Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Current Trends in Computational Modeling for Drug Discovery / / edited by Supratik Kar, Jerzy Leszczynski
| Current Trends in Computational Modeling for Drug Discovery / / edited by Supratik Kar, Jerzy Leszczynski |
| Autore | Kar Supratik |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (311 pages) |
| Disciplina |
541.2
615.19 |
| Altri autori (Persone) | LeszczynskiJerzy |
| Collana | Challenges and Advances in Computational Chemistry and Physics |
| Soggetto topico |
Drugs—Design
Molecules—Models Chemistry—Data processing Medicinal chemistry Pharmacology Structure-Based Drug Design Molecular Modelling Computational Chemistry Medicinal Chemistry |
| ISBN | 3-031-33871-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | SBDD and its challenges -- In silico discovery of class IIb HDAC inhibitors: The state of art -- Role of computational modelling in drug discovery for Alzheimer’s disease -- Computational Modeling in the Development of Antiviral Agents -- Targeted computational approaches to identify potential inhibitors for Nipah virus -- Role of Computational Modelling in Drug Discovery for HIV -- Recent insight of the emerging severe fever with thrombocytopenia syndrome virus: drug discovery, therapeutic options, and limitations -- Computational toxicological aspects in drug design and discovery, screening adverse effects -- Read-Across and RASAR tools from the DTC Laboratory -- Databases for Drug Discovery and Development. |
| Record Nr. | UNINA-9910734828903321 |
Kar Supratik
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning in Molecular Sciences / / edited by Chen Qu, Hanchao Liu
| Machine Learning in Molecular Sciences / / edited by Chen Qu, Hanchao Liu |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (323 pages) |
| Disciplina | 006.31 |
| Collana | Challenges and Advances in Computational Chemistry and Physics |
| Soggetto topico |
Machine learning
Artificial intelligence Molecules - Models Chemistry, Physical and theoretical Chemistry - Data processing Bioinformatics Machine Learning Artificial Intelligence Molecular Modelling Theoretical Chemistry Computational Chemistry Computational and Systems Biology |
| ISBN | 3-031-37196-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | An Introduction to Machine Learning in Molecular Sciences -- Graph Neural Networks for Molecules -- Voxelized representations of atomic systems for machine learning applications -- Development of exchange-correlation functionals assisted by machine learning -- Machine-Learning for Static and Dynamic Electronic Structure Theory -- Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-dimensional Accurate Potential Energy Surfaces (PESs) of Molecules and Reactions -- Machine Learning Applications in Chemical Kinetics and Thermochemistry -- Synthesize in A Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis -- Machine Learning for Protein Engineering. |
| Record Nr. | UNINA-9910746977103321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Philosophy of Astrophysics : Stars, Simulations, and the Struggle to Determine What is Out There / / edited by Nora Mills Boyd, Siska De Baerdemaeker, Kevin Heng, Vera Matarese
| Philosophy of Astrophysics : Stars, Simulations, and the Struggle to Determine What is Out There / / edited by Nora Mills Boyd, Siska De Baerdemaeker, Kevin Heng, Vera Matarese |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (XII, 332 p. 1 illus.) |
| Disciplina | 501 |
| Collana | Synthese Library, Studies in Epistemology, Logic, Methodology, and Philosophy of Science |
| Soggetto topico |
Science—Philosophy
Astronomy Molecules—Models Knowledge, Theory of Astronomy—Observations Philosophy of Science Astronomy, Cosmology and Space Sciences Molecular Modelling Epistemology Astronomy, Observations and Techniques |
| ISBN | 3-031-26618-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction (Vera Matarese, Siska De Baerdemaeker, and Nora Mills Boyd) -- Part I: Theory, Observation, and the Relation Between Them. 2. Laboratory Astrophysics: Lessons for Epistemology of Astrophysics (Nora Mills Boyd) -- 3. A Crack in the Track of the Hubble Constant (Marie Gueguen) -- 4. Theory Testing in Gravitational-Wave Astrophysics (Jamee Elder) -- 5. Hybrid Enrichment of Theory and Observation in Next-Generation Stellar Population Synthesis (Lydia Patton) -- 6. Doing More with Less: Dark Matter & Modified Gravity (Niels C. M. Martens and Martin King) -- Part II: Models and Simulations. 7. Stellar Structure Models Revisited: Evidence and Data in Asteroseismology (Mauricio Suárez) -- 8. Idealizations in Astrophysical Computer Simulations (Melissa Jacquart and Regy-Null R. Arcadia) -- 9. Simulation Verification in Practice (Kevin Kadowaki) -- 10. (What) Do We Learn from Code Comparisons? A Case Study of Self-Interacting Dark Matter Implementations (Helen Meskhidze) -- 11. Simulation and Experiment Revisited: Temporal Data in Astronomy and Astrophysics (Shannon Sylvie Abelson) -- 12. What’s In a Survey? Simulation-Induced Selection Effects in Astronomy (Sarah C. Gallagher and Christopher Smeenk) -- Part III: Black Holes. 13. On the Epistemology of Observational Black Hole Astrophysics (Juliusz Doboszewski and Dennis Lehmkuhl) -- 14. Black Holes and Analogy (Alex Mathie) -- 15. Extragalactic Reality Revisited: Astrophysics and Entity Realism (Simon Allzén) -- Part IV: Concluding Thoughts. 16. Reflections by a Theoretical Astrophysicist (Kevin Heng) -- 17. Annotated Bibliography (Cameron C. Yetman). |
| Record Nr. | UNINA-9910731412903321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
q-RASAR : A Path to Predictive Cheminformatics / / by Kunal Roy, Arkaprava Banerjee
| q-RASAR : A Path to Predictive Cheminformatics / / by Kunal Roy, Arkaprava Banerjee |
| Autore | Roy Kunal <1971-> |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (99 pages) |
| Disciplina | 542.85 |
| Collana | SpringerBriefs in Molecular Science |
| Soggetto topico |
Chemistry - Data processing
Quantum physics Computer simulation Molecules - Models Computational Chemistry Quantum Simulations Molecular Modelling |
| ISBN | 3-031-52057-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chemical Information and Molecular Similarity -- Read-across and Quantitative Structure-activity Relationships (QSAR) for Making Predictions and Data Gap-Filling -- Quantitative Read-Across (q-RA) and Quantitative Read-Across Structure-Activity Relationships (q-RASAR) – Genesis and Model Development -- Tools, Applications, and Case Studies (q-RA and q-RASAR) -- Future Prospects. |
| Record Nr. | UNINA-9910806198703321 |
|
Roy Kunal <1971->
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
