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008			211103s2022 xxua o |||| 0|eng d
020			_a9781071617878
024	7		_a10.1007/978-1-0716-1787-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRS420 _b2022 EB
245	0	0	_aArtificial Intelligence in Drug Design _cedited by Alexander Heifetz
250			_a1st edition 2022
264		1	_aNew York, NY _bSpringer International Publising _c2022
300			_a1 recurso en línea (XI, 529 páginas) _b103 ilustraciones, 89 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aMethods in Molecular Biology _x1940-6029 _v2390
505	0		_aApplications of Artificial Intelligence in Drug Design: Opportunities and Challenges -- Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints -- Fighting COVID-19 with Artificial Intelligence -- Application of Artificial Intelligence and Machine Learning in Drug Discovery -- Deep Learning and Computational Chemistry -- Has Drug Design Augmented by Artificial Intelligence Become a Reality? -- Network Driven Drug Discovery -- Predicting Residence Time of GPCR Ligands with Machine Learning -- De Novo Molecular Design with Chemical Language Models -- Deep Neural Networks for QSAR -- Deep Learning in Structure-Based Drug Design -- Deep Learning Applied to Ligand-Based De Novo Drug Design -- Ultra-High Throughput Protein-Ligand Docking with Deep Learning -- Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors -- Artificial Intelligence in Compound Design -- Artificial Intelligence, Machine Learning, and Deep Learning in Real Life Drug Design Cases -- Artificial Intelligence-Enabled De Novo Design of Novel Compounds that are Synthesizable -- Machine Learning from Omics Data -- Deep Learning in Therapeutic Antibody Development -- Machine Learning for In Silico ADMET Prediction -- Opportunities and Considerations in the Application of Artificial Intelligence to Pharmacokinetic Prediction -- Artificial Intelligence in Drug Safety and Metabolism -- Molecule Ideation Using Matched Molecular Pairs.
520			_aThis volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers.
988			_aSpringer_Protocols_2022
650		7	_2embne _9395061 _aMedicamentos _xDiseño
650		7	_2embne _aInteligencia artificial _9413115
776	0	8	_iPrinted edition: _z9781071617861
776	0	8	_iPrinted edition: _z9781071617885
776	0	8	_iPrinted edition: _z9781071617892
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-0716-1787-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b07/2023 _dz _eb _zSI