About this Research Topic
In the last few decades, computational approaches have become essential complements to experimental procedures in drug discovery and precision medicine. Computational approaches reduce the cost and improve the efficiency of the development of drugs and treatments for diseases. Moreover, these methods also contribute new ideas, directions and perspectives for drug design and increase our understanding of the underlining biological mechanisms.
Computational approaches are involved in almost all stages of drug development and precision medicine, from identifying potential drug targets to discovery of clinical biomarkers. In the drug target discovery stage, chemical structure similarity searching and ligand-based interaction fingerprint methods are used to look for ligands with similar chemical structures and predict probable target of a ligand. In addition, network pharmacology modeling is used to gain a system level understanding of how ligand-receptor interactions influence signal pathways and identify new drug targets. Molecular modeling plays a crucial role in the stage of compound screening and lead molecule optimization. Structure-based virtual screening (SBVS) methods, including homology modeling, in silico docking, and de novo drug design, utilize knowledges of target structures and ligand-receptor interactions. The ligand-based virtual screening methods involve pharmacophore modeling and quantitative structure-activity relationship (QSAR) and try to construct predictive models based on ligands with similar structures. In the preclinical and clinical stages, in silico ADMET prediction approaches such as quantitative structure-property relationship (QSPR) and physiologically based pharmacokinetic modeling (PBPK) are developed to assess the absorption, distribution, metabolism, elimination, and toxicity (ADMET) of lead compounds. Finally, at the drug polymorphism stage, molecular packing (MOLPAK) analysis and lattice energy minimization (LEM) method are often used to aid drug crystal prediction.
Development of high throughput techniques and the increasing automation of biological experiments led to the collection of massive amounts of data, which require mathematical and computational approaches to analyze and extract relevant knowledge. In this regard, data mining, including statistical methods, artificial intelligence, and machine learning, has been highly involved in drug discovery and precision medicine. For instance, analyses of proteomic and genomic data are helpful to look for new targets for drug development, such as proteins, miRNA, biomarkers, and pathways. In addition, computational pathology allows analyses of tissue images by machine learning methods and understanding of the heterogeneity of the patients. Among available data mining approaches, machine learning, in particular deep learning algorithms, are of great interest to the community due to their ability to tackle huge and complex data sets with high quality. However, the studies of these new techniques including data mining and network pharmacology must provide clear insights into the structure-activity relationships of pharmaceuticals.
Potential topics include, but are not limited to:
- Developments and applications of bioinformatics approaches and network pharmacology in prediction and validation of novel drug targets.
- Developments and applications of structure/ligand-based virtual screening methods in computer added drug design.
- Developments and applications of QSAR models in lead compound discovery and optimization.
- Applications of molecular simulations, PBPK and ADMET modeling in preclinical studies
- Recent advances in drug deposition and the design of nanoscale drug carriers
- Computational approaches to predict or design unknown crystal structure and new polymorphs of drugs
- Developments and applications of data mining approaches in all stages of drug discovery and precision medicine
We welcome research and review articles that will create a multidisciplinary forum of discussion on recent advances in these computational methods and their applications to drug discovery, precision medicine, and the understanding of the underlining mechanisms of biological processes involved in drug development.
Computational approaches are involved in almost all stages of drug development and precision medicine, from identifying potential drug targets to discovery of clinical biomarkers. In the drug target discovery stage, chemical structure similarity searching and ligand-based interaction fingerprint methods are used to look for ligands with similar chemical structures and predict probable target of a ligand. In addition, network pharmacology modeling is used to gain a system level understanding of how ligand-receptor interactions influence signal pathways and identify new drug targets. Molecular modeling plays a crucial role in the stage of compound screening and lead molecule optimization. Structure-based virtual screening (SBVS) methods, including homology modeling, in silico docking, and de novo drug design, utilize knowledges of target structures and ligand-receptor interactions. The ligand-based virtual screening methods involve pharmacophore modeling and quantitative structure-activity relationship (QSAR) and try to construct predictive models based on ligands with similar structures. In the preclinical and clinical stages, in silico ADMET prediction approaches such as quantitative structure-property relationship (QSPR) and physiologically based pharmacokinetic modeling (PBPK) are developed to assess the absorption, distribution, metabolism, elimination, and toxicity (ADMET) of lead compounds. Finally, at the drug polymorphism stage, molecular packing (MOLPAK) analysis and lattice energy minimization (LEM) method are often used to aid drug crystal prediction.
Development of high throughput techniques and the increasing automation of biological experiments led to the collection of massive amounts of data, which require mathematical and computational approaches to analyze and extract relevant knowledge. In this regard, data mining, including statistical methods, artificial intelligence, and machine learning, has been highly involved in drug discovery and precision medicine. For instance, analyses of proteomic and genomic data are helpful to look for new targets for drug development, such as proteins, miRNA, biomarkers, and pathways. In addition, computational pathology allows analyses of tissue images by machine learning methods and understanding of the heterogeneity of the patients. Among available data mining approaches, machine learning, in particular deep learning algorithms, are of great interest to the community due to their ability to tackle huge and complex data sets with high quality. However, the studies of these new techniques including data mining and network pharmacology must provide clear insights into the structure-activity relationships of pharmaceuticals.
Potential topics include, but are not limited to:
- Developments and applications of bioinformatics approaches and network pharmacology in prediction and validation of novel drug targets.
- Developments and applications of structure/ligand-based virtual screening methods in computer added drug design.
- Developments and applications of QSAR models in lead compound discovery and optimization.
- Applications of molecular simulations, PBPK and ADMET modeling in preclinical studies
- Recent advances in drug deposition and the design of nanoscale drug carriers
- Computational approaches to predict or design unknown crystal structure and new polymorphs of drugs
- Developments and applications of data mining approaches in all stages of drug discovery and precision medicine
We welcome research and review articles that will create a multidisciplinary forum of discussion on recent advances in these computational methods and their applications to drug discovery, precision medicine, and the understanding of the underlining mechanisms of biological processes involved in drug development.
Keywords: Drug Discovery, Precision Medicine, Computational Biochemistry, Molecular Modeling, Molecular Dynamics, Systematic Biology, Data Mining, Network Pharmacology, in silico Drug Design, Computational Methods in Medicinal Chemistry
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