Computational Approaches in Drug Discovery and Design
In this Special Issue, we have collected some research works on drug design using computational methods with respect to drug–target interactions, covering three topics, i.e., the developments of methods including machine learning and transformer-based models, the conformational effects in the target...
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| Sprog: | engelsk |
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MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Online adgang: | ONIX_20260416T142754_9783725867028_31 |
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| description | In this Special Issue, we have collected some research works on drug design using computational methods with respect to drug–target interactions, covering three topics, i.e., the developments of methods including machine learning and transformer-based models, the conformational effects in the target site based on molecular dynamics simulations, and the rational design of new inhibitors via virtual screening and experimental validation. Xiaojuan Shen et al. proposed a molecular generative model, NIMO, based on a conditional transformer neural network model. Xiangying Zhang et al. developed METEOR, a graph-based generative model within a reinforcement learning framework. As discussed in the review by Xin Qi et al., many computational tools utilizing artificial intelligence (AI) algorithms have been developed in recent years, such as DeepHome2.0, AFTGAN, and PeSTo for protein–protein interactions; DeepDTAF, DeepAffinity, DeepMGT-DT, MolTranse, DTITR, GSATDTA, and VITScore for drug–target interactions; MolGPT, LigGPT, AlphaDrug, and cMolGPT for de novo drug design; and SMILES-BERT, ChemBERTa, K-BERT, DHTNN, MolFPG, and GROVER for predicting molecular properties. Several integrated approaches have been built from some of the typical computational tools, including CHARMM, ROSETTA3, VMD, GROMACS, Marvin Sketch, Chimera, Avogadro, SPORES, DataWarrior, Schrödinger, MOE, PLANTS, Pharmit, Swiss-ADME, Swiss Target Prediction, and MouseTox. These approaches were applied to systems such as cytochrome P450, SARS-CoV-2, Gram-positive bacteria, PPARγ partial agonists, and a set of protein–ligand complexes collected in PDBbind. |
| format | Online |
| id | doab-20.500.12854ir-175376 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1753762026-04-16T20:32:47Z Computational Approaches in Drug Discovery and Design Zhong, Shijun Computer-Aided Drug Design Molecular Generative Model Inhibitor Identification Virtual Screening Binding Free Energy Calculation Molecular Dynamics Simulation Docking Scoring Function Machine Learning Transformer-Based Model Experimental Validation thema EDItEUR::M Medicine and Nursing thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KND Manufacturing industries In this Special Issue, we have collected some research works on drug design using computational methods with respect to drug–target interactions, covering three topics, i.e., the developments of methods including machine learning and transformer-based models, the conformational effects in the target site based on molecular dynamics simulations, and the rational design of new inhibitors via virtual screening and experimental validation. Xiaojuan Shen et al. proposed a molecular generative model, NIMO, based on a conditional transformer neural network model. Xiangying Zhang et al. developed METEOR, a graph-based generative model within a reinforcement learning framework. As discussed in the review by Xin Qi et al., many computational tools utilizing artificial intelligence (AI) algorithms have been developed in recent years, such as DeepHome2.0, AFTGAN, and PeSTo for protein–protein interactions; DeepDTAF, DeepAffinity, DeepMGT-DT, MolTranse, DTITR, GSATDTA, and VITScore for drug–target interactions; MolGPT, LigGPT, AlphaDrug, and cMolGPT for de novo drug design; and SMILES-BERT, ChemBERTa, K-BERT, DHTNN, MolFPG, and GROVER for predicting molecular properties. Several integrated approaches have been built from some of the typical computational tools, including CHARMM, ROSETTA3, VMD, GROMACS, Marvin Sketch, Chimera, Avogadro, SPORES, DataWarrior, Schrödinger, MOE, PLANTS, Pharmit, Swiss-ADME, Swiss Target Prediction, and MouseTox. These approaches were applied to systems such as cytochrome P450, SARS-CoV-2, Gram-positive bacteria, PPARγ partial agonists, and a set of protein–ligand complexes collected in PDBbind. 2026-04-16T20:32:41Z 2026-04-16T20:32:41Z 2026 book ONIX_20260416T142754_9783725867028_31 9783725867028 9783725867035 https://directory.doabooks.org/handle/20.500.12854/175376 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/book/12291 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-6703-5 10.3390/books978-3-7258-6703-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725867028 9783725867035 144 CH open access |
| spellingShingle | Computer-Aided Drug Design Molecular Generative Model Inhibitor Identification Virtual Screening Binding Free Energy Calculation Molecular Dynamics Simulation Docking Scoring Function Machine Learning Transformer-Based Model Experimental Validation thema EDItEUR::M Medicine and Nursing thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KND Manufacturing industries Computational Approaches in Drug Discovery and Design |
| title | Computational Approaches in Drug Discovery and Design |
| title_full | Computational Approaches in Drug Discovery and Design |
| title_fullStr | Computational Approaches in Drug Discovery and Design |
| title_full_unstemmed | Computational Approaches in Drug Discovery and Design |
| title_short | Computational Approaches in Drug Discovery and Design |
| title_sort | computational approaches in drug discovery and design |
| topic | Computer-Aided Drug Design Molecular Generative Model Inhibitor Identification Virtual Screening Binding Free Energy Calculation Molecular Dynamics Simulation Docking Scoring Function Machine Learning Transformer-Based Model Experimental Validation thema EDItEUR::M Medicine and Nursing thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KND Manufacturing industries |
| topic_facet | Computer-Aided Drug Design Molecular Generative Model Inhibitor Identification Virtual Screening Binding Free Energy Calculation Molecular Dynamics Simulation Docking Scoring Function Machine Learning Transformer-Based Model Experimental Validation thema EDItEUR::M Medicine and Nursing thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KND Manufacturing industries |
| url | ONIX_20260416T142754_9783725867028_31 |