trained Neural Networks
In the past, neural networks were viewed as classification and regression systems whose internal representations were incomprehensible. It is now becoming apparent that algorithms can be designed that extract ...
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Overview of Artificial Neural Networks
The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modern drug discovery research requires so...
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Kohonen and Counterpropagation Neural Networks Applied for Map** and Interpretation of IR Spectra
The principles of learning strategy of Kohonen and counterpropagation neural networks are introduced. The advantages of unsupervised learning are discussed. The self-organizing maps produced in both methods ar...
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Neural Networks in Analytical Chemistry
This chapter covers a part of the spectrum of neural-network uses in analytical chemistry. Different architectures of neural networks are described briefly. The chapter focuses on the development of three-laye...
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Neural Networks in Building QSAR Models
This chapter critically reviews some of the important methods being used for building quantitative structure-activity relationship (QSAR) models using the artificial neural networks (ANNs). It attends predomin...
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Deep Learning and Computational Chemistry
Within the context of the latest resurgence in the application of artificial intelligence approaches, deep learning has undergone a renaissance over recent years. These methods have been applied to a number of...
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Network-Driven Drug Discovery
We describe an approach to early stage drug discovery that explicitly engages with the complexities of human biology. The combined computational and experimental approach is formulated on a conceptual framewor...
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De Novo Molecular Design with Chemical Language Models
Artificial intelligence (AI) offers new possibilities for hit and lead finding in medicinal chemistry. Several instances of AI have been used for prospective de novo drug design. Among these, chemical language...
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Deep Learning in Structure-Based Drug Design
Computational methods play an increasingly important role in drug discovery. Structure-based drug design (SBDD), in particular, includes techniques that take into account the structure of the macromolecular ta...
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Ultrahigh Throughput Protein–Ligand Docking with Deep Learning
Ultrahigh-throughput virtual screening (uHTVS) is an emerging field linking together classical docking techniques with high-throughput AI methods. We outline mechanistic docking models’ goals and successes. We...
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Artificial Intelligence in Compound Design
Artificial intelligence has seen an incredibly fast development in recent years. Many novel technologies for property prediction of drug molecules as well as for the design of novel molecules were introduced b...
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Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints
The well-known concept of quantitative structure–activity relationships (QSAR) has been gaining significant interest in the recent years. Data, descriptors, and algorithms are the main pillars to build useful ...
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Artificial Intelligence–Enabled De Novo Design of Novel Compounds that Are Synthesizable
Development of computer-aided de novo design methods to discover novel compounds in a speedy manner to treat human diseases has been of interest to drug discovery scientists for the past three decades. In the ...
