[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.

Interactome networks and human disease

In recent years, tools for the development of new drugs have been dramatically improved. These include genomic and proteomic research, numerous biophysical methods, combinatorial chemistry and screening technologies. In addition, early ADMET studies are employed in order to significantly reduce the failure rate in the development of drug candidates. As a consequence, the lead finding, lead optimization and development process has gained marked enhancement in speed and efficiency. In parallel to this development, major pharma companies are increasingly outsourcing many components of drug discovery research to biotech companies. All these measures are designed to address the need for a faster time to market. New screening methodologies have contributed significantly to the efficiency of the drug discovery process. The conventional screening of single compounds or compound libraries has been dramatically accelerated by high throughput screening methods. In addition, in silico screening methods allow the evaluation of virtual compounds. A wide range of new lead finding and lead optimization opportunities result from novel screening methods by NMR, which are the topic of this review article.

Drug Discovery Today

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

The significance of artificial intelligence in drug delivery system design.

Measuring the structural diversity of compound databases is relevant in drug discovery and many other areas of chemistry. Since molecular diversity depends on molecular representation, comprehensive chemoinformatic analysis of the diversity of libraries uses multiple criteria. For instance, the diversity of the molecular libraries is typically evaluated employing molecular scaffolds, structural fingerprints, and physicochemical properties. However, the assessment with each criterion is analyzed independently and it is not straightforward to provide an evaluation of the “global diversity”. Herein the Consensus Diversity Plot (CDP) is proposed as a novel method to represent in low dimensions the diversity of chemical libraries considering simultaneously multiple molecular representations. We illustrate the application of CDPs to classify eight compound data sets and two subsets with different sizes and compositions using molecular scaffolds, structural fingerprints, and physicochemical properties. CDPs are general data mining tools that represent in two-dimensions the global diversity of compound data sets using multiple metrics. These plots can be constructed using single or combined measures of diversity. An online version of the CDPs is freely available at: https://consensusdiversityplots-difacquim-unam.shinyapps.io/RscriptsCDPlots/
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https://link.springer.com/content/pdf/10.1186/s13321-016-0176-9.pdf

Consensus Diversity Plots: a global diversity analysis of chemical libraries.

Computer-aided drug design (CADD) comprises a broad range of theoretical and computational approaches that are part of modern drug discovery. CADD methods have made key contributions to the development of drugs that are in clinical use or in clinical trials. Such methods have emerged and evolved along with experimental approaches used in drug design. In this chapter we discuss the major CADD methods and examples of recent applications to drugs that have advanced in clinical trials or that have been approved for clinical use. We also comment on representative trends in current drug discovery that are shaping the development of novel methods, such as computer-aided drug repurposing. Similarly we present emerging concepts and technologies in molecular modeling and chemoinformatics. Furthermore, this chapter discusses the authors’ point of view of the challenges of traditional and novel CADD methods to increase their positive impact in drug discovery.

Computational Drug Design Methods—Current and Future Perspectives

Automated molecular docking aims at predicting the possible interactions between two molecules. This method has proven useful in medicinal chemistry and drug discovery providing atomistic insights into molecular recognition. Over the last 20 years methods for molecular docking have been improved, yielding accurate results on pose prediction. Nonetheless, several aspects of molecular docking need revision due to changes in the paradigm of drug discovery. In the present article, we review the principles, techniques, and algorithms for docking with emphasis on protein-ligand docking for drug discovery. We also discuss current approaches to address major challenges of docking.

/pdf/acoplamiento-molecular-avances-recientes-y-retos-1uh1ulq4su.pdf

Acoplamiento Molecular: Avances Recientes y Retos

Aim: Fungi are valuable resources for bioactive secondary metabolites. However, the chemical space of fungal secondary metabolites has been studied only on a limited basis. Herein, we report a comprehensive chemoinformatic analysis of a unique set of 207 fungal metabolites isolated and characterized in a USA National Cancer Institute funded drug discovery project. Results: Comparison of the molecular complexity of the 207 fungal metabolites with approved anticancer and nonanticancer drugs, compounds in clinical studies, general screening compounds and molecules Generally Recognized as Safe revealed that fungal metabolites have high degree of complexity. Molecular fingerprints showed that fungal metabolites are as structurally diverse as other natural products and have, in general, drug-like physicochemical properties. Conclusion: Fungal products represent promising candidates to expand the medicinally relevant chemical space. This work is a significant expansion of an analysis reported years ago for a sma...

/pdf/chemoinformatic-expedition-of-the-chemical-space-of-fungal-280mb3p6fs.pdf

Chemoinformatic Expedition of the Chemical Space of Fungal Products

This review discusses the concept of polypharmacology in drug discovery and development. The relationship between polyphar-macology and polypharmacy, drug repurposing, combination of drugs and in vivo testing are discussed. Modern applications of polypharma-cology and polypharmacy in epigenetic and antiviral drug development are described as examples. A survey of modern methodologies to design and develop multiple-target ligands is presented with a special focus on computational-based methods. These approaches include, but are not limited to, target fishing, proteochemometric modeling, data mining of side effects of drugs, and computer-aided drug repurposing.

/pdf/one-drug-for-multiple-targets-a-computational-perspective-1efagzitou.pdf

Fernando D. Prieto-Martínez

Papers

Consensus Diversity Plots: a global diversity analysis of chemical libraries.

Computational Drug Design Methods—Current and Future Perspectives

Acoplamiento Molecular: Avances Recientes y Retos

Chemoinformatic Expedition of the Chemical Space of Fungal Products

One Drug for Multiple Targets: A Computational Perspective