Journal Article10.1111/CBDD.13388
Homology modeling in drug discovery: Overview, current applications, and future perspectives.
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TL;DR: There has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling, which contributes to the identification of novel drug candidates.
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Abstract: Homology modeling is one of the computational structure prediction methods that are used to determine protein 3D structure from its amino acid sequence. It is considered to be the most accurate of the computational structure prediction methods. It consists of multiple steps that are straightforward and easy to apply. There are many tools and servers that are used for homology modeling. There is no single modeling program or server which is superior in every aspect to others. Since the functionality of the model depends on the quality of the generated protein 3D structure, maximizing the quality of homology modeling is crucial. Homology modeling has many applications in the drug discovery process. Since drugs interact with receptors that consist mainly of proteins, protein 3D structure determination, and thus homology modeling is important in drug discovery. Accordingly, there has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling. This contributes to the identification of novel drug candidates. Homology modeling plays an important role in making drug discovery faster, easier, cheaper, and more practical. As new modeling methods and combinations are introduced, the scope of its applications widens.
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Citations
Molecular Docking: Shifting Paradigms in Drug Discovery.
Luca Pinzi,Giulio Rastelli +1 more
TL;DR: This review describes how molecular docking was firstly applied to assist in drug discovery tasks, and illustrates newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling.
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Artificial intelligence to deep learning: machine intelligence approach for drug discovery.
TL;DR: In this article, Artificial Neural Networks and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity.
Advances in de Novo Drug Design: From Conventional to Machine Learning Methods.
Varnavas D. Mouchlis,Antreas Afantitis,Angela Serra,Michele Fratello,Anastasios G. Papadiamantis,Vassilis Aidinis,Iseult Lynch,Dario Greco,Georgia Melagraki +8 more
TL;DR: Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures as mentioned in this paper, which has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural network, generative adversarial networks, and autoencoders.
AlphaFold2 and its applications in the fields of biology and medicine
TL;DR: The AlphaFold2 (AF2) as discussed by the authors is an artificial intelligence (AI) system developed by DeepMind that can predict 3D structures of proteins from amino acid sequences with atomic-level accuracy.
Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation.
TL;DR: To assess the present and future value of simulation for drug discovery, this work reviews key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest.
204
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