The phenomenal growth of global pharmaceutical sales and the quest for innovation are driving an unprecedented search for human test subjects, particularly in middle- and low-income countries. Our ...hope for medical progress increasingly depends on the willingness of the world's poor to participate in clinical drug trials. While these experiments often provide those in need with vital and previously unattainable medical resources, the outsourcing and offshoring of trials also create new problems. In this groundbreaking book, anthropologist Adriana Petryna takes us deep into the clinical trials industry as it brings together players separated by vast economic and cultural differences. Moving between corporate and scientific offices in the United States and research and public health sites in Poland and Brazil,When Experiments Traveldocuments the complex ways that commercial medical science, with all its benefits and risks, is being integrated into local health systems and emerging drug markets.
Providing a unique perspective on globalized clinical trials,When Experiments Travelraises central questions: Are such trials exploitative or are they social goods? How are experiments controlled and how is drug safety ensured? And do these experiments help or harm public health in the countries where they are conducted? Empirically rich and theoretically innovative, the book shows that neither the language of coercion nor that of rational choice fully captures the range of situations and value systems at work in medical experiments today.When Experiments Travelchallenges conventional understandings of the ethics and politics of transnational science and changes the way we think about global medicine and the new infrastructures of our lives.
Beware of docking Chen, Yu-Chian
Trends in pharmacological sciences (Regular ed.),
02/2015, Letnik:
36, Številka:
2
Journal Article
Recenzirano
Highlights • Docking is a fast and low-cost technology for large-scale drug screening. • Molecular dynamics simulations sometimes reveal docking poses that are actually unstable. • Drug design should ...consider both binding affinity and residence time. • A molecular dynamics simulation is currently not long enough to accurately model residence time.
Over the last few decades, computer-aided drug design has emerged as a powerful technique playing a crucial role in the development of new drug molecules. Structure-based drug design and ligand-based ...drug design are two methods commonly used in computer-aided drug design. In this article, we discuss the theory behind both methods, as well as their successful applications and limitations. To accomplish this, we reviewed structure based and ligand based virtual screening processes. Molecular dynamics simulation, which has become one of the most influential tool for prediction of the conformation of small molecules and changes in their conformation within the biological target, has also been taken into account. Finally, we discuss the principles and concepts of molecular docking, pharmacophores and other methods used in computer-aided drug design.
Cryo‐electron microscopy (cryo‐EM) is gaining large attention for high‐resolution protein structure determination in solutions. However, a very high percentage of cryo‐EM structures correspond to ...resolutions of 3–5 Å, making the structures difficult to be used in in silico drug design. In this study, we analyze how useful cryo‐EM protein structures are for in silico drug design by evaluating ligand docking accuracy. From realistic cross‐docking scenarios using medium resolution (3–5 Å) cryo‐EM structures and a popular docking tool Autodock‐Vina, only 20% of docking succeeded, when the success rate doubles in the same kind of cross‐docking but using high‐resolution (<2 Å) crystal structures instead. We decipher the reason for failures by decomposing the contribution from resolution‐dependent and independent factors. The heterogeneity in the protein side‐chain and backbone conformations is identified as the major resolution‐dependent factor causing docking difficulty from our analysis, while intrinsic receptor flexibility mainly comprises the resolution‐independent factor. We demonstrate the flexibility implementation in current ligand docking tools is able to rescue only a portion of failures (10%), and the limited performance was majorly due to potential structural errors than conformational changes. Our work suggests the strong necessity of more robust method developments on ligand docking and EM modeling techniques in order to fully utilize cryo‐EM structures for in silico drug design.
Benchmarking applicability of medium‐resolution cryo‐EM protein structures for structure‐based drug design. The figure illustrates that medium resolution cryo‐EM structures have fewer feasible cross‐docking scenarios than high resolution X‐ray crystallography.
AlphaFold2 is a promising new tool for researchers to predict protein structures and generate high‐quality models, with low backbone and global root‐mean‐square deviation (RMSD) when compared with ...experimental structures. However, it is unclear if the structures predicted by AlphaFold2 will be valuable targets of docking. To address this question, we redocked ligands in the PDBbind datasets against the experimental co‐crystallized receptor structures and against the AlphaFold2 structures using AutoDock‐GPU. We find that the quality measure provided during structure prediction is not a good predictor of docking performance, despite accurately reflecting the quality of the alpha carbon alignment with experimental structures. Removing low‐confidence regions of the predicted structure and making side chains flexible improves the docking outcomes. Overall, despite high‐quality prediction of backbone conformation, fine structural details limit the naive application of AlphaFold2 models as docking targets.
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost ...impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind.
Graphic abstract
The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
Key Topics in Molecular Docking for Drug Design Torres, Pedro H M; Sodero, Ana C R; Jofily, Paula ...
International journal of molecular sciences,
09/2019, Letnik:
20, Številka:
18
Journal Article
Recenzirano
Odprti dostop
Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to ...consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.
Designing new medicines more cheaply and quickly is tightly linked to the quest of exploring chemical space more widely and efficiently. Chemical space is monumentally large, but recent advances in ...computer software and hardware have enabled researchers to navigate virtual chemical spaces containing billions of chemical structures. This review specifically concerns collections of many millions or even billions of enumerated chemical structures as well as even larger chemical spaces that are not fully enumerated. We present examples of chemical libraries and spaces and the means used to construct them, and we discuss new technologies for searching huge libraries and for searching combinatorially in chemical space. We also cover space navigation techniques and consider new approaches to
drug design and the impact of the "autonomous laboratory" on synthesis of designed compounds. Finally, we summarize some other challenges and opportunities for the future.
Generative molecular design for drug discovery and development has seen a recent resurgence promising to improve the efficiency of the design-make-test-analyse cycle; by computationally exploring ...much larger chemical spaces than traditional virtual screening techniques. However, most generative models thus far have only utilized small-molecule information to train and condition de novo molecule generators. Here, we instead focus on recent approaches that incorporate protein structure into de novo molecule optimization in an attempt to maximize the predicted on-target binding affinity of generated molecules. We summarize these structure integration principles into either distribution learning or goal-directed optimization and for each case whether the approach is protein structure-explicit or implicit with respect to the generative model. We discuss recent approaches in the context of this categorization and provide our perspective on the future direction of the field.
This reprint is a collection of 31 original papers and four reviews, published from 2021 to 2022, focused on the application of a wide range of computational tools in medicinal chemistry projects: ...from molecular docking to artificial intelligence approaches. Applications of in silico tools are crucial in the early stages of drug design, such as planning more efficient and economic synthetic routes for chemical administration, screening of huge databases, as well as proposing hypotheses for probable mechanisms of action of drugs in macromolecular targets. Such endeavors are extremely complex and require the usage of modern and sophisticated approaches, such as artificial intelligence, data mining, computational molecular simulations through classical mechanics and quantum mechanics, molecular docking, chemoinformatics, applied mathematics, and biostatistics.
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